Editorial

4 pages, 167 KiB

Open AccessEditorial

Preface to the Special Issue on “Recent Advances in Swarm Intelligence Algorithms and Their Applications”—Special Issue Book

by Jian Dong

Mathematics 2023, 11(12), 2624; https://doi.org/10.3390/math11122624 - 08 Jun 2023

Viewed by 681

Abstract

Swarm intelligence algorithms represent a rapidly growing research domain and have recently attracted a great deal of attention [...] Full article

(This article belongs to the Special Issue Recent Advances in Swarm Intelligence Algorithms and Their Applications)

Research

Jump to: Editorial, Review

21 pages, 1076 KiB

Open AccessArticle

A Novel Discrete Differential Evolution with Varying Variables for the Deficiency Number of Mahjong Hand

by Xueqing Yan and Yongming Li

Mathematics 2023, 11(9), 2135; https://doi.org/10.3390/math11092135 - 02 May 2023

Cited by 2 | Viewed by 1444

Abstract

The deficiency number of one hand, i.e., the number of tiles needed to change in order to win, is an important factor in the game Mahjong, and plays a significant role in the development of artificial intelligence (AI) for Mahjong. However, it is [...] Read more.

The deficiency number of one hand, i.e., the number of tiles needed to change in order to win, is an important factor in the game Mahjong, and plays a significant role in the development of artificial intelligence (AI) for Mahjong. However, it is often difficult to compute due to the large amount of possible combinations of tiles. In this paper, a novel discrete differential evolution (DE) algorithm is presented to calculate the deficiency number of the tiles. In detail, to decrease the difficulty of computing the deficiency number, some pretreatment mechanisms are first put forward to convert it into a simple combinatorial optimization problem with varying variables by changing its search space. Subsequently, by means of the superior framework of DE, a novel discrete DE algorithm is specially developed for the simplified problem through devising proper initialization, a mapping solution method, a repairing solution technique, a fitness evaluation approach, and mutation and crossover operations. Finally, several experiments are designed and conducted to evaluate the performance of the proposed algorithm by comparing it with the tree search algorithm and three other kinds of metaheuristic methods on a large number of various test cases. Experimental results indicate that the proposed algorithm is efficient and promising. Full article

(This article belongs to the Special Issue Recent Advances in Swarm Intelligence Algorithms and Their Applications)

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21 pages, 406 KiB

Open AccessArticle

CPPE: An Improved Phasmatodea Population Evolution Algorithm with Chaotic Maps

by Tsu-Yang Wu, Haonan Li and Shu-Chuan Chu

Mathematics 2023, 11(9), 1977; https://doi.org/10.3390/math11091977 - 22 Apr 2023

Cited by 31 | Viewed by 1307

Abstract

The Phasmatodea Population Evolution (PPE) algorithm, inspired by the evolution of the phasmatodea population, is a recently proposed meta-heuristic algorithm that has been applied to solve problems in engineering. Chaos theory has been increasingly applied to enhance the performance and convergence of meta-heuristic [...] Read more.

The Phasmatodea Population Evolution (PPE) algorithm, inspired by the evolution of the phasmatodea population, is a recently proposed meta-heuristic algorithm that has been applied to solve problems in engineering. Chaos theory has been increasingly applied to enhance the performance and convergence of meta-heuristic algorithms. In this paper, we introduce chaotic mapping into the PPE algorithm to propose a new algorithm, the Chaotic-based Phasmatodea Population Evolution (CPPE) algorithm. The chaotic map replaces the initialization population of the original PPE algorithm to enhance performance and convergence. We evaluate the effectiveness of the CPPE algorithm by testing it on 28 benchmark functions, using 12 different chaotic maps. The results demonstrate that CPPE outperforms PPE in terms of both performance and convergence speed. In the performance analysis, we found that the CPPE algorithm with the Tent map showed improvements of 8.9647%, 10.4633%, and 14.6716%, respectively, in the Final, Mean, and Standard metrics, compared to the original PPE algorithm. In terms of convergence, the CPPE algorithm with the Singer map showed an improvement of 65.1776% in the average change rate of fitness value, compared to the original PPE algorithm. Finally, we applied our CPPE to stock prediction. The results showed that the predicted curve was relatively consistent with the real curve. Full article

(This article belongs to the Special Issue Recent Advances in Swarm Intelligence Algorithms and Their Applications)

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28 pages, 14151 KiB

Open AccessArticle

Further Optimization of Maxwell-Type Dynamic Vibration Absorber with Inerter and Negative Stiffness Spring Using Particle Swarm Algorithm

by Yuying Chen, Jing Li, Shaotao Zhu and Hongzhen Zhao

Mathematics 2023, 11(8), 1904; https://doi.org/10.3390/math11081904 - 17 Apr 2023

Cited by 5 | Viewed by 1384

Abstract

Dynamic vibration absorbers (DVAs) are widely used in engineering practice because of their good vibration control performance. Structural design or parameter optimization could improve its control efficiency. In this paper, the viscoelastic Maxwell-type DVA model with an inerter and multiple stiffness springs is [...] Read more.

