Efficient Resource Allocation in Cooperative Co-Evolution for Large-Scale Global Optimization

Yang, Ming; Omidvar, Mohammad Nabi; Li, Changhe; Li, Xiaodong; Cai, Zhihua; Kazimipour, Borhan; Yao, Xin

doi:10.1109/tevc.2016.2627581

Cited by 100 publications

(45 citation statements)

References 49 publications

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“…The difference between RBF-SHADE-SACC and SHADE-CC mainly lies in the sub-solution evaluation method. Moreover, we also compared RBF-SHADE-SACC with an existing CC algorithm developed in [35] with a name of CC-I. Different from SHADE-CC, CC-I uses another efficient DE variant named SaNSDE as optimizer.…”

Section: Comparison Between Rbf-shade-sacc and Other CC Algorithmsmentioning

confidence: 99%

“…Table 2 summarizes the results obtained by SHADE-CC and RBF-SHADE-SACC with 5 1.0 10  and 5 3.0 10  FEs and the results obtained by CC-I with 6 3.0 10  FEs. It is necessary to mention that the results of CC-I are directly taken from [35]. To statistically analyze the performance of the three competitors, we employed Cohen's d effect size [38] Table 2 is judged to be better than, worse than, or similar to the corresponding one obtained by RBF-SHADE-SACC, it is marked with '+', '−', and '≈', respectively.…”

Section: Comparison Between Rbf-shade-sacc and Other CC Algorithmsmentioning

confidence: 99%

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Surrogate model assisted cooperative coevolution for large scale optimization

et al. 2018

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It has been shown that cooperative coevolution (CC) can effectively deal with large scale optimization problems (LSOPs) through a divide-and-conquer strategy. However, its performance is severely restricted by the current context-vector-based sub-solution evaluation method since this method needs to access the original high dimensional simulation model when evaluating each sub-solution and thus requires many computation resources. To alleviate this issue, this study proposes a novel surrogate model assisted cooperative coevolution (SACC) framework. SACC constructs a surrogate model for each sub-problem obtained via decomposition and employs it to evaluate corresponding sub-solutions. The original simulation model is only adopted to reevaluate some good sub-solutions selected by surrogate models, and these real evaluated sub-solutions will be in turn employed to update surrogate models. By this means, the computation cost could be greatly reduced without significantly sacrificing evaluation quality. To show the efficiency of SACC, this study uses radial basis function (RBF) and success-history based adaptive differential evolution (SHADE) as surrogate model and optimizer, respectively. RBF and SHADE have been proved to be effective on small and medium scale problems. This study first scales them up to LSOPs of 1000 dimensions under the SACC framework, where they are tailored to a certain extent for adapting to the characteristics of LSOP and SACC. Empirical studies on IEEE CEC 2010 benchmark functions demonstrate that SACC significantly enhances the evaluation efficiency on sub-solutions, and even with much fewer computation resource, the resultant RBF-SHADE-SACC algorithm is able to find much better solutions than traditional CC algorithms. Keywords:Cooperative coevolution (CC); Large scale optimization problem (LSOP); Surrogate model; Radial basis function (RBF); Success-history based adaptive differential evolution (SHADE) Nowadays, large scale optimization problems (LSOPs) are becoming more and more popular in scientific research and engineering applications with the rapid development of big data techniques [1,2]. Since this kind of problems generally possesses black-box characteristics, the gradient-free evolutionary algorithms (EAs) are often employed to tackle them.However, the performance of conventional EAs rapidly deteriorates as the problem dimension increases. This is the so-called 'curse of dimensionality' [3,4], the main reason for which consists in that the solution space of a problem exponentially grows with the increase of its dimension and conventional EAs cannot adequately explore the solution space of a LSOP within acceptable computation time.Taking the idea of 'divide-and-conquer', cooperative coevolution (CC) provides a natural way for solving LSOPs [5]. It first decomposes an original LSOP into several smaller and simpler sub-problems, and then solves the LSOP by cooperatively optimizing all the sub-problems with a conventional EA. It is understandable that decomposition plays a fundamental ...

