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An immune system based differential evolution algorithm using near-neighbor effect in dynamic environments

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Abstract

Many real-world problems are dynamic, requiring optimization algorithms being able to continuously track changing optima (optimum) over time. This paper proposes an improved differential evolutionary algorithm using the notion of the near-neighbor effect to determine one individuals neighborhoods, for tracking multiple optima in the dynamic environment. A new mutation strategy using the near-neighbor effect is also presented. It creates individuals by utilizing the stored memory point in its neighborhood, and utilizing the differential vector produced by the ‘nearneighbor-superior’ and ‘near-neighbor-inferior’. Taking inspirations from the biological immune system, an immune system based scheme is presented for rapidly detecting and responding to the environmental changes. In addition, a difference-related multidirectional amplification scheme is presented to integrate valuable information from different dimensions for effectively and rapidly finding the promising optimum in the search space. Experiments on dynamic scenarios created by the typical dynamic test instance—moving peak problem, have demonstrated that the near-neighbor and immune system based differential evolution algorithm (NIDE) is effective in dealing with dynamic optimization functions.

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Author information

Authors and Affiliations

  1. PetroChina Northeast Refining & Chemicals Engineering Company Limited, Shenyang Liaoning, 110013, China

    Lili Liu

  2. School of Information Science and Engineering, Northeastern University, Shenyang Liaoning, 110819, China

    Dingwei Wang & Jiafu Tang

  3. State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang Liaoning, 110819, China

    Dingwei Wang & Jiafu Tang

Authors
  1. Lili Liu
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  2. Dingwei Wang
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  3. Jiafu Tang
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Corresponding author

Correspondence to Lili Liu.

Additional information

This work was partly supported by the Key Program of National Natural Science Foundation (NNSF) of China (Nos. 70931001, 70771021, 70721001), the National Natural Science Foundation of China for Youth (Nos. 61004121, 70771021), the Science Fund for Creative Research Group of NNSF of China (No. 60821063), and the Ph.D. Programs Foundation of Ministry of Education of China (No. 200801450008).

Lili LIU received her B.S. degree in automatic control and M.S. degree in systems engineering from Northeastern University, Shenyang, China in 2003 and in 2007, respectively, and Ph.D. degree in system engineering from Northeastern University in 2011. She is currently a lecture with the Department of Information Science and Engineering, Northeastern University. Her current research interests include evolutionary computation for dynamic optimization problems, i.e., dynamic problems within the contexts of scheduling, mobile ad hoc networks and traffic engineering. She is the author or coauthor of more than 30 technical papers.

DingweiWANG received his M.S. degree in systems engineering from Huazhong University of Science and Technology, China, in 1984, and Ph.D. degree in control theory and application from Northeastern University in China, in 1993. He had been a post doctor with North Caroline State University, USA, from 1994 to 1995. He is currently a professor in the Institute of Systems Engineering, Northeastern University in Shenyang, China. He has published more than 300 papers in international or domestic journals including IEEE Trans. on SMC, IEEE Trans. on Neural Network, Sciences in China, etc. His research interests include the modelling of optimization of complex systems, evolutionary computation and soft computing. He is a member of the Chinese Association Automation, member of the Chinese Association of Operations Research. He serves in the editorial board of Fuzzy Optimization and Decision Making, and several Chinese journals.

Jiafu TANG is currently a ChangJiang scholarship program chair professor and head with Department of System Engineering, School of Information Science and Engineering, and serving as vice director of the Stated Key Laboratory of Integrated Automation of Process Industry of MOE with Northeastern University (NEU), in Shenyang, China. He is interested in the areas of production and operations optimization in manufacturing systems; logistics optimization and transportation research in commercial and service systems; and quality design engineering and management.

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Liu, L., Wang, D. & Tang, J. An immune system based differential evolution algorithm using near-neighbor effect in dynamic environments. J. Control Theory Appl. 10, 417–425 (2012). https://doi.org/10.1007/s11768-012-0217-5

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  • DOI: https://doi.org/10.1007/s11768-012-0217-5

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