Dynamic vibration absorbers (DVAs) are widely used in engineering practice because of their good vibration control performance. Structural design or parameter optimization could improve its control efficiency. In this paper, the viscoelastic Maxwell-type DVA model with an inerter and multiple stiffness springs is investigated with the combination of the traditional theory and an intelligent algorithm. Firstly, the expressions and approximate optimal values of the system parameters are obtained using the fixed-point theory to deal with the

H_{\infty}

optimization problem, which can provide help with the range of parameters in the algorithm. Secondly, we innovatively introduce the particle swarm optimization (PSO) algorithm to prove that the algorithm could adjust the value of the approximate solution to minimize the maximum amplitude by analyzing and optimizing the single variable and four variables. Furthermore, the validity of the parameters is further verified by simulation between the numerical solution and the analytical solution using the fourth-order Runge–Kutta method. Finally, the DVA demonstrated in this paper is compared with typical DVAs under random excitation. The timing sequence and variances, as well as the decreased ratios of the displacements, show that the presented DVA has a more satisfactory control performance. The inerter and negative stiffness spring can indeed bring beneficial effects to the vibration absorber. Remarkably, the intelligent algorithm can make the resonance peaks equal in the parameter optimization of the vibration absorber, which is quite difficult to achieve with theoretical methods at present. The results may provide a theoretical and computational basis for the optimization design of DVA. Full article

(This article belongs to the Special Issue Recent Advances in Swarm Intelligence Algorithms and Their Applications)

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25 pages, 1213 KiB

Open AccessArticle

Improved Multi-Strategy Harris Hawks Optimization and Its Application in Engineering Problems

by Fulin Tian, Jiayang Wang and Fei Chu

Mathematics 2023, 11(6), 1525; https://doi.org/10.3390/math11061525 - 21 Mar 2023

Cited by 2 | Viewed by 1730

Abstract

In order to compensate for the low convergence accuracy, slow rate of convergence, and easily falling into the trap of local optima for the original Harris hawks optimization (HHO) algorithm, an improved multi-strategy Harris hawks optimization (MSHHO) algorithm is proposed. First, the population [...] Read more.

In order to compensate for the low convergence accuracy, slow rate of convergence, and easily falling into the trap of local optima for the original Harris hawks optimization (HHO) algorithm, an improved multi-strategy Harris hawks optimization (MSHHO) algorithm is proposed. First, the population is initialized by Sobol sequences to increase the diversity of the population. Second, the elite opposition-based learning strategy is incorporated to improve the versatility and quality of the solution sets. Furthermore, the energy updating strategy of the original algorithm is optimized to enhance the exploration and exploitation capability of the algorithm in a nonlinear update manner. Finally, the Gaussian walk learning strategy is introduced to avoid the algorithm being trapped in a stagnant state and slipping into a local optimum. We perform experiments on 33 benchmark functions and 2 engineering application problems to verify the performance of the proposed algorithm. The experimental results show that the improved algorithm has good performance in terms of optimization seeking accuracy, the speed of convergence, and stability, which effectively remedies the defects of the original algorithm. Full article

(This article belongs to the Special Issue Recent Advances in Swarm Intelligence Algorithms and Their Applications)

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24 pages, 13534 KiB

Open AccessArticle

A Compact and High-Performance Acoustic Echo Canceller Neural Processor Using Grey Wolf Optimizer along with Least Mean Square Algorithms

by Eduardo Pichardo, Esteban Anides, Angel Vazquez, Luis Garcia, Juan G. Avalos, Giovanny Sánchez, Héctor M. Pérez and Juan C. Sánchez

Mathematics 2023, 11(6), 1421; https://doi.org/10.3390/math11061421 - 15 Mar 2023

Cited by 2 | Viewed by 1263

Abstract

Recently, the use of acoustic echo canceller (AEC) systems in portable devices has significantly increased. Therefore, the need for superior audio quality in resource-constrained devices opens new horizons in the creation of high-convergence speed adaptive algorithms and optimal digital designs. Nowadays, AEC systems [...] Read more.

Recently, the use of acoustic echo canceller (AEC) systems in portable devices has significantly increased. Therefore, the need for superior audio quality in resource-constrained devices opens new horizons in the creation of high-convergence speed adaptive algorithms and optimal digital designs. Nowadays, AEC systems mainly use the least mean square (LMS) algorithm, since its implementation in digital hardware architectures demands low area consumption. However, its performance in acoustic echo cancellation is limited. In addition, this algorithm presents local convergence optimization problems. Recently, new approaches, based on stochastic optimization algorithms, have emerged to increase the probability of encountering the global minimum. However, the simulation of these algorithms requires high-performance computational systems. As a consequence, these algorithms have only been conceived as theoretical approaches. Therefore, the creation of a low-complexity algorithm potentially allows the development of compact AEC hardware architectures. In this paper, we propose a new convex combination, based on grey wolf optimization and LMS algorithms, to save area and achieve high convergence speed by exploiting to the maximum the best features of each algorithm. In addition, the proposed convex combination algorithm shows superior tracking capabilities when compared with existing approaches. Furthermore, we present a new neuromorphic hardware architecture to simulate the proposed convex combination. Specifically, we present a customized time-multiplexing control scheme to dynamically vary the number of search agents. To demonstrate the high computational capabilities of this architecture, we performed exhaustive testing. In this way, we proved that it can be used in real-world acoustic echo cancellation scenarios. Full article

