A majority–minority cellular automata algorithm for global optimization

Seck-Tuoh-Mora, Juan Carlos; Hernández-Romero, Norberto; Santander-Baños, Fredy; León, Valeria Volpi; Medina-Marín, Joselito; Lagos-Eulogio, Pedro

doi:10.1016/j.eswa.2022.117379

Cited by 9 publications

(7 citation statements)

References 33 publications

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“…Experiment 1: The I-WHO algorithm and the WHO algorithm are analyzed in 30, 60, and 90 dimensions on 25 benchmark functions [37,38].…”

Section: Experiments and Analysismentioning

confidence: 99%

Improving Wild Horse Optimizer: Integrating Multistrategy for Robust Performance across Multiple Engineering Problems and Evaluation Benchmarks

Chen,

Zhao,

et al. 2023

Mathematics

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In recent years, optimization problems have received extensive attention from researchers, and metaheuristic algorithms have been proposed and applied to solve complex optimization problems. The wild horse optimizer (WHO) is a new metaheuristic algorithm based on the social behavior of wild horses. Compared with the popular metaheuristic algorithms, it has excellent performance in solving engineering problems. However, it still suffers from the problem of insufficient convergence accuracy and low exploration ability. This article presents an improved wild horse optimizer (I-WHO) with early warning and competition mechanisms to enhance the performance of the algorithm, which incorporates three strategies. First, the random operator is introduced to improve the adaptive parameters and the search accuracy of the algorithm. Second, an early warning strategy is proposed to improve the position update formula and increase the population diversity during grazing. Third, a competition selection mechanism is added, and the search agent position formula is updated to enhance the search accuracy of the multimodal search at the exploitation stage of the algorithm. In this article, 25 benchmark functions (Dim = 30, 60, 90, and 500) are tested, and the complexity of the I-WHO algorithm is analyzed. Meanwhile, it is compared with six popular metaheuristic algorithms, and it is verified by the Wilcoxon signed-rank test and four real-world engineering problems. The experimental results show that I-WHO has significantly improved search accuracy, showing preferable superiority and stability.

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“…Experiment 1: The I-WHO algorithm and the WHO algorithm are analyzed in 30, 60, and 90 dimensions on 25 benchmark functions [37,38].…”

Section: Experiments and Analysismentioning

confidence: 99%

Improving Wild Horse Optimizer: Integrating Multistrategy for Robust Performance across Multiple Engineering Problems and Evaluation Benchmarks

Chen,

Zhao,

et al. 2023

Mathematics

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“…Examples of these optimization algorithms inspired by cellular automata for discrete problems include cellular particle swarm optimization VOLUME 10, 2022 with a simple adaptive local search (CAPSO-SALS) [32], local neighborhood search algorithm (LNSA) [33], and the global-local neighborhood search algorithm (GLNSA) [34]. For continuous problems and searching global optima, there are also the cellular particle swarm optimization (CPSO) [35], [36], the continuous-state cellular automata algorithm (CCAA) [9] and the majority-minority cellular automata algorithm (MmCAA) [10].…”

Section: Review Of Swarm Intelligence Metaheuristicsmentioning

confidence: 99%

“…Depending on the actual value f (s i ), if it is close to the best obtained cost f (b S ), then there will be a higher chance of rounding. This rule was introduced in [9] and also used in [10] and is very useful for bounding solutions in the later stages of the optimization process, especially for engineering design problems. Figure 4 depicts the operation of the rounding rule.…”

Section: Algorithm 2 Exploitation Rulementioning

confidence: 99%

“…The analogous process is done in case a value falls below lb by taking a random value between [lb, lb +(ub−lb)/4]. This process has been previously implemented in similar algorithms [9], [10], [36], obtaining satisfactory results. Figure 5 shows the flow chart of the proposed algorithm.…”

Section: Algorithm 3 Rounding Rulementioning

confidence: 99%

“…The original part of this work continues to adapt various complex behaviors in cellular automata to propose metaheuristics for the global optimization of functions, as has been done previously in [9] and [10]. This adaptation is materialized in an algorithm based on two new rules for exploration and exploitation that are easy to implement.…”

Section: Introductionmentioning

confidence: 99%

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A New Algorithm Inspired on Reversible Elementary Cellular Automata for Global Optimization

et al. 2022

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This work presents a new global optimization algorithm of functions inspired by the dynamic behavior of reversible cellular automata, denominated Reversible Elementary Cellular Automata Algorithm (RECAA). This algorithm adapts the reversible evolution rules in elementary cellular automata (in one dimension and only with two states) to work with vectors of real values to realize optimization tasks. The originality of RECAA lies in adapting the dynamic of the reversible elementary cellular automata to perform exploration and exploitation actions in the optimization process. This work shows that diversity in cellular automata behaviors (in this case, reversibility) is useful to define new metaheuristics to solve optimization problems. The algorithm is compared with 15 recently published metaheuristics that recognized for their good performance, using 50 test functions in 30, 500, and with a fixed number of dimensions, and the CEC 2022 benchmark suit. Additionally, it is shown that RECAA has been applied in 3 engineering problems. In all the experiments, RECAA obtained satisfactory results. RECAA was implemented in MATLAB, and its source code can be consulted in GitHub. https://github.com/juanseck/RECAA INDEX TERMS Engineering applications, global optimization, metaheuristics, reversible cellular automata.

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Improved optimal foraging algorithm for global optimization

Ding,

Zhu

2024

Computing

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A majority–minority cellular automata algorithm for global optimization

Cited by 9 publications

References 33 publications

Improving Wild Horse Optimizer: Integrating Multistrategy for Robust Performance across Multiple Engineering Problems and Evaluation Benchmarks

Improving Wild Horse Optimizer: Integrating Multistrategy for Robust Performance across Multiple Engineering Problems and Evaluation Benchmarks

A New Algorithm Inspired on Reversible Elementary Cellular Automata for Global Optimization

Improved optimal foraging algorithm for global optimization

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