Opposition based Ensemble Micro Differential Evolution

TL;DR

This paper introduces an opposition-based ensemble mutation strategy for Micro-DE, significantly improving population diversity and optimization performance on benchmark functions.

Contribution

It combines ensemble mutation schemes with opposition-based learning to enhance diversity in Micro-DE without extra parameter tuning.

Findings

Outperforms existing micro-DE algorithms on benchmark tests.

Improves exploration ability with minimal parameter setting.

Effective for high-dimensional optimization problems.

Abstract

Differential evolution (DE) algorithm with a small population size is called Micro-DE (MDE). A small population size decreases the computational complexity but also reduces the exploration ability of DE by limiting the population diversity. In this paper, we propose the idea of combining ensemble mutation scheme selection and opposition-based learning concepts to enhance the diversity of population in MDE at mutation and selection stages. The proposed algorithm enhances the diversity of population by generating a random mutation scale factor per individual and per dimension, randomly assigning a mutation scheme to each individual in each generation, and diversifying individuals selection using opposition-based learning. This approach is easy to implement and does not require the setting of mutation scheme selection and mutation scale factor. Experimental results are conducted for a variety of objective functions with low and high dimensionality on the CEC Black- Box Optimization Benchmarking 2015 (CEC-BBOB 2015). The results show superior performance of the proposed algorithm compared to the other micro-DE algorithms.