Metaheuristics for Real-World Optimization Problems

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 31 March 2024 | Viewed by 15874

Special Issue Editors

Departamento de Lenguajes y Ciencias de la Computación, E.T.S. de Ingeniería Informática, ITIS Software, University of Malaga, 29071 Málaga, Spain
Interests: evolutionary computation; multi-objective optimization; parallel computing
Special Issues, Collections and Topics in MDPI journals
Departamento de Lenguajes y Ciencias de la Computación, E.T.S. de Ingeniería Informática, ITIS Software, University of Malaga, 29071 Málaga, Spain
Interests: evolutionary computation; particle swarm optimization; machine learning; big data analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Optimization problems found in real-world applications frequently have features that make them hard to be solved with exact techniques. The alternative is to use approximate techniques and, in this context, metaheuristics have emerged as a broad family of optimization algorithms that have gained many attention in the last 30 years.

This Special Issue on “Metaheuristics for Real-World Optimization Problems” is aimed at presenting recent advances in the application of metaheuristics to real-world problems. We are interested in studies and developments which can offer new insights and tools, leading to fostering the adoption of modern techniques in fields including Engineering, Medicine, Bioinformatics, Telecommunication, Logistics, Agriculture, etc. Hot topics we would like to cover include large-scale search spaces, Big Data applications, combination of metaheuristics and machine learning, and dealing with fitness fuctions that are costly to compute. Use cases describing successful applications of metaheuristics in complex scenarios are welcome.

Topics of interest include but are not limited to the following areas:

  • Large-scale optimization;
  • Metaheuristics and machine learning;
  • Big Data applications;
  • Hybrid metaheuristics;
  • Parallel metaheuristics;
  • Experiences in adopting metaheuristics in difficult real scenarios;
  • Surveys about adopting metaheuristics in particular fields.

Prof. Dr. Antonio Nebro
Prof. Dr. José Manuel García-Nieto
Guest Editors

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Published Papers (8 papers)

