Open Access

ARTICLE

An Enhanced Adaptive Differential Evolution Approach for Constrained Optimization Problems

Wenchao Yi, Zhilei Lin, Yong Chen, Zhi Pei^*, Jiansha Lu

College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou, 310023, China

* Corresponding Author: Zhi Pei. Email:

(This article belongs to the Special Issue: Computing Methods for Industrial Artificial Intelligence)

Computer Modeling in Engineering & Sciences 2023, 136(3), 2841-2860. https://doi.org/10.32604/cmes.2023.027055

Received 11 October 2022; Accepted 25 November 2022; Issue published 09 March 2023

Abstract

Effective constrained optimization algorithms have been proposed for engineering problems recently. It is common to consider constraint violation and optimization algorithm as two separate parts. In this study, a pbest selection mechanism is proposed to integrate the current mutation strategy in constrained optimization problems. Based on the improved pbest selection method, an adaptive differential evolution approach is proposed, which helps the population jump out of the infeasible region. If all the individuals are infeasible, the top 5% of infeasible individuals are selected. In addition, a modified truncated ε-level method is proposed to avoid trapping in infeasible regions. The proposed adaptive differential evolution approach with an improved ε constraint process mechanism (IεJADE) is examined on CEC 2006 and CEC 2010 constrained benchmark function series. Besides, a standard IEEE-30 bus test system is studied on the efficiency of the IεJADE. The numerical analysis verifies the IεJADE algorithm is effective in comparison with other effective algorithms.

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