Hybrid parliamentary optimization and big bang-big crunch algorithm for global optimization

Abstract

Researchers have developed different metaheuristic algorithms to solve various optimization problems. Theefficiency of a metaheuristic algorithm depends on the balance between exploration and exploitation. This paper presentsthe hybrid parliamentary optimization and big bang-big crunch (HPO-BBBC) algorithm, which is a combination ofthe parliamentary optimization algorithm (POA) and the big bang-big crunch (BB-BC) optimization algorithm. Theintragroup competition phase of the POA is a process that searches for potential points in the search space, therebyproviding an exploration mechanism. By contrast, the BB-BC algorithm has an effective exploitation mechanism. Inthe proposed method, steps of the BB-BC algorithm are added to the intragroup competition phase of the POA inorder to improve the exploitation capabilities of the POA. Thus, the proposed method achieves a good balance betweenexploration and exploitation. The performance of the HPO-BBBC algorithm was tested using well-known mathematicaltest functions and compared with that of the POA, the BB-BC algorithm, and some other metaheuristics, namelythe genetic algorithm, multiverse optimizer, crow search algorithm, dragonfly algorithm, and moth-flame optimizationalgorithm. The HPO-BBBC algorithm was found to achieve better optimization performance and a higher convergencespeed than the above-mentioned algorithms on most benchmark problems.