(This article belongs to the Special Issue Recent Advances in Swarm Intelligence Algorithms and Their Applications)

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28 pages, 3699 KiB

Open AccessArticle

MFO-SFR: An Enhanced Moth-Flame Optimization Algorithm Using an Effective Stagnation Finding and Replacing Strategy

by Mohammad H. Nadimi-Shahraki, Hoda Zamani, Ali Fatahi and Seyedali Mirjalili

Mathematics 2023, 11(4), 862; https://doi.org/10.3390/math11040862 - 08 Feb 2023

Cited by 26 | Viewed by 2441

Abstract

Moth-flame optimization (MFO) is a prominent problem solver with a simple structure that is widely used to solve different optimization problems. However, MFO and its variants inherently suffer from poor population diversity, leading to premature convergence to local optima and losses in the [...] Read more.

Moth-flame optimization (MFO) is a prominent problem solver with a simple structure that is widely used to solve different optimization problems. However, MFO and its variants inherently suffer from poor population diversity, leading to premature convergence to local optima and losses in the quality of its solutions. To overcome these limitations, an enhanced moth-flame optimization algorithm named MFO-SFR was developed to solve global optimization problems. The MFO-SFR algorithm introduces an effective stagnation finding and replacing (SFR) strategy to effectively maintain population diversity throughout the optimization process. The SFR strategy can find stagnant solutions using a distance-based technique and replaces them with a selected solution from the archive constructed from the previous solutions. The effectiveness of the proposed MFO-SFR algorithm was extensively assessed in 30 and 50 dimensions using the CEC 2018 benchmark functions, which simulated unimodal, multimodal, hybrid, and composition problems. Then, the obtained results were compared with two sets of competitors. In the first comparative set, the MFO algorithm and its well-known variants, specifically LMFO, WCMFO, CMFO, ODSFMFO, SMFO, and WMFO, were considered. Five state-of-the-art metaheuristic algorithms, including PSO, KH, GWO, CSA, and HOA, were considered in the second comparative set. The results were then statistically analyzed through the Friedman test. Ultimately, the capacity of the proposed algorithm to solve mechanical engineering problems was evaluated with two problems from the latest CEC 2020 test-suite. The experimental results and statistical analysis confirmed that the proposed MFO-SFR algorithm was superior to the MFO variants and state-of-the-art metaheuristic algorithms for solving complex global optimization problems, with 91.38% effectiveness. Full article

(This article belongs to the Special Issue Recent Advances in Swarm Intelligence Algorithms and Their Applications)

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30 pages, 4858 KiB

Open AccessArticle

A Partition-Based Random Search Method for Multimodal Optimization

by Ziwei Lin, Andrea Matta, Sichang Du and Evren Sahin

Mathematics 2023, 11(1), 17; https://doi.org/10.3390/math11010017 - 21 Dec 2022

Cited by 2 | Viewed by 1122

Abstract

Practical optimization problems are often too complex to be formulated exactly. Knowing multiple good alternatives can help decision-makers easily switch solutions when needed, such as when faced with unforeseen constraints. A multimodal optimization task aims to find multiple global optima as well as [...] Read more.

Practical optimization problems are often too complex to be formulated exactly. Knowing multiple good alternatives can help decision-makers easily switch solutions when needed, such as when faced with unforeseen constraints. A multimodal optimization task aims to find multiple global optima as well as high-quality local optima of an optimization problem. Evolutionary algorithms with niching techniques are commonly used for such problems, where a rough estimate of the optima number is required to determine the population size. In this paper, a partition-based random search method is proposed, in which the entire feasible domain is partitioned into smaller and smaller subregions iteratively. Promising regions are partitioned faster than unpromising regions, thus, promising areas will be exploited earlier than unpromising areas. All promising areas are exploited in parallel, which allows multiple good solutions to be found in a single run. The proposed method does not require prior knowledge about the optima number and it is not sensitive to the distance parameter. By cooperating with local search to refine the obtained solutions, the proposed method demonstrates good performance in many benchmark functions with multiple global optima. In addition, in problems with numerous local optima, high-quality local optima are captured earlier than low-quality local optima. Full article

(This article belongs to the Special Issue Recent Advances in Swarm Intelligence Algorithms and Their Applications)

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22 pages, 895 KiB

Open AccessArticle

Throughput Optimization for NOMA Cognitive Relay Network with RF Energy Harvesting Based on Improved Bat Algorithm

by Yi Luo, Chenyang Wu, Yi Leng, Nüshan Huang, Lingxi Mao and Junhao Tang

Mathematics 2022, 10(22), 4357; https://doi.org/10.3390/math10224357 - 19 Nov 2022

Cited by 3 | Viewed by 1001

Abstract

Due to the shortcomings of the standard bat algorithm (BA) for multi-parameter optimization, an improved bat algorithm is proposed. The benchmark function test shows that the proposed algorithm has better realization of high-dimensional function optimization by introducing multiple flight modes, adopting adaptive strategy [...] Read more.