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Research

19 pages, 1282 KiB  
Article
Inverse Kinematics: An Alternative Solution Approach Applying Metaheuristics
by Raúl López-Muñoz, Edgar A. Portilla-Flores, Leonel G. Corona-Ramírez, Eduardo Vega-Alvarado and Mario C. Maya-Rodríguez
Appl. Sci. 2023, 13(11), 6543; https://doi.org/10.3390/app13116543 - 27 May 2023
Viewed by 1015
Abstract
The inverse kinematics problem (IKP) is fundamental in robotics, but it gets harder to solve as the complexity of the mechanisms increases. For that reason, several approaches have been applied to solve it, including metaheuristic algorithms. This work presents a proposal for solving [...] Read more.
The inverse kinematics problem (IKP) is fundamental in robotics, but it gets harder to solve as the complexity of the mechanisms increases. For that reason, several approaches have been applied to solve it, including metaheuristic algorithms. This work presents a proposal for solving the IKP of a doubly articulated kinematic chain by means of a modified differential evolution (DE) algorithm. The novelty of the proposal is both in the modeling of the problem and the modification to the DE for solving it. The modeling is inspired by a technique used in animation software to recreate movements by dividing the complete trajectory in a number of segments. Each segment represents a single optimization problem linked to the IKP as a sequence that is solved by the modified DE where the initial population for each single problem is biased by using the solution of the previous one. The approach produces solutions for positioning the end effector in a specific point within the work space while minimizing the angular displacement between the initial and final poses. The proposal was able to obtain solutions requiring a fewer total execution cycles compared to the usual approach of solving only one optimization problem related to the inverse kinematics. Different trajectories were used to test the solutions generated by the proposed approach, and the set of conditions that must be covered to apply it to solve the IKP of a particular mechanism are presented. Full article
(This article belongs to the Special Issue Metaheuristics for Real-World Optimization Problems)
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25 pages, 3212 KiB  
Article
Gaussian Mutation Specular Reflection Learning with Local Escaping Operator Based Artificial Electric Field Algorithm and Its Engineering Application
by Oluwatayomi Rereloluwa Adegboye and Ezgi Deniz Ülker
Appl. Sci. 2023, 13(7), 4157; https://doi.org/10.3390/app13074157 - 24 Mar 2023
Cited by 6 | Viewed by 928
Abstract
During the contribution of a metaheuristic algorithm for solving complex problems, one of the major challenges is to obtain the one that provides a well-balanced exploration and exploitation. Among the possible solutions to overcome this issue is to combine the strengths of the [...] Read more.
During the contribution of a metaheuristic algorithm for solving complex problems, one of the major challenges is to obtain the one that provides a well-balanced exploration and exploitation. Among the possible solutions to overcome this issue is to combine the strengths of the different methods. In this study, one of the recently developed metaheuristic algorithms, artificial electric field algorithm (AEFA), has been used, to improve its converge speed and the ability to avoid the local optimum points of the given problems. To address these issues, Gaussian mutation specular reflection learning (GS) and local escaping operator (LEO) have been added to the essential steps on AEFA and called GSLEO-AEFA. In order to observe the effect of the applied features, 23 benchmark functions as well as engineering and real-world application problems were tested and compared with the other algorithms. Friedman and Wilcoxon rank-sum statistical tests, and complexity analyses were also conducted to measure the performance of GSLEO-AEFA. The overall effectiveness of the algorithm among the compared algorithms obtained in between 84.62–92.31%. According to the achieved results, it can be seen that GSLEO-AEFA has precise optimization accuracy even in changing dimensions, especially in engineering optimization problems. Full article
(This article belongs to the Special Issue Metaheuristics for Real-World Optimization Problems)
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35 pages, 7635 KiB  
Article
Inverse Analysis of Structural Damage Based on the Modal Kinetic and Strain Energies with the Novel Oppositional Unified Particle Swarm Gradient-Based Optimizer
by Nizar Faisal Alkayem, Lei Shen, Tareq Al-hababi, Xiangdong Qian and Maosen Cao
Appl. Sci. 2022, 12(22), 11689; https://doi.org/10.3390/app122211689 - 17 Nov 2022
Cited by 13 | Viewed by 1488
Abstract
Structural damage inspection is a key structural engineering technique that strives for ensuring structural safety. In this regard, one of the major intelligent approaches is the inverse analysis of structural damage using evolutionary computation. By considering the recent advances in this field, an [...] Read more.
