An improved artificial bee colony algorithm based on spider monkey optimization global search for complex benchmarks and engineering applications

Abstract The complexities of high-dimensional optimization problems and the exponential expansion of the search space make them extremely difficult to solve in both scientific and technical fields. This work presents a new metaheuristic method for benchmark global optimization problems called HABCSMO (Hybrid Artificial Bee Colony with Spider Monkey Optimization) in order to overcome this difficulty. By combining the exploration abilities of Artificial Bee Colony (ABC) and the exploitation abilities of Spider Monkey Optimization (SMO), the HABCSMO approach seeks to improve convergence speed and solution quality. The effectiveness of HABCSMO is confirmed by comprehensive tests on IEEE CEC2017 benchmark functions, standard benchmark functions, and real-world engineering challenges. The proposed method is tested on 7 real-world engineering problems, 20 standard benchmark functions, and 29 IEEE CEC2017 benchmark functions, proving its competitiveness and adaptability. The outcomes are contrasted using ABC and various ABC variants, including Modified ABC (MABC), Best-So-Far ABC (BSFABC), and Self-Adaptive ABC (SaABC), to demonstrate the superiority of HABCSMO. Furthermore, comparisons with the 8 well-established metaheuristic algorithms SMO, DE, PSO, GWO, WOA, AO, SCA, and SSA and 4 of the most recent algorithms SFOA, HOA, SFWO, and EAO with 2 hybrid algorithms AC-ABC and PSO-ABC show how effective our proposed HABCSMO approach. The results suggest that HABCSMO generates competitive solutions in terms of accuracy and convergence time, indicating its potential as a powerful optimization tool for difficult global optimization problems. The results validate HABCSMO as a competitive candidate in the metaheuristic space.