Due to the shortcomings of the standard bat algorithm (BA) for multi-parameter optimization, an improved bat algorithm is proposed. The benchmark function test shows that the proposed algorithm has better realization of high-dimensional function optimization by introducing multiple flight modes, adopting adaptive strategy based on group trend, and employing loudness mutation flight selection strategy based on Brownian motion. Aiming at the characteristics of complex networks structure and multiple design variables of energy harvesting non-orthogonal multiple access cognitive relay networks (EH-NOMA-CRNs), we utilize the proposed hybrid strategy improved bat algorithm (HSIBA) to optimize the performance of EH-NOMA-CRNs. At first, we construct a novel two-hop underlay power beacon assisted EH-NOMA-CRN, and derive the closed-form expressions of secondary network’s outage probability and throughput. Then, the secondary network performance optimization is formulated as the throughput maximation problem with regard to EH ratio and power allocation factors. Subsequently, the HSIBA is employed to optimize the above parameters. Numerical results show that the proposed HSIBA can achieve optimization to the constructed EH-NOMA-CRN with faster convergence speed and higher stability. Full article

(This article belongs to the Special Issue Recent Advances in Swarm Intelligence Algorithms and Their Applications)

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18 pages, 5246 KiB

Open AccessArticle

A Multi-Population Mean-Field Game Approach for Large-Scale Agents Cooperative Attack-Defense Evolution in High-Dimensional Environments

by Guofang Wang, Ziming Li, Wang Yao and Sikai Xia

Mathematics 2022, 10(21), 4075; https://doi.org/10.3390/math10214075 - 02 Nov 2022

Cited by 3 | Viewed by 1710

Abstract

As one of the important issues of multi-agent collaboration, the large-scale agents’ cooperative attack–defense evolution requires a large number of agents to make stress-effective strategies to achieve their goals in complex environments. Multi-agent attack and defense in high-dimensional environments (3D obstacle scenarios) present [...] Read more.

As one of the important issues of multi-agent collaboration, the large-scale agents’ cooperative attack–defense evolution requires a large number of agents to make stress-effective strategies to achieve their goals in complex environments. Multi-agent attack and defense in high-dimensional environments (3D obstacle scenarios) present the challenge of being able to accurately control high-dimensional state quantities. Moreover, the large scale makes the dynamic interactions in the attack and defense problems increase dramatically, which, using traditional optimal control techniques, can cause a dimensional explosion. How to model and solve the cooperative attack–defense evolution problem of large-scale agents in high-dimensional environments have become a challenge. We jointly considered energy consumption, inter-group attack and defense, intra-group collision avoidance, and obstacle avoidance in their cost functions. Meanwhile, the high-dimensional state dynamics were used to describe the motion of agents under environmental interference. Then, we formulated the cooperative attack–defense evolution of large-scale agents in high-dimensional environments as a multi-population high-dimensional stochastic mean-field game (MPHD-MFG), which significantly reduced the communication frequency and computational complexity. We tractably solved the MPHD-MFG with a generative-adversarial-network (GAN)-based method using the MFGs’ underlying variational primal–dual structure. Based on our approach, we carried out an integrative experiment in which we analytically showed the fast convergence of our cooperative attack–defense evolution algorithm by the convergence of the Hamilton–Jacobi–Bellman equation’s residual errors. The experiment also showed that a large number of drones can avoid obstacles and smoothly evolve their attack and defense behaviors while minimizing their energy consumption. In addition, the comparison with the baseline methods showed that our approach is advanced. Full article

(This article belongs to the Special Issue Recent Advances in Swarm Intelligence Algorithms and Their Applications)

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39 pages, 8022 KiB

Open AccessArticle

CLTSA: A Novel Tunicate Swarm Algorithm Based on Chaotic-Lévy Flight Strategy for Solving Optimization Problems

by Yi Cui, Ronghua Shi and Jian Dong

Mathematics 2022, 10(18), 3405; https://doi.org/10.3390/math10183405 - 19 Sep 2022

Cited by 6 | Viewed by 1556

Abstract

In this paper, we proposed a tunicate swarm algorithm based on Tent-Lévy flight (TLTSA) to avoid converging prematurely or failing to escape from a local optimal solution. First, we combined nine chaotic maps with the Lévy flight strategy to obtain nine different TSAs [...] Read more.

