Differential evolution with modified initialization scheme using chaotic oppositional based learning strategy

Differential evolution (DE) is a popular optimization algorithm with easy implementation and fast convergence rate. For evolutionary algorithms such as DE, the initialization process of solution members is crucial because the distribution of initial population can govern the overall quality of final solution obtained in terms of accuracy and convergence speed. This study leverages the strengths of both chaotic maps and oppositional-based learning strategy to design a new DE variant with modified initialization scheme, namely chaotic oppositional DE (CODE) in order to generate the initial population with good quality of mean fitness and diversity of the solutions. The effectiveness of CODE variants incorporated with seven different chaotic maps are investigated using CEC 2014 benchmark functions and the chaotic circle oppositional DE (CCODE) is revealed as the best performing CODE variants. The optimization performance of CCODE is further compared with other existing optimization algorithms in terms of solution accuracy and convergence speed. Extensive simulation studies prove that the proposed algorithm is able to outperform its peers by achieving better trade-off between two contradicting requirements of fast convergence speed and population diversity preservation.(c) 2022 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).

Automated summary

Differential evolution (DE) is a popular optimization algorithm known for its simplicity and fast convergence rate. This study introduces a new DE variant called chaotic oppositional DE (CODE), which combines the strengths of chaotic maps and oppositional-based learning strategy to improve the quality of the initial population. The performance of CODE variants using seven different chaotic maps is evaluated, and chaotic circle oppositional DE (CCODE) is found to be the best performing variant. The optimization performance of CCODE is compared with other existing algorithms, demonstrating its superiority in terms of solution accuracy and convergence speed.