Black-hole gbest differential evolution algorithm for solving robot path planning problem

Abstract

The differential evaluation (DE) algorithm is a population-based very well-known meta-heuristic, proposed to fix the complex real-world optimization problems. This paper presents a variant of DE, inspired by the black-hole (BH) phenomenon in space and named as Black-Hole Gbest DE algorithm (BHGDE). In BHGDE, the realization of Black-Hole improves the exploration capability, while maintaining the original exploitation capability of the DE algorithm. The efficiency, reliability, accuracy, and robustness of the anticipated BHGDE algorithm are analyzed while simulating it over 15 complex benchmark functions of different modality and characteristics. The competitiveness of the newly anticipated BHGDE algorithm is proved by comparing the simulated results with the DE and its two recent variants, namely Opposition-based Differential Evolution (ODE) and Hybrid Artificial Bee Colony algorithm with Differential Evolution (HABCDE) algorithms. To check the robustness of the propounded BHGDE, it is implemented to solve the problem of path planning of the robots starting from the source node to the destination node.