In this paper, we proposed a tunicate swarm algorithm based on Tent-Lévy flight (TLTSA) to avoid converging prematurely or failing to escape from a local optimal solution. First, we combined nine chaotic maps with the Lévy flight strategy to obtain nine different TSAs based on a Chaotic-Lévy flight strategy (CLTSA). Experimental results demonstrated that a TSA based on Tent-Lévy flight (TLTSA) performed the best among nine CLTSAs. Afterwards, the TLTSA was selected for comparative research with other well-known meta-heuristic algorithms. The 16 unimodal benchmark functions, 14 multimodal benchmark functions, 6 fixed-dimension functions, and 3 constrained practical problems in engineering were selected to verify the performance of TLTSA. The results of the test functions suggested that the TLTSA was better than the TSA and other algorithms in searching for global optimal solutions because of its excellent exploration and exploitation capabilities. Finally, the engineering experiments also demonstrated that a TLTSA solved constrained practical engineering problems more effectively. Full article

(This article belongs to the Special Issue Recent Advances in Swarm Intelligence Algorithms and Their Applications)

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24 pages, 1837 KiB

Open AccessArticle

Oppositional Pigeon-Inspired Optimizer for Solving the Non-Convex Economic Load Dispatch Problem in Power Systems

by Rajakumar Ramalingam, Dinesh Karunanidy, Sultan S. Alshamrani, Mamoon Rashid, Swamidoss Mathumohan and Ankur Dumka

Mathematics 2022, 10(18), 3315; https://doi.org/10.3390/math10183315 - 13 Sep 2022

Cited by 7 | Viewed by 1285

Abstract

Economic Load Dispatch (ELD) belongs to a non-convex optimization problem that aims to reduce total power generation cost by satisfying demand constraints. However, solving the ELD problem is a challenging task, because of its parity and disparity constraints. The Pigeon-Inspired Optimizer (PIO) is [...] Read more.

Economic Load Dispatch (ELD) belongs to a non-convex optimization problem that aims to reduce total power generation cost by satisfying demand constraints. However, solving the ELD problem is a challenging task, because of its parity and disparity constraints. The Pigeon-Inspired Optimizer (PIO) is a recently proposed optimization algorithm, which belongs to the family of swarm intelligence algorithms. The PIO algorithm has the benefit of conceptual simplicity, and provides better outcomes for various real-world problems. However, this algorithm has the drawback of premature convergence and local stagnation. Therefore, we propose an Oppositional Pigeon-Inspired Optimizer (OPIO) algorithm—to overcome these deficiencies. The proposed algorithm employs Oppositional-Based Learning (OBL) to enhance the quality of the individual, by exploring the global search space. The proposed algorithm would be used to determine the load demand of a power system, by sustaining the various equality and inequality constraints, to diminish the overall generation cost. In this work, the OPIO algorithm was applied to solve the ELD problem of small- (13-unit, 40-unit), medium- (140-unit, 160-unit) and large-scale (320-unit, 640-unit) test systems. The experimental results of the proposed OPIO algorithm demonstrate its efficiency over the conventional PIO algorithm, and other state-of-the-art approaches in the literature. The comparative results demonstrate that the proposed algorithm provides better results—in terms of improved accuracy, higher convergence rate, less computation time, and reduced fuel cost—than the other approaches. Full article

(This article belongs to the Special Issue Recent Advances in Swarm Intelligence Algorithms and Their Applications)

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18 pages, 4630 KiB

Open AccessArticle

IN-ME Position Error Compensation Algorithm for the Near-Field Beamforming of UAVs

by Yinan Zhang, Guangxue Wang, Yi Leng, Guowen Yu and Shirui Peng

Mathematics 2022, 10(18), 3256; https://doi.org/10.3390/math10183256 - 07 Sep 2022

Cited by 2 | Viewed by 1015

Abstract

The target of an unmanned aerial vehicle swarm will present near-field characteristics when it is integrated as an array, and the existence of the unmanned aerial vehicle swarm motion error will greatly deteriorate the beam pattern formed by the array. To solve these [...] Read more.

The target of an unmanned aerial vehicle swarm will present near-field characteristics when it is integrated as an array, and the existence of the unmanned aerial vehicle swarm motion error will greatly deteriorate the beam pattern formed by the array. To solve these problems, a near-field array beamforming model with array element position error is constructed, and the Taylor expansion of the phase difference function is used to approximately simplify the model. The improved Newton maximum entropy algorithm is proposed to estimate and compensate for the phase errors. The maximum entropy objective function is established, and the Newton iterative algorithm is used to estimate the phase error iteratively. To select the proper Newton iteration initial value, based on a single reference source signal, the initial value of the phase error is estimated through the phase gradient information of the received array signal. Beamforming is carried out after phase error compensation regarding the array. In order to assess the mismatch of the phase error compensation function based on the proposed method, when the beam is scanning, the effectively compensated spatial area of the array beamforming is divided, which lays a foundation for subsequent spatial region division and unmanned aerial vehicle swarm path planning. The simulation results show that the beam formed by the method proposed in this paper has a lower sidelobe level, and as the signal-to-noise ratio changes, the robustness of the proposed method is better validated. The proposed algorithm can effectively suppress the adverse influence of array element position error on array beamforming, and when the beam is scanning, the effectively compensated area of the phase error compensation function is divided, based on the proposed method. Full article