Structural damage inspection is a key structural engineering technique that strives for ensuring structural safety. In this regard, one of the major intelligent approaches is the inverse analysis of structural damage using evolutionary computation. By considering the recent advances in this field, an efficient hybrid objective function that combines the global modal kinetic and modal strain energies is introduced. The newly developed objective function aims to extract maximum dynamic information from the structure and overcome noisy conditions. Moreover, the original methods are usually vulnerable to the associated high multimodality and uncertainty of the inverse problem. Therefore, the oppositional learning (OL) for population initialization and convergence acceleration is first adopted. Thereafter, the unified particle swarm algorithm (UPSO) mechanism is combined with another newly developed algorithm, the gradient-based optimizer (GBO). The new algorithm, called the oppositional unified particle swarm gradient-based optimizer (OL-UPSGBO), with the convergence acceleration feature of (OL), enhances balanced exploration-exploitation of UPSO, and the local escaping operator of GBO is designed to specifically deal with the complex inverse analysis of structural damage problems. To authenticate the performance of the OL-UPSGBO, the complex benchmark set of CEC 2017 is adopted to compare the OL-UPSGBO with several original metaheuristics. Furthermore, the developed approach for structural damage identification is tested using several damage scenarios in a multi-story frame structure. Results show that the developed approach shows superior performance and robust behavior when tackling the inverse analysis of structural damage. Full article
(This article belongs to the Special Issue Metaheuristics for Real-World Optimization Problems)
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18 pages, 5698 KiB  
Article
An Evolutionary Algorithm to Optimise a Distributed UAV Swarm Formation System
by Daniel H. Stolfi and Grégoire Danoy
Appl. Sci. 2022, 12(20), 10218; https://doi.org/10.3390/app122010218 - 11 Oct 2022
Cited by 7 | Viewed by 1970
Abstract
In this article, we present a distributed robot 3D formation system optimally parameterised by a hybrid evolutionary algorithm (EA) in order to improve its efficiency and robustness. To achieve that, we first describe the novel distributed formation algorithm3 (DFA3), the [...] Read more.
In this article, we present a distributed robot 3D formation system optimally parameterised by a hybrid evolutionary algorithm (EA) in order to improve its efficiency and robustness. To achieve that, we first describe the novel distributed formation algorithm3 (DFA3), the proposed EA, and the two crossover operators to be tested. The EA hyperparameterisation is performed by using the irace package and the evaluation of the three case studies featuring three, five, and ten unmanned aerial vehicles (UAVs) is performed through realistic simulations by using ARGoS and ten scenarios evaluated in parallel to improve the robustness of the configurations calculated. The optimisation results, reported with statistical significance, and the validation performed on 270 unseen scenarios show that the use of a metaheuristic is imperative for such a complex problem despite some overfitting observed under certain circumstances. All in all, the UAV swarm self-organised itself to achieve stable formations in 95% of the scenarios studied with a plus/minus ten percent tolerance. Full article
(This article belongs to the Special Issue Metaheuristics for Real-World Optimization Problems)
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24 pages, 4479 KiB  
Article
Lemurs Optimizer: A New Metaheuristic Algorithm for Global Optimization
by Ammar Kamal Abasi, Sharif Naser Makhadmeh, Mohammed Azmi Al-Betar, Osama Ahmad Alomari, Mohammed A. Awadallah, Zaid Abdi Alkareem Alyasseri, Iyad Abu Doush, Ashraf Elnagar, Eman H. Alkhammash and Myriam Hadjouni
Appl. Sci. 2022, 12(19), 10057; https://doi.org/10.3390/app121910057 - 06 Oct 2022
Cited by 18 | Viewed by 2706
Abstract
The Lemur Optimizer (LO) is a novel nature-inspired algorithm we propose in this paper. This algorithm’s primary inspirations are based on two pillars of lemur behavior: leap up and dance hub. These two principles are mathematically modeled in the optimization context to handle [...] Read more.
The Lemur Optimizer (LO) is a novel nature-inspired algorithm we propose in this paper. This algorithm’s primary inspirations are based on two pillars of lemur behavior: leap up and dance hub. These two principles are mathematically modeled in the optimization context to handle local search, exploitation, and exploration search concepts. The LO is first benchmarked on twenty-three standard optimization functions. Additionally, the LO is used to solve three real-world problems to evaluate its performance and effectiveness. In this direction, LO is compared to six well-known algorithms: Salp Swarm Algorithm (SSA), Artificial Bee Colony (ABC), Sine Cosine Algorithm (SCA), Bat Algorithm (BA), Flower Pollination Algorithm (FPA), and JAYA algorithm. The findings show that the proposed algorithm outperforms these algorithms in fourteen standard optimization functions and proves the LO’s robust performance in managing its exploration and exploitation capabilities, which significantly leads LO towards the global optimum. The real-world experimental findings demonstrate how LO may tackle such challenges competitively. Full article
(This article belongs to the Special Issue Metaheuristics for Real-World Optimization Problems)
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28 pages, 10844 KiB  
Article
A Hybrid Golden Jackal Optimization and Golden Sine Algorithm with Dynamic Lens-Imaging Learning for Global Optimization Problems
by Panliang Yuan, Taihua Zhang, Liguo Yao, Yao Lu and Weibin Zhuang
Appl. Sci. 2022, 12(19), 9709; https://doi.org/10.3390/app12199709 - 27 Sep 2022
Cited by 16 | Viewed by 2497
Abstract
Golden jackal optimization (GJO) is an effective metaheuristic algorithm that imitates the cooperative hunting behavior of the golden jackal. However, since the update of the prey’s position often depends on the male golden jackal and there is insufficient diversity of golden jackals in [...] Read more.
