Enhanced shuffled frog-leaping algorithm for solving numerical function optimization problems

Chao Liu (), Peifeng Niu (), Guoqiang Li, Yunpeng Ma, Weiping Zhang and Ke Chen
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Chao Liu: Yanshan University
Peifeng Niu: Yanshan University
Guoqiang Li: Yanshan University
Yunpeng Ma: Yanshan University
Weiping Zhang: Qinhuangdao Institute of Technology
Ke Chen: Yanshan University

Journal of Intelligent Manufacturing, 2018, vol. 29, issue 5, No 11, 1133-1153

Abstract: Abstract The shuffled frog-leaping algorithm (SFLA) is a relatively new meta-heuristic optimization algorithm that can be applied to a wide range of problems. After analyzing the weakness of traditional SFLA, this paper presents an enhanced shuffled frog-leaping algorithm (MS-SFLA) for solving numerical function optimization problems. As the first extension, a new population initialization scheme based on chaotic opposition-based learning is employed to speed up the global convergence. In addition, to maintain efficiently the balance between exploration and exploitation, an adaptive nonlinear inertia weight is introduced into the SFLA algorithm. Further, a perturbation operator strategy based on Gaussian mutation is designed for local evolutionary, so as to help the best frog to jump out of any possible local optima and/or to refine its accuracy. In order to illustrate the efficiency of the proposed method (MS-SFLA), 23 well-known numerical function optimization problems and 25 benchmark functions of CEC2005 are selected as testing functions. The experimental results show that the enhanced SFLA has a faster convergence speed and better search ability than other relevant methods for almost all functions.

Keywords: Shuffled frog-leaping algorithm; Optimization; Opposition-based learning; Adaptive nonlinear inertia weight; Perturbation operator strategy; Gaussian mutation (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s10845-015-1164-z

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