CBCC3 — A contribution-based cooperative co-evolutionary algorithm with improved exploration/exploitation balance

Omidvar, Mohammad Nabi; Kazimipour, Borhan; Li, Xiaodong; Yao, Xin

doi:10.1109/cec.2016.7744238

Cited by 55 publications

(32 citation statements)

References 14 publications

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“…Contribution-based cooperative coevolution (CBCC) [23], [32] is an improved CC framework whose component selection policy is based on the contribution of components towards improving the overall solution quality, which makes CBCC a good fit for solving IOPs. It is clear that CBCC requires an estimation for the contribution of each component, but due to the nature of the problem, the actual contribution of each component is not directly observable.…”

Section: Methodsmentioning

confidence: 99%

“…As mentioned previously, the incremental grouping method denoted by Algorithm 2 is invoked repeatedly within the CBCC framework at the beginning of each new design stage to find how the newly added decision variables interact with the previous ones, such that the results obtained from the previous stages can be reused in the new stage. In this paper, we modify the framework in [32] as our proposed CBCC framework for solving IOPs, where the details are given in Algorithm 3. The proposed CBCC framework differs from its original version in the following ways.…”

Section: Methodsmentioning

confidence: 99%

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Solving Incremental Optimization Problems via Cooperative Coevolution

Cheng

Omidvar

Gandomi

et al. 2019

IEEE Trans. Evol. Computat.

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Engineering designs can involve multiple stages, where at each stage, the design models are incrementally modified and optimized. In contrast to traditional dynamic optimization problems where the changes are caused by some objective factors, the changes in such incremental optimization problems are usually caused by the modifications made by the decision makers during the design process. While existing work in the literature is mainly focused on traditional dynamic optimization, little research has been dedicated to solving such incremental optimization problems. In this work, we study how to adopt cooperative coevolution to efficiently solve a specific type of incremental optimization problems, namely, those with increasing decision variables. First, we present a benchmark function generator on the basis of some basic formulations of incremental optimization problems with increasing decision variables and exploitable modular structure. Then, we propose a contribution based cooperative coevolutionary framework coupled with an incremental grouping method for dealing with them. On one hand, the benchmark function generator is capable of generating various benchmark functions with various characteristics. On the other hand, the proposed framework is promising in solving such problems in terms of both optimization accuracy and computational efficiency. In addition, the proposed method is further assessed using a real-world application, i.e., the design optimization of a stepped cantilever beam. Index Terms-incremental optimization problem, cooperative coevolution, variable grouping, experience based optimization I. INTRODUCTION O PTIMIZATION problems are widely seen in various areas of science and engineering. Some of the optimization problems have static objective functions, Manuscript received- .

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Solving Incremental Optimization Problems via Cooperative Coevolution

Cheng

Omidvar

Gandomi

et al. 2019

IEEE Trans. Evol. Computat.

Self Cite

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show abstract

“…3) the dynamics of the optimizer, its convergence behavior, and stagnation. Contributionbased cooperative coevolution (CBCC) [29], [30] is an improved CC framework which addresses the imbalance issue by assigning more resources to components with higher overall contributions. An important aspect of a contribution-aware coevolutionary framework is maintaining an optimal balance between an exploration phase in which the contribution of components is updated, and an exploitation phase in which the most contributing component is optimized.…”

Section: B Cooperative Coevolutionmentioning

confidence: 99%

Scaling Up Dynamic Optimization Problems: A Divide-and-Conquer Approach

Yazdani

Omidvar

Branke

et al. 2020

IEEE Trans. Evol. Computat.

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Scalability is a crucial aspect of designing efficient algorithms. Despite their prevalence, large-scale dynamic optimization problems are not well-studied in the literature. This paper is concerned with designing benchmarks and frameworks for the study of large-scale dynamic optimization problems. We start by a formal analysis of the moving peaks benchmark and show its nonseparable nature irrespective of its number of peaks. We then propose a composite moving peaks benchmark suite with exploitable modularity covering a wide range of scalable partially separable functions suitable for the study of largescale dynamic optimization problems. The benchmark exhibits modularity, heterogeneity, and imbalance features to resemble real-world problems. To deal with the intricacies of large-scale dynamic optimization problems, we propose a decompositionbased coevolutionary framework which breaks a large-scale dynamic optimization problem into a set of lower dimensional components. A novel aspect of the framework is its efficient bilevel resource allocation mechanism which controls the budget assignment to components and the populations responsible for tracking multiple moving optima. Based on a comprehensive empirical study on a wide range of large-scale dynamic optimization problems with up to 200 dimensions, we show the crucial role of problem decomposition and resource allocation in dealing with these problems. The experimental results clearly show the superiority of the proposed framework over three other approaches in solving large-scale dynamic optimization problems.

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“…CBCC3 was proposed by Omidvar for large-scale optimization based on Contribution-Based Cooperative Co-evolution (CBCC). It includes an exploration phase that is controlled by a random method and an exploitation phase controlled by a contribution information based mechanism where the contribution information of a given component is computed based on the last non-zero difference in the objective value of two consecutive iterations [30]. In [31], the authors applied the CC-framework to PSO and obtained satisfactory performance.…”

Section: Related Workmentioning

confidence: 99%

“…To validate the performance of the proposed algorithm, we employed a benchmark suite in CEC 2013 on large-scale optimization problems [33]. In the performance comparisons, four popular algorithms were adopted: CBCC3 [30], DECC-dg [24], DECC-dg2 [29] and SL-PSO [34]. The four algorithms are proposed to address large-scale optimization problems in the corresponding papers.…”

Section: Experimental Settingsmentioning

confidence: 99%

An Adaptive Multi-Swarm Competition Particle Swarm Optimizer for Large-Scale Optimization

2019

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As a powerful tool in optimization, particle swarm optimizers have been widely applied to many different optimization areas and drawn much attention. However, for large-scale optimization problems, the algorithms exhibit poor ability to pursue satisfactory results due to the lack of ability in diversity maintenance. In this paper, an adaptive multi-swarm particle swarm optimizer is proposed, which adaptively divides a swarm into several sub-swarms and a competition mechanism is employed to select exemplars. In this way, on the one hand, the diversity of exemplars increases, which helps the swarm preserve the exploitation ability. On the other hand, the number of sub-swarms adaptively changes from a large value to a small value, which helps the algorithm make a suitable balance between exploitation and exploration. By employing several peer algorithms, we conducted comparisons to validate the proposed algorithm on a large-scale optimization benchmark suite of CEC 2013. The experiments results demonstrate the proposed algorithm is effective and competitive to address large-scale optimization problems.

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CBCC3 — A contribution-based cooperative co-evolutionary algorithm with improved exploration/exploitation balance

Cited by 55 publications

References 14 publications

Solving Incremental Optimization Problems via Cooperative Coevolution

Solving Incremental Optimization Problems via Cooperative Coevolution

Scaling Up Dynamic Optimization Problems: A Divide-and-Conquer Approach

An Adaptive Multi-Swarm Competition Particle Swarm Optimizer for Large-Scale Optimization

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