Island-based differential evolution with varying subpopulation size

Kushida, Jun-ichi; Hara, Akira; Takahama, Tetsuyuki; Kido, Ayumi

doi:10.1109/iwcia.2013.6624798

Cited by 16 publications

(10 citation statements)

References 5 publications

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“…The island-based Differential Evolution (i DE) algorithm [40] modifies the DE algorithm to use an island model. i DE divides the population of candidate solutions to islands with varying population size and parameters.…”

Section: Literature Reviewmentioning

confidence: 99%

Island-based Cuckoo Search with Elite Opposition-based Learning and Multiple Mutation Methods for Solving Discrete and Continuous Optimization Problems

Abed-alguni

Paul

2021

Preprint

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The Island Cuckoo Search ( i CSPM) algorithm is a new variation of Cuckoo Search (CS) that uses the island model and the Highly Disruptive Polynomial (HDP) mutation for solving a broad range of optimization problems. This article introduces an improved i CSPM algorithm called i CSPM with elite opposition-based learning and multiple mutation methods ( i CSPM2). i CSPM2 has three main characteristics. Firstly, it separates candidate solutions into a number of islands (sub-populations) and then divides the islands equally among four improved versions of CS: CS via Le'vy fights (CS1) [1], CS with HDPM mutation (CS10) [2], CS with Jaya mutation (CSJ) and CS with pitch adjustment mutation (CS11) [2]. Secondly, it uses Elite Opposition-based Learning (EOBL) to improve its convergence rate and exploration ability. Finally, it uses the Smallest Position Value (SPV) with scheduling problems to convert continuous candidate solutions into discrete ones. A set of 15 popular benchmark functions was used to compare the performance of iCSPM2 to the performance of the original i CSPM algorithm based on different experimental scenarios. Results indicate that i CSPM2 exhibits improved performance over i CSPM. However, the sensitivity analysis of i CSPM and i CSPM2 to their parameters indicates that their convergence behavior is sensitive to the island model parameters. Further, the single-objective IEEE CEC 2014 functions were used to evaluate and compare the performance of iCSPM2 to four well-known swarm optimization algorithms: DGWO [3], L-SHADE [4], MHDA [5] and FWA-DM [6]. The overall experimental and statistical results suggest that i CSPM2 has better performance than the four well-known swarm optimization algorithms. i CSPM2's performance was also compared to two powerful discrete optimization algorithms (GAIbH [7] and MASC [8]) using a set of Taillard's benchmark instances for the permutation flow shop scheduling problem. The results indicate that i CSPM2 performs better than GAIbH and MASC. The source code of i CSPM2 is publicly available at https://github.com/bilalh2021/iCSPM2

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Section: Literature Reviewmentioning

confidence: 99%

Island-based Cuckoo Search with Elite Opposition-based Learning and Multiple Mutation Methods for Solving Discrete and Continuous Optimization Problems

Abed-alguni

Paul

2021

Preprint

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“…When the migration frequency is reached, the migration rate determines the number of migrant individuals to be sent and received based on migration topology. The migration policy is another process in migration responsible for selecting the migrant individuals to be exchanged among islands (Kushida et al, 2013). Normally, some researchers introduced a migration policy either based on greedy step or randomly selection.…”

Section: Island Model Conceptsmentioning

confidence: 99%

“…Island based Genetic Algorithm (Lardeux & Goëffon, 2010;Rahman, Ś l ßezak, & Wróblewski, 2005;Skolicki & De Jong, 2004;Whitley et al, 1997), Island based Differential Evolution (Kushida et al, 2013;Thein, 2014), Island based Ant Colony (Michel & Middendorf, 1998), and Island based Particle Swarm Optimization (Romero & Cotta, 2005) are but few examples of methods that successfully incorporate their frameworks with island model concepts. Furthermore, the sensitivity of island-based methods to their parameters (number of islands, migration interval and migration frequency) have been well studied to show their effect on the convergence and their optimal values to improve the performance (Skolicki & De Jong, 2005;Tomassini, 2005).…”

Section: Island Model Conceptsmentioning

confidence: 99%

“…In structured population, instead of all the other solutions in the population being treated as potential mates as in panmictic populations, only those that are in the same neighborhood can share their information and interact (Tomassini, 2005). The island model is the most popular structured population EA method that divides the whole individuals of a large panmictic population into smaller independent subgroups called ''islands'' (Kushida, Hara, Takahama, & Kido, 2013). The EA method is run to each subgroup independently and the interaction between subgroups is achieved through the migration process which will be periodically performed.…”

Section: Introductionmentioning

confidence: 99%

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Island-based harmony search for optimization problems

Al-Betar

Awadallah

Khader

et al. 2015

Expert Systems with Applications

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“…The COBRA-f approach, like the original COBRA algorithm, was developed for continuous optimization [29], but despite its effectiveness compared to the mentioned biology-inspired algorithms (in other words its components), the COBRA-f meta-heuristic still needs to address the problem of exploitation and exploration [33]. As was noted before, a variety of ideas has been proposed to find the exploration-exploitation balance in the population-based biology-related algorithms, including methods of parameter adaptation [34][35][36], island models [37,38], population size control [39,40], and many others. One of the most valuable ideas proposed for the Differential Evolution (DE) [41] algorithm in the study [42] is to use an external archive of potentially good solutions, which has limited size and updated during the optimization process.…”

Section: Introductionmentioning

confidence: 99%

Success History-Based Position Adaptation in Fuzzy-Controlled Ensemble of Biology-Inspired Algorithms

et al. 2020

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In this study, a new modification of the meta-heuristic approach called Co-Operation of Biology-Related Algorithms (COBRA) is proposed. Originally the COBRA approach was based on a fuzzy logic controller and used for solving real-parameter optimization problems. The basic idea consists of a cooperative work of six well-known biology-inspired algorithms, referred to as components. However, it was established that the search efficiency of COBRA depends on its ability to keep the exploitation and exploration balance when solving optimization problems. The new modification of the COBRA approach is based on other method for generating potential solutions. This method keeps a historical memory of successful positions found by individuals to lead them in different directions and therefore to improve the exploitation and exploration capabilities. The proposed technique was applied to the COBRA components and to its basic steps. The newly proposed meta-heuristic as well as other modifications of the COBRA approach and components were evaluated on three sets of various benchmark problems. The experimental results obtained by all algorithms with the same computational effort are presented and compared. It was concluded that the proposed modification outperformed other algorithms used in comparison. Therefore, its usefulness and workability were demonstrated.

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Island-based differential evolution with varying subpopulation size

Cited by 16 publications

References 5 publications

Island-based Cuckoo Search with Elite Opposition-based Learning and Multiple Mutation Methods for Solving Discrete and Continuous Optimization Problems

Island-based Cuckoo Search with Elite Opposition-based Learning and Multiple Mutation Methods for Solving Discrete and Continuous Optimization Problems

Island-based harmony search for optimization problems

Success History-Based Position Adaptation in Fuzzy-Controlled Ensemble of Biology-Inspired Algorithms

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