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A memetic algorithm with adaptive hill climbing strategy for dynamic optimization problems

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Abstract

Dynamic optimization problems challenge traditional evolutionary algorithms seriously since they, once converged, cannot adapt quickly to environmental changes. This paper investigates the application of memetic algorithms, a class of hybrid evolutionary algorithms, for dynamic optimization problems. An adaptive hill climbing method is proposed as the local search technique in the framework of memetic algorithms, which combines the features of greedy crossover-based hill climbing and steepest mutation-based hill climbing. In order to address the convergence problem, two diversity maintaining methods, called adaptive dual mapping and triggered random immigrants, respectively, are also introduced into the proposed memetic algorithm for dynamic optimization problems. Based on a series of dynamic problems generated from several stationary benchmark problems, experiments are carried out to investigate the performance of the proposed memetic algorithm in comparison with some peer evolutionary algorithms. The experimental results show the efficiency of the proposed memetic algorithm in dynamic environments.

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Authors and Affiliations

  1. School of Information Science and Engineering, Northeastern University, Shenyang, 110004, People’s Republic of China

    Hongfeng Wang & Dingwei Wang

  2. Department of Computer Science, University of Leicester, University Road, Leicester, LE1 7RH, UK

    Shengxiang Yang

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  1. Hongfeng Wang
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  2. Dingwei Wang
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  3. Shengxiang Yang
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Correspondence to Hongfeng Wang.

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Wang, H., Wang, D. & Yang, S. A memetic algorithm with adaptive hill climbing strategy for dynamic optimization problems. Soft Comput 13, 763–780 (2009). https://doi.org/10.1007/s00500-008-0347-3

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