Black Widow Optimization Algorithm: A novel meta-heuristic approach for solving engineering optimization problems

Abstract

Nature-inspired optimization algorithms can solve different engineering and scientific problems owing to their easiness and flexibility. There is no need for structural modifications of optimization problems to apply meta-heuristic algorithms on them. Recently, meta-heuristic algorithms are becoming powerful methods for solving NP problems. In this paper, the authors propose a novel meta-heuristic algorithm suitable for continuous nonlinear optimization problems. The proposed method, Black Widow Optimization Algorithm (BWO), is inspired by the unique mating behavior of black widow spiders. This method includes an exclusive stage, namely, cannibalism. Due to this stage, species with inappropriate fitness are omitted from the circle, thus leading to early convergence. BWO algorithm is evaluated on 51 various benchmark functions to verify its efficiency in obtaining the optimal solutions for the problems. The obtained results indicate that the proposed algorithm has numerous advantages in different aspects such as early convergence and achieving optimized fitness value compared to other algorithms. Also, it has the capability of providing competitive and promising results. The research also solves three different challenging engineering design problems adopting BWO algorithm. The outcomes of the real case study problems prove the effectiveness of the proposed algorithm in solving real-world issues with unknown and challenging spaces.