(This article belongs to the Special Issue Recent Advances in Swarm Intelligence Algorithms and Their Applications)

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18 pages, 9439 KiB

Open AccessArticle

DL-Aided Underground Cavity Morphology Recognition Based on 3D GPR Data

by Feifei Hou, Xu Liu, Xinyu Fan and Ying Guo

Mathematics 2022, 10(15), 2806; https://doi.org/10.3390/math10152806 - 08 Aug 2022

Cited by 7 | Viewed by 1858

Abstract

Cavity under urban roads has increasingly become a huge threat to traffic safety. This paper aims to study cavity morphology characteristics and proposes a deep learning (DL)-based morphology classification method using the 3D ground-penetrating radar (GPR) data. Fine-tuning technology in DL can be [...] Read more.

Cavity under urban roads has increasingly become a huge threat to traffic safety. This paper aims to study cavity morphology characteristics and proposes a deep learning (DL)-based morphology classification method using the 3D ground-penetrating radar (GPR) data. Fine-tuning technology in DL can be used in some cases with relatively few samples, but in the case of only one or very few samples, there will still be overfitting problems. To address this issue, a simple and general framework, few-shot learning (FSL), is first employed for the cavity classification tasks, based on which a classifier learns to identify new classes given only very few examples. We adopt a relation network (RelationNet) as the FSL framework, which consists of an embedding module and a relation module. Furthermore, the proposed method is simpler and faster because it does not require pre-training or fine-tuning. The experimental results are validated using the 3D GPR road modeling data obtained from the gprMax3D system. The proposed method is compared with other FSL networks such as ProtoNet, R2D2, and BaseLine relative to different benchmarks. The experimental results demonstrate that this method outperforms other prior approaches, and its average accuracy reaches 97.328% in a four-way five-shot problem using few support samples. Full article

(This article belongs to the Special Issue Recent Advances in Swarm Intelligence Algorithms and Their Applications)

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19 pages, 1412 KiB

Open AccessArticle

Harris Hawk Optimization-Based Deep Neural Networks Architecture for Optimal Bidding in the Electricity Market

by Kavita Jain, Muhammed Basheer Jasser, Muzaffar Hamzah, Akash Saxena and Ali Wagdy Mohamed

Mathematics 2022, 10(12), 2094; https://doi.org/10.3390/math10122094 - 16 Jun 2022

Cited by 6 | Viewed by 2003

Abstract

In the power sector, competitive strategic bidding optimization has become a major challenge. Digital plate-form provides a superior technical base as well as backing for the optimization’s execution. The state-of-the-art frameworks used for simulating strategic bidding decisions in deregulated electricity markets (EM’s) in [...] Read more.

In the power sector, competitive strategic bidding optimization has become a major challenge. Digital plate-form provides a superior technical base as well as backing for the optimization’s execution. The state-of-the-art frameworks used for simulating strategic bidding decisions in deregulated electricity markets (EM’s) in this article are bi-level optimization and neural networks. In this research, we provide HHO-NN (Harris Hawk Optimization-Neural network), a novel algorithm based on Harris Hawk Optimization (HHO) that is capable of fast convergence when compared to previous evolutionary algorithms for automatically searching for meaningful multilayered perceptron neural networks (MPNNs) topologies for optimal bidding. This technique usually demands a considerable amount of time and computer resources. This method sets up the problem in multi-dimensional continuous state-action spaces, allowing market players to get precise information on the effect of their bidding judgments on the market clearing results, as well as implement more valuable bidding decisions by utilizing a whole action domain and accounting for non-convex operating principles. Due to the use of the MPNN, case studies show that the suggested methodology delivers a much larger profit than other state-of-the-art methods and has a better computational performance than the benchmark HHO technique. Full article

(This article belongs to the Special Issue Recent Advances in Swarm Intelligence Algorithms and Their Applications)

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26 pages, 5782 KiB

Open AccessArticle

A Novel Approach Based on Honey Badger Algorithm for Optimal Allocation of Multiple DG and Capacitor in Radial Distribution Networks Considering Power Loss Sensitivity

by Mohamed A. Elseify, Salah Kamel, Hussein Abdel-Mawgoud and Ehab E. Elattar

Mathematics 2022, 10(12), 2081; https://doi.org/10.3390/math10122081 - 15 Jun 2022

Cited by 16 | Viewed by 2634

Abstract

Recently, the integration of distributed generators (DGs) in radial distribution systems (RDS) has been widely evolving due to its sustainability and lack of pollution. This study presents an efficient optimization technique named the honey badger algorithm (HBA) for specifying the optimum size and [...] Read more.

