Cooperative Co-Evolution With Differential Grouping for Large Scale Optimization

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

doi:10.1109/tevc.2013.2281543

Cited by 585 publications

(367 citation statements)

References 30 publications

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“…With random grouping, the parameters are frequently regrouped to increase the chance of having interacting parameters in the same subproblem. Omidvar et al (2014) propose differential grouping to automatically uncover the underlying substructures of the problem for grouping the parameters. The parameter groups are then determined so that the interactions between the subproblems is kept to a minimum.…”

Section: Cooperative Coevolutionary Algorithmmentioning

confidence: 99%

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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%

“…CC has a good performance especially on large-scale optimisation problems. Though, other works (Li and Yao 2009;Omidvar et al 2014Omidvar et al , 2010Potter and De Jong 1994;Ray and Yao 2009;Yang et al 2008) found that the CC struggles to solve non-separable problems (i.e. interactions/interdependencies between parameters).…”

Section: Introductionmentioning

confidence: 99%

Constructive cooperative coevolution for large-scale global optimisation

et al. 2017

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“…When tackling large-scale optimization problems, the divideand-conquer approach is commonly adopted to decompose the overall problem into smaller sub-problems (Boyd et al 2007;Omidvar et al 2014;Mei et al 2014a). For many real-world problems, the sub-problems are naturally defined.…”

Section: Introductionmentioning

confidence: 99%

On investigation of interdependence between sub-problems of the Travelling Thief Problem

Mei

Yao

2014

Soft Comput

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In this paper, the interdependence between subproblems in a complex overall problem is investigated using a benchmark problem called Travelling Thief Problem (TTP), which is a combination of Travelling Salesman Problem (TSP) and Knapsack Problem (KP). First, the analysis on the mathematical formulation shows that it is impossible to decompose the problem into independent sub-problems due to the non-linear relationship in the objective function. Therefore, the algorithm for TTP is not straightforward although each sub-problem alone has been investigated intensively. Then, two meta-heuristics are proposed for TTP. One is the Cooperative Co-evolution (CC) that solves the sub-problems separately and transfers the information between them in each generation. The other is the Memetic Algorithm (MA) that solves TTP as a whole. The comparative results showed that MA consistently obtained much better results than both the standard and dynamic versions of CC within comparable computational budget. This indicates the importance of considering the interdependence between sub-problems in an overall problem like TTP.

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“…The first case decomposes, usually manually, a problem before starting the evolutionary process, and it does not alter the decomposed components afterwards (Bucci and Pollack 2005;Panait and Luke 2005;Potter and Jong 2000). The second case predecomposes a problem at the beginning, but components are able to be self-adaptively tuned to proper interaction levels during the evolutionary process (Ray and Yao 2009;Weicker and Weicker 1999;Yang et al 2008a, b;Omidvar et al 2014). In step 2, there are two main patterns to evolve components: sequentially and in parallel.…”

mentioning

confidence: 99%

“…A recent research by Omidvar et al (2014) proposed an automatic decomposition strategy called differential grouping. Their method at first detects the underlying interaction structure of decision variables, and then form subcomponent based on the detection such that the interdependence between the variables is kept to a minimum.…”

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confidence: 99%

Reference sharing: a new collaboration model for cooperative coevolution

Shi

Gao

2017

J Heuristics

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Cooperative coevolutionary algorithms have been a popular and effective learning approach to solve optimization problems through problem decomposition. However, their performance is highly sensitive to the degree of problem separability. Different collaboration mechanisms usually have to be chosen for particular problems. In the paper, we aim to design a collaboration model that can be successfully applied to a wide range of problems. We present a novel collaboration mechanism that offers this type of potential, along with a new sorting strategy for individuals that are assigned multiple fitness values. Furthermore, we demonstrate and analyze our algorithm through comparison studies with other popular cooperative coevolutionary models on a suite of standard function optimization problems.

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Cooperative Co-Evolution With Differential Grouping for Large Scale Optimization

Cited by 585 publications

References 30 publications

Constructive cooperative coevolution for large-scale global optimisation

Constructive cooperative coevolution for large-scale global optimisation

On investigation of interdependence between sub-problems of the Travelling Thief Problem

Reference sharing: a new collaboration model for cooperative coevolution

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