Differential Evolution and Its Applications in Image Processing Problems: A Comprehensive Review

Differential evolution (DE) is one of the highly acknowledged population-based optimization algorithms due to its simplicity, user-friendliness, resilience, and capacity to solve problems. DE has grown steadily since its beginnings due to its ability to solve various issues in academics and industry. Different mutation techniques and parameter choices influence DE's exploration and exploitation capabilities, motivating academics to continue working on DE. This survey aims to depict DE's recent developments concerning parameter adaptations, parameter settings and mutation strategies, hybridizations, and multi-objective variants in the last twelve years. It also summarizes the problems solved in image processing by DE and its variants.

Automated summary

This article presents the recent developments of differential evolution algorithm, including parameter adaptations, parameter settings and mutation strategies, hybridizations, and multi-objective variants. It also summarizes the applications of differential evolution algorithm and its variants in image processing.