2023

Jump to: 2022

Open AccessArticle

Enhancing Metaheuristic Optimization: A Novel Nature-Inspired Hybrid Approach Incorporating Selected Pseudorandom Number Generators

by

Marko Gulić

and

Martina Žuškin

Algorithms 2023, 16(9), 413; https://doi.org/10.3390/a16090413 - 28 Aug 2023

Viewed by 383

Abstract

In this paper, a hybrid nature-inspired metaheuristic algorithm based on the Genetic Algorithm and the African Buffalo Optimization is proposed. The hybrid approach adaptively switches between the Genetic Algorithm and the African Buffalo Optimization during the optimization process, leveraging their respective strengths to [...] Read more.

In this paper, a hybrid nature-inspired metaheuristic algorithm based on the Genetic Algorithm and the African Buffalo Optimization is proposed. The hybrid approach adaptively switches between the Genetic Algorithm and the African Buffalo Optimization during the optimization process, leveraging their respective strengths to improve performance. To improve randomness, the hybrid approach uses two high-quality pseudorandom number generators—the 64-bit and 32-bit versions of the SIMD-Oriented Fast Mersenne Twister. The effectiveness of the hybrid algorithm is evaluated on the NP-hard Container Relocation Problem, focusing on a test set of restricted Container Relocation Problems with higher complexity. The results show that the hybrid algorithm outperforms the individual Genetic Algorithm and the African Buffalo Optimization, which use standard pseudorandom number generators. The adaptive switch method allows the algorithm to adapt to different optimization problems and mitigate problems such as premature convergence and local optima. Moreover, the importance of pseudorandom number generator selection in metaheuristic algorithms is highlighted, as it directly affects the optimization results. The use of powerful pseudorandom number generators reduces the probability of premature convergence and local optima, leading to better optimization results. Overall, the research demonstrates the potential of hybrid metaheuristic approaches for solving complex optimization problems, which makes them relevant for scientific research and practical applications. Full article

► Show Figures

Figure 1

Open AccessArticle

An Effective Local Particle Swarm Optimization-Based Algorithm for Solving the School Timetabling Problem

by

Ioannis X. Tassopoulos

,

Christina A. Iliopoulou

,

Iosif V. Katsaragakis

and

Grigorios N. Beligiannis

Algorithms 2023, 16(6), 291; https://doi.org/10.3390/a16060291 - 04 Jun 2023

Viewed by 859

Abstract

This paper deals with the school timetabling problem. The problem was formulated as encountered in a typical Greek high school. A local version of the particle swarm optimization algorithm was developed and applied to the problem at hand. Results on well-established benchmark instances [...] Read more.

This paper deals with the school timetabling problem. The problem was formulated as encountered in a typical Greek high school. A local version of the particle swarm optimization algorithm was developed and applied to the problem at hand. Results on well-established benchmark instances showed that the proposed algorithm achieved the proven optima provided from an integer programming method presented in earlier research. In almost all cases, the current algorithm beat the integer programming method, either concerning the lower bound yielded or the execution time needed. Full article

► Show Figures

Figure 1

Open AccessArticle

A Brain Storm and Chaotic Accelerated Particle Swarm Optimization Hybridization

by

Alkmini Michaloglou

and

Nikolaos L. Tsitsas

Algorithms 2023, 16(4), 208; https://doi.org/10.3390/a16040208 - 13 Apr 2023

Cited by 2 | Viewed by 1007

Abstract

Brain storm optimization (BSO) and particle swarm optimization (PSO) are two popular nature-inspired optimization algorithms, with BSO being the more recently developed one. It has been observed that BSO has an advantage over PSO regarding exploration with a random initialization, while PSO is [...] Read more.

Brain storm optimization (BSO) and particle swarm optimization (PSO) are two popular nature-inspired optimization algorithms, with BSO being the more recently developed one. It has been observed that BSO has an advantage over PSO regarding exploration with a random initialization, while PSO is more capable at local exploitation if given a predetermined initialization. The two algorithms have also been examined as a hybrid. In this work, the BSO algorithm was hybridized with the chaotic accelerated particle swarm optimization (CAPSO) algorithm in order to investigate how such an approach could serve as an improvement to the stand-alone algorithms. CAPSO is an advantageous variant of APSO, an accelerated, exploitative and minimalistic PSO algorithm. We initialized CAPSO with BSO in order to study the potential benefits from BSO’s initial exploration as well as CAPSO’s exploitation and speed. Seven benchmarking functions were used to compare the algorithms’ behavior. The chosen functions included both unimodal and multimodal benchmarking functions of various complexities and sizes of search areas. The functions were tested for different numbers of dimensions. The results showed that a properly tuned BSO–CAPSO hybrid could be significantly more beneficial over stand-alone BSO, especially with respect to computational time, while it heavily outperformed stand-alone CAPSO in the vast majority of cases. Full article

