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Low dimensional simplex evolution: a new heuristic for global optimization

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Abstract

This paper presents a new heuristic for global optimization named low dimensional simplex evolution (LDSE). It is a hybrid evolutionary algorithm. It generates new individuals following the Nelder-Mead algorithm and the individuals survive by the rule of natural selection. However, the simplices therein are real-time constructed and low dimensional. The simplex operators are applied selectively and conditionally. Every individual is updated in a framework of try-try-test. The proposed algorithm is very easy to use. Its efficiency has been studied with an extensive testbed of 50 test problems from the reference (J Glob Optim 31:635–672, 2005). Numerical results show that LDSE outperforms an improved version of differential evolution (DE) considerably with respect to the convergence speed and reliability.

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Authors and Affiliations

  1. Key Laboratory of High Temperature Gas Dynamics, Chinese Academy of Sciences, 100190, Beijing, China

    Changtong Luo

  2. Department of Applied Mathematics, Dalian University of Technology, Dalian, 116024, China

    Bo Yu

Authors
  1. Changtong Luo
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  2. Bo Yu
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Correspondence to Changtong Luo.

Additional information

This research has been supported by the National Natural Science Foundation of China (Grants 10632090 and 90916028).

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Luo, C., Yu, B. Low dimensional simplex evolution: a new heuristic for global optimization. J Glob Optim 52, 45–55 (2012). https://doi.org/10.1007/s10898-011-9678-1

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  • DOI: https://doi.org/10.1007/s10898-011-9678-1

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