A Competitive Divide-and-Conquer Algorithm for Unconstrained Large-Scale Black-Box Optimization

Mei, Yi; Omidvar, Mohammad Nabi; Li, Xiaodong; Yao, Xin

doi:10.1145/2791291

Cited by 199 publications

(114 citation statements)

References 47 publications

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“…For reducing the search iterations, there are two major ways. One is to enhance the search ability of existing EAs by re-scheduling the local search operators [20], [21]; while the other is to simplify the problem via Divide-and-Conquer (DC) [22], [23], [24] or Dimension Reduction [25], [26]. For saving the computational time in each iteration, parallel computing or distributed computing techniques are frequently employed to optimize individual solutions or decision variables on different threads [12], [13], [27], [28], [29].…”

Section: The Divide-and-conquer Based Easmentioning

confidence: 99%

“…If so, such pair of decision variables were deemed to be interdependent and grouped together. More works [17], [22], [34] improved the grouping accuracy by referencing a tighter interdependency among decision variables, i.e., the additively separability [35].…”

Section: The Divide-and-conquer Based Easmentioning

confidence: 99%

“…For example, Potter and De Jong [45] randomly selects the partial solutions in the current population (iteration) of other M − 1 sub-problems. The algorithms in [14], [17], [22], [23], [32], [33] select the best partial solutions in the current population of other M − 1 sub-problems. Other works like [14], [41], [42] select the best partial solutions from historical populations of other M − 1 sub-problems.…”

Section: The Serial Workflow Of Existing Dc-based Easmentioning

confidence: 99%

See 2 more Smart Citations

A Parallel Divide-and-Conquer-Based Evolutionary Algorithm for Large-Scale Optimization

2019

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Large-scale optimization problems that involve thousands of decision variables have extensively arisen from various industrial areas. As a powerful optimization tool for many real-world applications, evolutionary algorithms (EAs) fail to solve the emerging large-scale problems both effectively and efficiently. In this paper, we propose a novel Divide-and-Conquer (DC) based EA that can not only produce high-quality solution by solving sub-problems separately, but also highly utilizes the power of parallel computing by solving the sub-problems simultaneously. Existing DC-based EAs that were deemed to enjoy the same advantages of the proposed algorithm, are shown to be practically incompatible with the parallel computing scheme, unless some trade-offs are made by compromising the solution quality.

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Section: The Divide-and-conquer Based Easmentioning

confidence: 99%

Section: The Divide-and-conquer Based Easmentioning

confidence: 99%

Section: The Serial Workflow Of Existing Dc-based Easmentioning

confidence: 99%

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A Parallel Divide-and-Conquer-Based Evolutionary Algorithm for Large-Scale Optimization

2019

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“…Many real world LSOPs are difficult to tackle but possess an appealing feature, i.e., separability, where partially additive separability is the most common type and is most extensively studied in the CC research field [20][21][22][23]. The definition of additive separability can be described as follows.…”

Section: Description Of Saccmentioning

confidence: 99%

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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“…Differential grouping is further improved by Mei et al [10]. They adopted a modified CMA-ES as the base optimizer for solving sub-problems.…”

Section: Based Variant Of Cma-es Called Cc-cma-es For Largementioning

confidence: 99%

A Survey on Metaheuristics for Solving Large Scale Optimization Problems

Singh¹,

Jana²

2017

IJCA

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In recent years, there has been a remarkable improvement in the computing power of computers. As a result, numerous realworld optimization problems in science and engineering, possessing very high dimensions, have appeared. In the research community, they are generally labeled as Large Scale Global Optimization (LSGO) problems. Several Metaheuristics has been proposed to tackle these problems. Broadly these algorithms can be categorized in 3 groups: Standard Evolutionary Algorithms, Cooperative Co-evolution (CC) based Evolutionary Algorithms and Memetic Algorithms. This paper gives a brief introduction of some state-of-the-art Metaheuristics used in the field of LSGO, discusses their performance in CEC Competition on LSGO and finally, future scope in this field is presented.

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A Competitive Divide-and-Conquer Algorithm for Unconstrained Large-Scale Black-Box Optimization

Cited by 199 publications

References 47 publications

A Parallel Divide-and-Conquer-Based Evolutionary Algorithm for Large-Scale Optimization

A Parallel Divide-and-Conquer-Based Evolutionary Algorithm for Large-Scale Optimization

Surrogate model assisted cooperative coevolution for large scale optimization

A Survey on Metaheuristics for Solving Large Scale Optimization Problems

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