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A permutation-based dual genetic algorithm for dynamic optimization problems

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Abstract

Adaptation to dynamic optimization problems is currently receiving growing interest as one of the most important applications of genetic algorithms. Inspired by dualism and dominance in nature, genetic algorithms with the dualism mechanism have been applied for several dynamic problems with binary encoding. This paper investigates the idea of dualism for combinatorial optimization problems in dynamic environments, which are also extensively implemented in the real-world. A new variation of the GA, called the permutation-based dual genetic algorithm (PBDGA), is presented. Within this GA, two schemes based on the characters of the permutation in group theory are introduced: a partial-dualism scheme motivated by a new multi-attribute dualism mechanism and a learning scheme. Based on the dynamic test environments constructed by stationary benchmark problems, experiments are carried out to validate the proposed PBDGA. The experimental results show the efficiency of PBDGA in dynamic environments.

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Authors and Affiliations

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

    Lili Liu & Dingwei Wang

  2. Department of Industrial and Systems Engineering, Hong Kong Polytechnic University, Hum Hom, Hong Kong

    W. H. Ip

Authors
  1. Lili Liu
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  2. Dingwei Wang
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  3. W. H. Ip
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Correspondence to Lili Liu.

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Liu, L., Wang, D. & Ip, W.H. A permutation-based dual genetic algorithm for dynamic optimization problems. Soft Comput 13, 725–738 (2009). https://doi.org/10.1007/s00500-008-0345-5

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  • DOI: https://doi.org/10.1007/s00500-008-0345-5

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