Topic
Constructive cooperative coevolution
About: Constructive cooperative coevolution is a research topic. Over the lifetime, 3 publications have been published within this topic receiving 21 citations.
Papers
1 Dec 2015
TL;DR: The Improved Constructive Cooperative Coevolutionary Differential Evolution (C3iDE) algorithm is proposed, which removes several limitations with the previous version and shows an improved solution quality on large-scale global optimisation problems compared to CCDE and DE.
Abstract: The Differential Evolution (DE) algorithm is widely used for real-world global optimisation problems in many different domains. To improve DE's performance on large-scale optimisation problems, it has been combined with the Cooperative Coevolution (CCDE) algorithm. CCDE adopts a divide-and-conquer strategy to optimise smaller subcomponents separately instead of tackling the large-scale problem at once. DE then evolves a separate subpopulation for each subcomponent but there is cooperation between the subpopulations to co-adapt the individuals of the subpopulations with each other. The Constructive Cooperative Coevolution (C3DE) algorithm, previously proposed by the authors, is an extended version of CCDE that has a better performance on large-scale problems, interestingly also on non-separable problems. This paper proposes a new version, called the Improved Constructive Cooperative Coevolutionary Differential Evolution (C3iDE), which removes several limitations with the previous version. A novel element of C3iDE is the advanced initialisation of the subpopulations. C3iDE initially optimises the subpopulations in a partially co-adaptive fashion. During the initial optimisation of a subpopulation, only a subset of the other subcomponents is considered for the co-adaptation. This subset increases stepwise until all subcomponents are considered. The experimental evaluation of C3iDE on 36 high-dimensional benchmark functions (up to 1000 dimensions) shows an improved solution quality on large-scale global optimisation problems compared to CCDE and DE. The greediness of the co-adaptation with C3iDE is also investigated in this paper.
19 citations
TL;DR: Two different versions of the Constructive Cooperative Coevolutionary algorithm, applied to continuous large-scale global optimisation problems, are evaluated on high-dimensional benchmark problems, demonstrating the enhanced performance by using constructive heuristics for generating initial feasible solutions for Cooperative CoEVolutionary algorithms in a multi-start framework.
Abstract: This paper presents the Constructive Cooperative Coevolutionary () algorithm, applied to continuous large-scale global optimisation problems. The novelty of 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, 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 are evaluated on high-dimensional benchmark problems, including the CEC'2013 test suite for large-scale global optimisation. is compared with several state-of-the-art algorithms, which shows that is among the most competitive algorithms. outperforms the other algorithms for most partially separable functions and overlapping functions. This shows that 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.
11 citations
1 Jan 2015
TL;DR: In this paper, the authors describe a large number of variables, non-linear, computationally expensive, complex and black-box (i.e. unknown internal structure) for engineering problems.
Abstract: Engineering problems have characteristics such as a large number of variables, non-linear, computationally expensive, complex and black-box (i.e. unknown internal structure). These characteristics ...
6 citations
Performance Metrics
| Year | Papers |
|---|---|
| 2017 | 1 |
| 2015 | 2 |