Recently, the integration of distributed generators (DGs) in radial distribution systems (RDS) has been widely evolving due to its sustainability and lack of pollution. This study presents an efficient optimization technique named the honey badger algorithm (HBA) for specifying the optimum size and location of capacitors and different types of DGs to minimize the total active power loss of the network. The Combined Power Loss Sensitivity (CPLS) factor is deployed with the HBA to accelerate the estimation process by specifying the candidate buses for optimal placement of DGs and capacitors in RDS. The performance of the optimization algorithm is demonstrated through the application to the IEEE 69-bus standard RDS with different scenarios: DG Type-I, DG Type-III, and capacitor banks (CBs). Furthermore, the effects of simultaneously integrating single and multiple DG Type-I with DG Type-III are illustrated. The results obtained revealed the effectiveness of the HBA for optimizing the size and location of single and multiple DGs and CBs with a considerable decline in the system’s real power losses. Additionally, the results have been compared with those obtained by other known algorithms. Full article

(This article belongs to the Special Issue Recent Advances in Swarm Intelligence Algorithms and Their Applications)

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33 pages, 4590 KiB

Open AccessArticle

Development and Applications of Augmented Whale Optimization Algorithm

by Khalid Abdulaziz Alnowibet, Shalini Shekhawat, Akash Saxena, Karam M. Sallam and Ali Wagdy Mohamed

Mathematics 2022, 10(12), 2076; https://doi.org/10.3390/math10122076 - 15 Jun 2022

Cited by 5 | Viewed by 1934

Abstract

Metaheuristics are proven solutions for complex optimization problems. Recently, bio-inspired metaheuristics have shown their capabilities for solving complex engineering problems. The Whale Optimization Algorithm is a popular metaheuristic, which is based on the hunting behavior of whale. For some problems, this algorithm suffers [...] Read more.

Metaheuristics are proven solutions for complex optimization problems. Recently, bio-inspired metaheuristics have shown their capabilities for solving complex engineering problems. The Whale Optimization Algorithm is a popular metaheuristic, which is based on the hunting behavior of whale. For some problems, this algorithm suffers from local minima entrapment. To make WOA compatible with a number of challenging problems, two major modifications are proposed in this paper: the first one is opposition-based learning in the initialization phase, while the second is inculcation of Cauchy mutation operator in the position updating phase. The proposed variant is named the Augmented Whale Optimization Algorithm (AWOA) and tested over two benchmark suits, i.e., classical benchmark functions and the latest CEC-2017 benchmark functions for 10 dimension and 30 dimension problems. Various analyses, including convergence property analysis, boxplot analysis and Wilcoxon rank sum test analysis, show that the proposed variant possesses better exploration and exploitation capabilities. Along with this, the application of AWOA has been reported for three real-world problems of various disciplines. The results revealed that the proposed variant exhibits better optimization performance. Full article

(This article belongs to the Special Issue Recent Advances in Swarm Intelligence Algorithms and Their Applications)

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16 pages, 2325 KiB

Open AccessArticle

Contextual Semantic-Guided Entity-Centric GCN for Relation Extraction

by Jun Long, Lei Liu, Hongxiao Fei, Yiping Xiang, Haoran Li, Wenti Huang and Liu Yang

Mathematics 2022, 10(8), 1344; https://doi.org/10.3390/math10081344 - 18 Apr 2022

Cited by 3 | Viewed by 1643

Abstract

Relation extraction tasks aim to predict potential relations between entities in a target sentence. As entity mentions have ambiguity in sentences, some important contextual information can guide the semantic representation of entity mentions to improve the accuracy of relation extraction. However, most existing [...] Read more.

Relation extraction tasks aim to predict potential relations between entities in a target sentence. As entity mentions have ambiguity in sentences, some important contextual information can guide the semantic representation of entity mentions to improve the accuracy of relation extraction. However, most existing relation extraction models ignore the semantic guidance of contextual information to entity mentions and treat entity mentions in and the textual context of a sentence equally. This results in low-accuracy relation extractions. To address this problem, we propose a contextual semantic-guided entity-centric graph convolutional network (CEGCN) model that enables entity mentions to obtain semantic-guided contextual information for more accurate relational representations. This model develops a self-attention enhanced neural network to concentrate on the importance and relevance of different words to obtain semantic-guided contextual information. Then, we employ a dependency tree with entities as global nodes and add virtual edges to construct an entity-centric logical adjacency matrix (ELAM). This matrix can enable entities to aggregate the semantic-guided contextual information with a one-layer GCN calculation. The experimental results on the TACRED and SemEval-2010 Task 8 datasets show that our model can efficiently use semantic-guided contextual information to enrich semantic entity representations and outperform previous models. Full article

(This article belongs to the Special Issue Recent Advances in Swarm Intelligence Algorithms and Their Applications)

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24 pages, 1955 KiB

Open AccessArticle

Usage of Selected Swarm Intelligence Algorithms for Piecewise Linearization

by Nicole Škorupová, Petr Raunigr and Petr Bujok

Mathematics 2022, 10(5), 808; https://doi.org/10.3390/math10050808 - 03 Mar 2022

Cited by 3 | Viewed by 1636

Abstract

The paper introduces a new approach to enhance optimization algorithms when solving the piecewise linearization problem of a given function. Eight swarm intelligence algorithms were selected to be experimentally compared. The problem is represented by the calculation of the distance between the original [...] Read more.

