Enhanced marine predator algorithm for global optimization and engineering design problems
Özet
Marine predator algorithm adopts the policy of optimal encounter rate in biological interaction between predator and prey, inspired by the Levy and Brownian motions commonly used in oceanic predators. However, the Marine predator algorithm faces problems of premature convergence, poor search capability, and stagnation in a local optimum. In order to tolerate these shortcomings, two different strategies are applied to Marine predator algorithm in this study. (1) Taylor-based optimal neighborhood strategy (TNS) provides a more effective update in obtaining optimal individuals, thus reducing the probability of local optimum falling and attempting to overcome premature convergence, (2) Asymmetric search space with dynamic option-based learning significantly increases the probability of the population achieving the global optimum, thus improving the exploitation ability of Marine predator algorithm. The new method introduced with these two strategies added to the Marine predator algorithm is called Marine Predator Algorithm Dynamic Opposition and Taylor neighborhood search (DOTMPA). DOTMPA's convergence performance is compared with current metaheuristic optimization and different versions of MPA in the literature on CEC2017, CEC2019, and CEC2022 benchmark suites. The results are discussed with convergence curves and box-plot analyses. DOTMPA is tested in Cantilever beam design, Tension/compression spring design, Speed reducer, Car crashworthiness, and Industrial refrigeration system engineering problems. The results are compared with the studies in the literature. Experimental results show that the proposed algorithm has strong competitiveness in terms of convergence accuracy. In addition, the stability of DOTMPA in convergence to the global optimum with trajectory analysis is also mentioned. Finally, the proposed method is applied to three truss topology optimization problems (20 -truss, 24-truss, and 72-truss). The experimental results, the applicability of the proposed algorithm in practical applications, and the benchmark problem make it a promising and competitive optimization algorithm for optimization problems.
