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Memetic Algorithms for Dynamic Optimization Problems

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Evolutionary Computation for Dynamic Optimization Problems

Part of the book series: Studies in Computational Intelligence ((SCI,volume 490))

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Abstract

Dynamic optimization problems challenge traditional evolutionary algorithms seriously since they, once converged, cannot adapt quickly to environmental changes. This chapter 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, a new immigrants scheme, where the immigrant individuals can be generated from mutating an elite individual adaptively, is 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 algorithms. The experimental results show the efficiency of the proposed memetic algorithm in dynamic environments.

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Authors and Affiliations

  1. College of Information Science and Engineering, Northeastern University, Shenyang, 110004, China

    Hongfeng Wang

  2. Centre for Computational Intelligence (CCI), School of Computer Science and Informatics, De Montfort University, The Gateway, Leicester, LE1 9BH, U.K.

    Shengxiang Yang

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

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Editors and Affiliations

  1. , School of Computer Science and Informati, De Montfort University, The Gateway, Leicester, LE1 9BH, United Kingdom

    Shengxiang Yang

  2. , School of Computer Science, University of Birmingham, Birmingham, B15 2TT, United Kingdom

    Xin Yao

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Wang, H., Yang, S. (2013). Memetic Algorithms for Dynamic Optimization Problems. In: Yang, S., Yao, X. (eds) Evolutionary Computation for Dynamic Optimization Problems. Studies in Computational Intelligence, vol 490. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38416-5_6

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  • DOI: https://doi.org/10.1007/978-3-642-38416-5_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-38415-8

  • Online ISBN: 978-3-642-38416-5

  • eBook Packages: EngineeringEngineering (R0)

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