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Indexed by:期刊论文

Date of Publication:2018-01-01

Journal:INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS

Included Journals:SCIE

Volume:12

Issue:1

Page Number:149-163

ISSN No.:1875-6891

Key Words:Monarch butterfly optimization; local search strategy; differential evolution; PID tuning; FIR filter design

Abstract:Global optimization for nonlinear function is a challenging issue. In this paper, an improved monarch butterfly algorithm based on local search and differential evolution is proposed. Local search strategy is first embedded into original monarch butterfly optimization to enhance the searching capability. Then, differential evolution is incorporated with the aim of balancing the exploration and exploitation. To evaluate the performance of proposed algorithm, some widely-used benchmark functions are tested, and the experiment results show its significant superiority compared with other state-of-the-art methods. In addition, the proposed algorithm is applied to PID tuning and FIR filter design, the superiority of solving practical problems is verified.