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Section: Comparison Between Rbf-shade-sacc and Other CC Algorithmsmentioning

confidence: 99%

Section: Comparison Between Rbf-shade-sacc and Other CC Algorithmsmentioning

confidence: 99%

Surrogate model assisted cooperative coevolution for large scale optimization

et al. 2018

View full text Add to dashboard Cite

show abstract

“…Then, the parameters of SHADE, n g D and P g are updated (steps 7-12). Finally, if a better solution is found, x* is updated (steps [14][15], and the fitness improvements of the subsolutions in n g D and P g are also updated (steps [16][17]. For the generation of the trial vectors and concrete update rule of the parameters of SHADE, readers can refer to [9].…”

Section: Description Of Rbf-shadementioning

confidence: 99%

“…When adopting ideal decomposition, the three algorithms are represented as SHADE-CC-I, PS-CC-I and ASMCC-I. Moreover, we also compared the three algorithms with an existing CC algorithm developed in [16] with a name of CC-I. Different from SHADE-CC, CC-I uses another efficient DE variant named SaNSDE as optimizer.…”

Section: Comparison Between Asmcc and Other CC Algorithms Under The Imentioning

confidence: 99%

“…Table 1 summarizes the results obtained by SHADE-CC-I, PS-CC-I and ASMCC-I with 5 3.0 10  FEs and the results obtained by CC-I with 6 3.0 10  FEs. It is necessary to mention that the results of CC-I is directly taken from [16]. And to statistically analyze the performance of the three competitors, we employed Cohen's d effect size [17] to quantify the difference among the average fitness values (FVs) obtained by them.…”

Section: Comparison Between Asmcc and Other CC Algorithms Under The Imentioning

confidence: 99%

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Enhancing cooperative coevolution for large scale optimization by adaptively constructing surrogate models

Pang

Ren

Liang

et al. 2018

Proceedings of the Genetic and Evolutionary Computation Conference Companion

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It  has been shown that cooperative coevolution (CC) can effectively deal with large scale optimization problems (LSOPs) through a divide-and-conquer strategy. However, its performance is severely restricted by the current context-vectorbased sub-solution evaluation method since this method needs to access the original high dimensional simulation model when evaluating each sub-solution and thus requires many computation resources. To alleviate this issue, this study proposes an adaptive surrogate model assisted CC framework. This framework adaptively constructs surrogate models for different sub-problems by fully considering their characteristics. For the single dimensional sub-problems obtained through decomposition, accurate enough surrogate models can be obtained and used to find out the optimal solutions of the corresponding sub-problems directly. As for the nonseparable sub-problems, the surrogate models are employed to evaluate the corresponding sub-solutions, and the original simulation model is only adopted to reevaluate some good sub-solutions selected by surrogate models. By these means, the computation cost could be greatly reduced without significantly sacrificing evaluation quality. Empirical studies on IEEE CEC 2010 benchmark functions show that the concrete algorithm based on this framework is able to find much better solutions than the conventional CC algorithms and a non-CC algorithm even with much fewer computation resources.

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Multi-objective Combinatorial Generative Adversarial Optimization and Its Application in Crowdsensing

Guo

Tan

et al. 2020

Lecture Notes in Computer Science

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With the increasing of the decision variables in multi-objective combinatorial optimization problems, the traditional evolutionary algorithms perform worse due to the low efficiency for generating the offspring by a stochastic mechanism. To address the issue, a multi-objective combinatorial generative adversarial optimization method is proposed to make the algorithm capable of learning the implicit information embodied in the evolution process. After classifying the optimal non-dominated solutions in the current generation as real data, the generative adversarial network (GAN) is trained by them, with the purpose of learning their distribution information. The Adam algorithm that employs the adaptively learning rate for each parameter is introduced to update the main parameters of GAN. Following that, an offspring reproduction strategy is designed to form a new feasible solution from the decimal output of the generator. To further verify the rationality of the proposed method, it is applied to solve the participant selection problem of the crowdsensing and the detailed offspring reproduction strategy is given. The experimental results for the crowdsensing systems with various tasks and participants show that the proposed algorithm outperforms the others in both convergence and distribution.

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Efficient Resource Allocation in Cooperative Co-Evolution for Large-Scale Global Optimization

Cited by 100 publications

References 49 publications

Surrogate model assisted cooperative coevolution for large scale optimization

Surrogate model assisted cooperative coevolution for large scale optimization

Enhancing cooperative coevolution for large scale optimization by adaptively constructing surrogate models

Multi-objective Combinatorial Generative Adversarial Optimization and Its Application in Crowdsensing

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