► Show Figures

Figure 1

Open AccessReview

Literature Review on Hybrid Evolutionary Approaches for Feature Selection

by

,

,

,

,

Vassilis C. Gerogiannis

and

Andreas Kanavos

Algorithms 2023, 16(3), 167; https://doi.org/10.3390/a16030167 - 20 Mar 2023

Cited by 2 | Viewed by 1666

Abstract

The efficiency and the effectiveness of a machine learning (ML) model are greatly influenced by feature selection (FS), a crucial preprocessing step in machine learning that seeks out the ideal set of characteristics with the maximum accuracy possible. Due to their dominance over [...] Read more.

The efficiency and the effectiveness of a machine learning (ML) model are greatly influenced by feature selection (FS), a crucial preprocessing step in machine learning that seeks out the ideal set of characteristics with the maximum accuracy possible. Due to their dominance over traditional optimization techniques, researchers are concentrating on a variety of metaheuristic (or evolutionary) algorithms and trying to suggest cutting-edge hybrid techniques to handle FS issues. The use of hybrid metaheuristic approaches for FS has thus been the subject of numerous research works. The purpose of this paper is to critically assess the existing hybrid FS approaches and to give a thorough literature review on the hybridization of different metaheuristic/evolutionary strategies that have been employed for supporting FS. This article reviews pertinent documents on hybrid frameworks that were published in the period from 2009 to 2022 and offers a thorough analysis of the used techniques, classifiers, datasets, applications, assessment metrics, and schemes of hybridization. Additionally, new open research issues and challenges are identified to pinpoint the areas that have to be further explored for additional study. Full article

► Show Figures

Figure 1

Open AccessArticle

Multi-Guide Set-Based Particle Swarm Optimization for Multi-Objective Portfolio Optimization

by

Kyle Erwin

and

Andries Engelbrecht

Algorithms 2023, 16(2), 62; https://doi.org/10.3390/a16020062 - 17 Jan 2023

Cited by 2 | Viewed by 835

Abstract

Portfolio optimization is a multi-objective optimization problem (MOOP) with risk and profit, or some form of the two, as competing objectives. Single-objective portfolio optimization requires a trade-off coefficient to be specified in order to balance the two objectives. Erwin and Engelbrecht proposed a [...] Read more.

Portfolio optimization is a multi-objective optimization problem (MOOP) with risk and profit, or some form of the two, as competing objectives. Single-objective portfolio optimization requires a trade-off coefficient to be specified in order to balance the two objectives. Erwin and Engelbrecht proposed a set-based approach to single-objective portfolio optimization, namely, set-based particle swarm optimization (SBPSO). SBPSO selects a sub-set of assets that form a search space for a secondary optimization task to optimize the asset weights. The authors found that SBPSO was able to identify good solutions to portfolio optimization problems and noted the benefits of redefining the portfolio optimization problem as a set-based problem. This paper proposes the first multi-objective optimization (MOO) approach to SBPSO, and its performance is investigated for multi-objective portfolio optimization. Alongside this investigation, the performance of multi-guide particle swarm optimization (MGPSO) for multi-objective portfolio optimization is evaluated and the performance of SBPSO for portfolio optimization is compared against multi-objective algorithms. It is shown that SBPSO is as competitive as multi-objective algorithms, albeit with multiple runs. The proposed multi-objective SBPSO, i.e., multi-guide set-based particle swarm optimization (MGSBPSO), performs similarly to other multi-objective algorithms while obtaining a more diverse set of optimal solutions. Full article

► Show Figures

Figure 1

2022

Jump to: 2023

Open AccessArticle

Hybrid Harmony Search for Stochastic Scheduling of Chemotherapy Outpatient Appointments

by

Roberto Rosario Corsini

,

Antonio Costa

,

Sergio Fichera

and

Vincenzo Parrinello

Algorithms 2022, 15(11), 424; https://doi.org/10.3390/a15110424 - 10 Nov 2022

Cited by 2 | Viewed by 1129

Abstract

This research deals with the same-day chemotherapy outpatient scheduling problem that is recognized as a leading strategy to pursue the objective of reducing patient waiting time. Inspired by a real-world context and different from the other studies, we modeled a multi-stage chemotherapy ward [...] Read more.

