An improved African buffalo optimization algorithm using cha-otic map and chaotic-levy flight

Abstract

Optimization is ever-growing research that cuts across all walks of life. Many popular metaheuristic algorithms have metamorphosed into numerous variants in search of the sophisticated kernel for the optimal solutions. The African Buffalo optimization (ABO) algorithm is one of the fastest metaheuristic algorithms. This algorithm is inspired by the alarm and alert calls of African buffaloes during their foraging and defending activities. The present study investigates the strengths and weaknesses of ABO and proposes two improvement strategies: Chaotic ABO (CABO) and chaotic-levy flight ABO (CLABO). The results are validated with ten benchmark optimization problems and compared with other metaheuristic algorithms in the literature. Further, the CABO and CLABO algorithms are ranked first and second, respectively. This proves the superiority of the proposed improved algorithms over others under this study. Finally, the improved chaotic ABO would be utilized for optimizing industrial scheduling for oil and gas in our future work.