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IEEE Congress on Evolutionary Computation (CEC)

作者： Elsayed, Saber M. Sarker, Ruhul A. Essam, Daryl L. Hamza, Noha M. Univ New S Wales Sch Engn & Informat Technol Canberra ACT Australia

ISBN: (纸本)9781479914883

This paper puts forward a proposal for combining multi-operator evolutionary algorithms (EAs), in which three EAs, each with multiple search operators, are used. During the evolution process, the algorithm gradually emphasizes on the best performing multi-operator EA, as well as the search operator. The proposed algorithm is tested on the CEC2014 single objective real-parameter competition. The results show that the proposed algorithm has the ability to reach good solutions.

关键词： evolutionary algorithms multi-method algorithms multi-operator algorithms

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United multi-Operator Evolutionary algorithms

United Multi-Operator Evolutionary Algorithms

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IEEE Congress on Evolutionary Computation (CEC)

作者： Elsayed, Saber M. Sarker, Ruhul A. Essam, Daryl L. Univ New S Wales Sch Informat Technol & Engn Canberra ACT Australia

ISBN: (纸本)9781479914883

multi-method and multi-operator evolutionary algorithms (EAs) have shown superiority to any single EAs with a single operator. To further improve the performance of such algorithms, in this research study, a united multi-operator EAs framework is proposed, in which two EAs, each with multiple search operators, are used. During the evolution process, the algorithm emphasizes on the best performing multi-operator EA, as well as the search operator. The proposed algorithm is tested on a well-known set of constrained problems with 10D and 30D. The results show that the proposed algorithm scales well and is superior to the-state-of-the-art algorithms, especially for the 30D test problems.

关键词： Constrained optimization evolutionary algorithms multi-method algorithms multi-operator algorithms

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Adaptive Configuration of evolutionary algorithms for constrained optimization

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APPLIED MATHEMATICS AND COMPUTATION 2013年 222卷 680-711页

作者： Elsayed, Saber M. Sarker, Ruhul A. Essam, Daryl L. Univ New S Wales Sch Engn & Informat Technol Canberra ACT Australia

In the literature, many different evolutionary algorithms (EAs) with different search operators have been reported for solving optimization problems. However, no single algorithm is consistently able to solve all types of problems. To overcome this problem, the recent trend is to use a mix of operators within a single algorithm. There are also cases where multiple methodologies, each with a single search operator, have been used under one approach. These approaches outperformed the single operator based single algorithm approaches. In this paper, we propose a new algorithm framework that uses multiple methodologies, where each methodology uses multiple search operators. We introduce it as the EA with Adaptive Configuration, where the first level is to decide the methodologies and the second level is to decide the search operators. In this approach, all operators and population sizes are updated adaptively. Although the framework may sound complex, one can gain significant benefits from it in solving optimization problems. The proposed framework has been tested by solving two sets of specialized benchmark problems. The results showed a competitive, if not better, performance when it was compared to the state-of-the-art algorithms. Moreover, the proposed algorithm significantly reduces the computational time in comparison to both single and multi-operator based algorithms. (C) 2013 Elsevier Inc. All rights reserved.

关键词： Constrained optimization Evolutionary algorithms multi-method algorithms multi-operator algorithms

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