Hybrid differential evolutionary strawberry algorithm for real-parameter optimization problems

Abstract

Evolutionary algorithms (EAs) is a family of population-based nature optimization methods. In contrast to classical optimization techniques, EAs provide a set of approximated solutions for different test suites of optimization and real-world problems in single simulation. In the last few years, hybrid EAs have received much attention by utilizing the valuable aspects of different nature of search strategies. Hybrid EAs are quite efficient in handling various optimization and search problems having had high complexity, noisy environment, imprecision, uncertainty and vagueness. In this article, a hybrid differential evolutionary strawberry algorithm (HDEA) is suggested to utilize the propagating behavior of the strawberry plant and perturbation process of differential evolution (DE) algorithm in order to evolve their population set of solutions. The proposed algorithm employs DE as a substitute while replacing the runners of the strawberry plant to effectively explore and exploit the search space of the problem at hand. The numerical results found by the proposed algorithm over most benchmark functions after extensive experiments are much promising in terms of proximity and diversity.