This research deals with the same-day chemotherapy outpatient scheduling problem that is recognized as a leading strategy to pursue the objective of reducing patient waiting time. Inspired by a real-world context and different from the other studies, we modeled a multi-stage chemotherapy ward in which the pharmacy is located away from the treatment area and drugs are delivered in batches. Processes in oncology wards are characterized by several sources of uncertainty that increase the complexity of the problem; thus, a stochastic approach was preferred to study the outpatient scheduling problem. To generate effective appointment schedules, we moved in two directions. First, we adopted a late-start scheduling strategy to reduce the idle times within and among the different stages, namely medical consultation, drug preparation and infusion. Then, since the problem is NP-hard in the strong sense, we developed a hybrid harmony search metaheuristic whose effectiveness was proved through an extended numerical analysis involving another optimization technique from the relevant literature. The outcomes from the numerical experiments confirmed the efficacy of the proposed scheduling model and the hybrid metaheuristic algorithm as well. Full article

► Show Figures

Figure 1

Open AccessArticle

k-Pareto Optimality-Based Sorting with Maximization of Choice and Its Application to Genetic Optimization

by

Jean Ruppert

,

Marharyta Aleksandrova

and

Thomas Engel

Algorithms 2022, 15(11), 420; https://doi.org/10.3390/a15110420 - 08 Nov 2022

Viewed by 1514

Abstract

Deterioration of the searchability of Pareto dominance-based, many-objective evolutionary optimization algorithms is a well-known problem. Alternative solutions, such as scalarization-based and indicator-based approaches, have been proposed in the literature. However, Pareto dominance-based algorithms are still widely used. In this paper, we propose to [...] Read more.

Deterioration of the searchability of Pareto dominance-based, many-objective evolutionary optimization algorithms is a well-known problem. Alternative solutions, such as scalarization-based and indicator-based approaches, have been proposed in the literature. However, Pareto dominance-based algorithms are still widely used. In this paper, we propose to redefine the calculation of Pareto-dominance. Instead of assigning solutions to non-dominated fronts, they are ranked according to the measure of dominating solutions referred to as k-Pareto optimality. In the case of probability measures, such re-definition results in an elegant and fast approximate procedure. Through experimental results on the many-objective

0 / 1

knapsack problem, we demonstrate the advantages of the proposed approach: (1) the approximate calculation procedure is much faster than the standard sorting by Pareto dominance; (2) it allows for achieving higher hypervolume values for both multi-objective (two objectives) and many-objective (25 objectives) optimization; (3) in the case of many-objective optimization, the increased ability to differentiate between solutions results in a better compared to NSGA-II and NSGA-III. Apart from the numerical improvements, the probabilistic procedure can be considered as a linear extension of multidimentional topological sorting. It produces almost no ties and, as opposed to other popular linear extensions, has an intuitive interpretation. Full article

► Show Figures

Figure 1

Open AccessArticle

Evolutionary Algorithm-Based Iterated Local Search Hyper-Heuristic for Combinatorial Optimization Problems

by

Stephen A. Adubi

,

Olufunke O. Oladipupo

and

Oludayo O. Olugbara

Algorithms 2022, 15(11), 405; https://doi.org/10.3390/a15110405 - 31 Oct 2022

Cited by 3 | Viewed by 1407

Abstract

Hyper-heuristics are widely used for solving numerous complex computational search problems because of their intrinsic capability to generalize across problem domains. The fair-share iterated local search is one of the most successful hyper-heuristics for cross-domain search with outstanding performances on six problem domains. [...] Read more.

Hyper-heuristics are widely used for solving numerous complex computational search problems because of their intrinsic capability to generalize across problem domains. The fair-share iterated local search is one of the most successful hyper-heuristics for cross-domain search with outstanding performances on six problem domains. However, it has recorded low performances on three supplementary problems, namely knapsack, quadratic assignment, and maximum-cut problems, which undermines its credibility across problem domains. The purpose of this study was to design an evolutionary algorithm-based iterated local search (EA-ILS) hyper-heuristic that applies a novel mutation operator to control the selection of perturbative low-level heuristics in searching for optimal sequences for performance improvement. The algorithm was compared to existing ones in the hyper-heuristics flexible (HyFlex) framework to demonstrate its performance across the problem domains of knapsack, quadratic assignment, and maximum cut. The comparative results have shown that the EA-ILS hyper-heuristic can obtain the best median objective function values on 22 out of 30 instances in the HyFlex framework. Moreover, it has achieved superiority in its generalization capability when compared to the reported top-performing hyper-heuristic algorithms. Full article