The paper introduces a new approach to enhance optimization algorithms when solving the piecewise linearization problem of a given function. Eight swarm intelligence algorithms were selected to be experimentally compared. The problem is represented by the calculation of the distance between the original function and the estimation from the piecewise linear function. Here, the piecewise linearization of 2D functions is studied. Each of the employed swarm intelligence algorithms is enhanced by a newly proposed automatic detection of the number of piecewise linear parts that determine the discretization points to calculate the distance between the original and piecewise linear function. The original algorithms and their enhanced variants are compared on several examples of piecewise linearization problems. The results show that the enhanced approach performs sufficiently better when it creates a very promising approximation of functions. Moreover, the degree of precision is slightly decreased by the focus on the speed of the optimization process. Full article

(This article belongs to the Special Issue Recent Advances in Swarm Intelligence Algorithms and Their Applications)

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22 pages, 1410 KiB

Open AccessArticle

Hybridisation of Swarm Intelligence Algorithms with Multi-Criteria Ordinal Classification: A Strategy to Address Many-Objective Optimisation

by Alejandro Castellanos, Laura Cruz-Reyes, Eduardo Fernández, Gilberto Rivera, Claudia Gomez-Santillan and Nelson Rangel-Valdez

Mathematics 2022, 10(3), 322; https://doi.org/10.3390/math10030322 - 20 Jan 2022

Cited by 6 | Viewed by 2225

Abstract

This paper introduces a strategy to enrich swarm intelligence algorithms with the preferences of the Decision Maker (DM) represented in an ordinal classifier based on interval outranking. Ordinal classification is used to bias the search toward the Region of Interest (RoI), the privileged [...] Read more.

This paper introduces a strategy to enrich swarm intelligence algorithms with the preferences of the Decision Maker (DM) represented in an ordinal classifier based on interval outranking. Ordinal classification is used to bias the search toward the Region of Interest (RoI), the privileged zone of the Pareto frontier containing the most satisfactory solutions according to the DM’s preferences. We applied this hybridising strategy to two swarm intelligence algorithms, i.e., Multi-objective Grey Wolf Optimisation and Indicator-based Multi-objective Ant Colony Optimisation for continuous domains. The resulting hybrid algorithms were called GWO-InClass and ACO-InClass. To validate our strategy, we conducted experiments on the DTLZ problems, the most widely studied test suit in the framework of multi-objective optimisation. According to the results, our approach is suitable when many objective functions are treated. GWO-InClass and ACO-InClass demonstrated the capacity of reaching the RoI better than the original metaheuristics that approximate the complete Pareto frontier. Full article

(This article belongs to the Special Issue Recent Advances in Swarm Intelligence Algorithms and Their Applications)

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Review

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17 pages, 1365 KiB

Open AccessReview

A Survey on Population-Based Deep Reinforcement Learning

by Weifan Long, Taixian Hou, Xiaoyi Wei, Shichao Yan, Peng Zhai and Lihua Zhang

Mathematics 2023, 11(10), 2234; https://doi.org/10.3390/math11102234 - 10 May 2023

Cited by 1 | Viewed by 2483

Abstract

Many real-world applications can be described as large-scale games of imperfect information, which require extensive prior domain knowledge, especially in competitive or human–AI cooperation settings. Population-based training methods have become a popular solution to learn robust policies without any prior knowledge, which can [...] Read more.

Many real-world applications can be described as large-scale games of imperfect information, which require extensive prior domain knowledge, especially in competitive or human–AI cooperation settings. Population-based training methods have become a popular solution to learn robust policies without any prior knowledge, which can generalize to policies of other players or humans. In this survey, we shed light on population-based deep reinforcement learning (PB-DRL) algorithms, their applications, and general frameworks. We introduce several independent subject areas, including naive self-play, fictitious self-play, population-play, evolution-based training methods, and the policy-space response oracle family. These methods provide a variety of approaches to solving multi-agent problems and are useful in designing robust multi-agent reinforcement learning algorithms that can handle complex real-life situations. Finally, we discuss challenges and hot topics in PB-DRL algorithms. We hope that this brief survey can provide guidance and insights for researchers interested in PB-DRL algorithms. Full article

(This article belongs to the Special Issue Recent Advances in Swarm Intelligence Algorithms and Their Applications)

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