Ensemble of parameters in a sinusoidal differential evolution with niching-based population reduction

• Main Authors: Noor H. Awad, Mostafa Z. Ali, Suganthan, Ponnuthurai Nagaratnam
• Other Authors: School of Electrical and Electronic Engineering
• Format: Article
• Language: English
• Published: 2020
• Subjects: Engineering::Electrical and electronic engineering; Differential Evolution; Parameter Adaptation
• Online Access: https://hdl.handle.net/10356/139031
• Institution: Nanyang Technological University

Many parameter adaptation methods were proposed for Differential Evolution (DE) algorithm. Although these methods succeed in enhancing the performance of DE when solving a diverse set of optimization problems, locating the optimal solution is still a challenging task in most of these methods for complex optimization problems. To improve the performance of DE, this study presents a new enhanced algorithm based on our published work namely LSHADE with ensemble parameter sinusoidal adaptation, LSHADE-EpSin, which ranked the joint winner in IEEE CEC2016 competition on real-parameter single objective optimization. The method proposes a mixture of two sinusoidal formulas and a Cauchy distribution to balance the exploration and the exploitation of already found best solutions. A restart method is used at later generations to enhance the quality of the found solutions. The proposed algorithm also introduces a novel approach to adapt the population size by using a niching-based reduction scheme. In this mechanism, two separate niches are used before performing the population reduction, to reduce the population size in an effective manner. The proposed algorithm namely ensemble sinusoidal differential evolution with niching reduction, EsDEr-NR, is tested on the IEEE CEC2014 problems used in the special session and competitions on real-parameter single objective optimization of the IEEE CEC2016. The results statistically affirm the efficiency of the proposed approach to obtain better results compared to the other state-of-the-art algorithms from the literature including CMA-ES variants.