► Show Figures

Figure 1

Open AccessArticle

Domination and Independent Domination in Extended Supergrid Graphs

by

Jong-Shin Chen

,

Ruo-Wei Hung

,

Fatemeh Keshavarz-Kohjerdi

and

Yung-Fa Huang

Algorithms 2022, 15(11), 402; https://doi.org/10.3390/a15110402 - 30 Oct 2022

Cited by 1 | Viewed by 1190

Abstract

Supergrid graphs are derived by computing stitch paths for computerized embroidery machines. In the past, we have studied the Hamiltonian-related properties of supergrid graphs and their subclasses of graphs. In this paper, we propose a generalized graph class for supergrid graphs called extended [...] Read more.

Supergrid graphs are derived by computing stitch paths for computerized embroidery machines. In the past, we have studied the Hamiltonian-related properties of supergrid graphs and their subclasses of graphs. In this paper, we propose a generalized graph class for supergrid graphs called extended supergrid graphs. Extended supergrid graphs include grid graphs, supergrid graphs, diagonal supergrid graphs, and triangular supergrid graphs as subclasses of graphs. In this paper, we study the problems of domination and independent domination on extended supergrid graphs. A dominating set of a graph is the subset of vertices on it, such that every vertex of the graph is in this set or adjacent to at least a vertex of this set. If any two vertices in a dominating set are not adjacent, this is called an independent dominating set. Domination and independent domination problems find a dominating set and an independent dominating set with the least number of vertices on a graph, respectively. The domination and independent domination set problems on grid graphs are known to be NP-complete, meaning that these two problems on extended supergrid graphs are also NP-complete. However, the complexities of these two problems in other subclasses of graphs remain unknown. In this paper, we first prove that these two problems on diagonal supergrid graphs are NP-complete, then, by a simple extension, we prove that these two problems on supergrid graphs and triangular supergrid graphs are also NP-complete. In addition, these two problems on rectangular supergrid graphs are known to be linearly solvable; however, the complexities of these two problems on rectangular triangular-supergrid graphs remain unknown. This paper provides tight upper bounds on the sizes of the minimum dominating and independent dominating sets for rectangular triangular-supergrid graphs. Full article

► Show Figures

Figure 1

Open AccessArticle

Application of the Tomtit Flock Metaheuristic Optimization Algorithm to the Optimal Discrete Time Deterministic Dynamical Control Problem

by

Andrei V. Panteleev

and

Anna A. Kolessa

Algorithms 2022, 15(9), 301; https://doi.org/10.3390/a15090301 - 26 Aug 2022

Cited by 3 | Viewed by 939

Abstract

A new bio-inspired method for optimizing the objective function on a parallelepiped set of admissible solutions is proposed. It uses a model of the behavior of tomtits during the search for food. This algorithm combines some techniques for finding the extremum of the [...] Read more.

A new bio-inspired method for optimizing the objective function on a parallelepiped set of admissible solutions is proposed. It uses a model of the behavior of tomtits during the search for food. This algorithm combines some techniques for finding the extremum of the objective function, such as the memory matrix and the Levy flight from the cuckoo algorithm. The trajectories of tomtits are described by the jump-diffusion processes. The algorithm is applied to the classic and nonseparable optimal control problems for deterministic discrete dynamical systems. This type of control problem can often be solved using the discrete maximum principle or more general necessary optimality conditions, and the Bellman’s equation, but sometimes it is extremely difficult or even impossible. For this reason, there is a need to create new methods to solve these problems. The new metaheuristic algorithm makes it possible to obtain solutions of acceptable quality in an acceptable time. The efficiency and analysis of this method are demonstrated by solving a number of optimal deterministic discrete open-loop control problems: nonlinear nonseparable problems (Luus–Tassone and Li–Haimes) and separable problems for linear control dynamical systems. Full article

► Show Figures

Figure 1

Journal Menu

Journal Browser

Topical Collection "Feature Paper in Metaheuristic Algorithms and Applications"

Share This Topical Collection

Editor

Topical Collection Information

Keywords

Published Papers (10 papers)

2023

Jump to: 2022

2022

Jump to: 2023

Further Information

Guidelines

MDPI Initiatives

Follow MDPI