Low dimensional simplex evolution: a new heuristic for global optimization

Authors:
Changtong Luo;Bo Yu
Affiliations:
Key Laboratory of High Temperature Gas Dynamics, Chinese Academy of Sciences, Beijing, China 100190;Department of Applied Mathematics, Dalian University of Technology, Dalian, China 116024
Venue:
Journal of Global Optimization
Year:
2012

Citing 6
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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.