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Agent Based Evolutionary Dynamic Optimization

  • Chapter
Agent-Based Evolutionary Search

Part of the book series: Adaptation, Learning, and Optimization ((ALO,volume 5))

Abstract

Agent-based Evolutionary Search (AES) has attracted a growing interest from the evolutionary computation community in recent years due to its robust ability in solving large scale problems, ranging from online trading, disaster response to financial investment planning. In order to solve these problems, a great variety of intelligent techniques have been developed to improve the framework and efficiency of AES. This chapter investigates an AES algorithm in which the agents are updated and co-evolve to track dynamic optimum by imitating the exhibited feature of living organism. In the proposed algorithm, all agents live in a lattice like environment, where each agent is fixed on a lattice point. In order to increase the predefined energy function, individual agent is designed to compete with its neighbors and also acquire knowledge through cumulative information. For the purpose of maintaining the diversity of the population, random immigrants and adaptive primal dual mapping schemes are incorporated. Simulation experiments on a set of dynamic benchmark problems show the proposed AES algorithm can yield a better performance on dynamic optimization problems (DOPs) in comparison with several peer algorithms.

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Author information

Authors and Affiliations

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

    Yang Yan, Dazhi Wang & Dingwei Wang

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

    Shengxiang Yang

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  1. Yang Yan
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  2. Shengxiang Yang
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  3. Dazhi Wang
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  4. Dingwei Wang
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Editors and Affiliations

  1. School of Engineering and Information Technology, University of New SouthWales , ADFA Campus, Northcott Drive, 2600, Canberra, ACT, Australia

    Ruhul Amin Sarker  & Tapabrata Ray  & 

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Yan, Y., Yang, S., Wang, D., Wang, D. (2010). Agent Based Evolutionary Dynamic Optimization. In: Sarker, R.A., Ray, T. (eds) Agent-Based Evolutionary Search. Adaptation, Learning, and Optimization, vol 5. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13425-8_5

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  • DOI: https://doi.org/10.1007/978-3-642-13425-8_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-13424-1

  • Online ISBN: 978-3-642-13425-8

  • eBook Packages: EngineeringEngineering (R0)

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