Golden jackal optimization (GJO) is an effective metaheuristic algorithm that imitates the cooperative hunting behavior of the golden jackal. However, since the update of the prey’s position often depends on the male golden jackal and there is insufficient diversity of golden jackals in some cases, it is prone to falling into a local optimal optimum. In order to address these drawbacks of GJO, this paper proposes an improved algorithm, called a hybrid GJO and golden sine (S) algorithm (Gold-SA) with dynamic lens-imaging (L) learning (LSGJO). First, this paper proposes novel dual golden spiral update rules inspired by Gold-SA. These rules give GJO the ability to think like a human (Gold-SA), making the golden jackal more intelligent in the process of preying, and improving the ability and efficiency of optimization. Second, a novel nonlinear dynamic decreasing scaling factor is introduced into the lens-imaging learning operator to maintain the population diversity. The performance of LSGJO is verified through 23 classical benchmark functions and 3 complex design problems in real scenarios. The experimental results show that LSGJO converges faster and more accurately than 11 state-of-the-art optimization algorithms, the global and local search ability has improved significantly, and the proposed algorithm has shown superior performance in solving constrained problems. Full article
(This article belongs to the Special Issue Metaheuristics for Real-World Optimization Problems)
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15 pages, 2054 KiB  
Article
Applications of Metaheuristics Inspired by Nature in a Specific Optimisation Problem of a Postal Distribution Sector
by Michał Berliński, Eryk Warchulski and Stanisław Kozdrowski
Appl. Sci. 2022, 12(18), 9384; https://doi.org/10.3390/app12189384 - 19 Sep 2022
Cited by 2 | Viewed by 1035
Abstract
This paper presents a logistics problem, related to the transport of goods, which can be applied in practice, for example, in postal or courier services. Two mathematical models are presented as problems occurring in a logistics network. The main objective of the optimisation [...] Read more.
This paper presents a logistics problem, related to the transport of goods, which can be applied in practice, for example, in postal or courier services. Two mathematical models are presented as problems occurring in a logistics network. The main objective of the optimisation problem presented is to minimise capital resources (Capex), such as cars or containers. Three methods are proposed to solve this problem. The first is a method based on mixed integer programming (MIP) and available through the CPLEX solver. This method is the reference method for us because, as an exact method, it is guaranteed to find the optimal solution as long as the problem is not too large. However, the logistic problem under consideration belongs to the class of NP-complete problems and therefore, for larger problems, i.e., for networks of large size, the MIP method does not find any integer solution in a reasonable computational time. Therefore, two nature-inspired heuristic methods have been proposed. The first is the evolutionary algorithm and the second is the artificial bee colony algorithm. Results indicate that the heuristics methods solve instances of large size, giving suboptimal solutions and therefore, enabling their application to real-life scenarios. Full article
(This article belongs to the Special Issue Metaheuristics for Real-World Optimization Problems)
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18 pages, 1301 KiB  
Article
GRASP Optimization for the Strip Packing Problem with Flags, Waste Functions, and an Improved Restricted Candidate List
by Edgar Oviedo-Salas, Jesús David Terán-Villanueva, Salvador Ibarra-Martínez, Alejandro Santiago-Pineda, Mirna Patricia Ponce-Flores, Julio Laria-Menchaca, José Antonio Castán-Rocha and Mayra Guadalupe Treviño-Berrones
Appl. Sci. 2022, 12(4), 1965; https://doi.org/10.3390/app12041965 - 14 Feb 2022
Cited by 1 | Viewed by 2115
Abstract
This research addresses the two-dimensional strip packing problem to minimize the total strip height used, avoiding overlapping and placing objects outside the strip limits. This is an NP-hard optimization problem. We propose a greedy randomized adaptive search procedure (GRASP), incorporating flags as a [...] Read more.
This research addresses the two-dimensional strip packing problem to minimize the total strip height used, avoiding overlapping and placing objects outside the strip limits. This is an NP-hard optimization problem. We propose a greedy randomized adaptive search procedure (GRASP), incorporating flags as a new approach for this problem. These flags indicate available space after accommodating an object; they hold the available width and height for the following objects. We also propose three waste functions as surrogate objective functions for the GRASP candidate list and use and enhanced selection for the restricted candidate list, limiting the object options to better elements. Finally, we use overlapping functions to ensure that the object fits in the flag because there are some cases where a flag’s width can be wrong due to new object placement. The tests showed that our proposal outperforms the most recent state-of-the-art metaheuristic. Additionally, we make comparisons against two exact algorithms and another metaheuristic. Full article
(This article belongs to the Special Issue Metaheuristics for Real-World Optimization Problems)
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