Cooperative Co-evolutionary Differential Evolution for Function Optimization

Shi, Yufeng; Teng, Hong‐Fei; Li, Ziqiang

doi:10.1007/11539117_147

Cited by 139 publications

(82 citation statements)

References 6 publications

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“…The search then convergence towards a local optimum. Shi et al (2005) propose using Differential Evolution (DE) instead of a genetic algorithm for the subproblem optimisations in the cooperative coevolutionary algorithm. Furthermore, an alternative static decomposition scheme is proposed in which a subproblem takes half of the parameters.…”

Section: Cooperative Coevolutionary Algorithmmentioning

confidence: 99%

Constructive cooperative coevolution for large-scale global optimisation

et al. 2017

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This paper presents the Constructive Cooperative Coevolutionary (C 3 ) algorithm, applied to continuous large-scale global optimisation problems. The novelty of C 3 is that it utilises a multi-start architecture and incorporates the Cooperative Coevolutionary algorithm. The considered optimisation problem is decomposed into subproblems. An embedded optimisation algorithm optimises the subproblems separately while exchanging information to co-adapt the solutions for the subproblems. Further, C 3 includes a novel constructive heuristic that generates different feasible solutions for the entire problem and thereby expedites the search. In this work, two different versions of C 3 are evaluated on high-dimensional benchmark problems, including the CEC'2013 test suite for large-scale global optimisation. C 3 is compared with several state-of-the-art algorithms, which shows that C 3 is among the most competitive algorithms. C 3 outperforms the other algorithms for most partially separable functions and overlapping functions. This shows that C 3 is an effective algorithm for large-scale global optimisation. This paper demonstrates the enhanced performance by using constructive heuristics for generating initial feasible solutions for Cooperative Coevolutionary algorithms in a multi-start framework.

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Section: Cooperative Coevolutionary Algorithmmentioning

confidence: 99%

Constructive cooperative coevolution for large-scale global optimisation

et al. 2017

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“…In each generation of this algorithm, the population moves towards the global optimum by mutation, cross over and selection operators. Shi, Teng and Li [43] applied the cooperative behavior of Potter to DE and invented Cooperative Co-evolutionary Differential Evolution (CCDE). This CCDE fragmented the standard problem into several subproblems and allocated a subpopulation to each of them.…”

Section: -3 Review Of Cooperative Optimization Heuristicsmentioning

confidence: 99%

“…However, covariance is a measure of calculating the correlation between each pair of correlated variables. In standard benchmark functions which are used in [6], [7], [41], [43], [46], all dimensions are independent from each other, but in coordinate rotated test functions which are introduced in [8], [18], dimensions of the problem are correlated. The rotation matrix which is used to rotate the variables of the problem correlates all dimensions of the search space.…”

Section: Adaptive Cooperative Particle Swarm Optimizermentioning

confidence: 99%

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Adaptive cooperative particle swarm optimizer

2013

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“…Tasoulis et al [37] introduce parallel DE, where the population is divided into sub-populations, and each sub-population is assigned to a different processor node. In [30], Shi et al propose the so called cooperative co-evolutionary differential evolution, where multiple cooperating sub-populations are used and high dimensional search spaces are partitioned into smaller spaces. Other methods for improving DE are based on hybridization.…”

Section: Introductionmentioning

confidence: 99%

Self-adaptive randomized and rank-based differential evolution for multimodal problems

Urfalıoḡlu

Arıkan

2011

J Glob Optim

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Differential Evolution (DE) is a widely used successful evolutionary algorithm (EA) based on a population of individuals, which is especially well suited to solve problems that have non-linear, multimodal cost functions. However, for a given population, the set of possible new populations is finite and a true subset of the cost function domain. Furthermore, the update formula of DE does not use any information about the fitness of the population. This paper presents a novel extension of DE called Randomized and Rank-based Differential Evolution (R2DE) and its self-adaptive version SAR2DE to improve robustness and global convergence speed on multimodal problems by introducing two multiplicative terms in the DE update formula. The first term is based on a random variate of a Cauchy distribution, which leads to a randomization. The second term is based on ranking of individuals, so that R2DE exploits additional information provided by the population fitness. In extensive experiments conducted with a wide range of complexity settings, we show that the proposed heuristics lead to an overall improvement in robustness and speed of convergence compared to several global optimization techniques, including DE, Opposition based Differential Evolution (ODE), DE with Random Scale Factor (DERSF) and the self-adaptive Cauchy distribution based DE (NSDE).

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Cooperative Co-evolutionary Differential Evolution for Function Optimization

Cited by 139 publications

References 6 publications

Constructive cooperative coevolution for large-scale global optimisation

Constructive cooperative coevolution for large-scale global optimisation

Adaptive cooperative particle swarm optimizer

Self-adaptive randomized and rank-based differential evolution for multimodal problems

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