Improved marine predators algorithm for engineering design optimization problems

Abstract

Abstract The Marine Predators Algorithm (MPA) is recognized as one of the optimization method in population-based algorithm that mimics the foraging optimization strategy dominated by the optimal foraging theory, which encounter rate policy between predator and prey in marine ecosystems for solving optimization problems. However, MPA presents weak point towards premature convergence, stuck into local optima, lack of diversity, specifically, which is in the real-world niche problems within different industrial engineering design domains. To get rid of such limitations, this paper presents an Improved Marine Predators Algorithm (IMPA) to mitigate above mentioned limitations by deploying the self-adaptive weight and dynamic social learning mechanism that performs well and challenges tough multimodal benchmark-functions and CEC 2021 benchmark suite, compared with the state-of-the-art hybrid optimization algorithms and the recently modified MPA. The experimental results show that the IMPA outperforms with better precision attainment and better robustness due to its enjoying equalized exploration and exploitation feature over other methods. In order to provide a promising solution for industrial engineering design problems and highlight the potential of the IMPA as a useful tool for solving real-world problems. This study has implemented four highly representative engineering design problems, including Welded Beam Design, Tension/Compression Spring Design, Pressure Vessel Design and Three Bar Design. The experimental results also proved its efficiency to successfully solve the complex industrial engineering design problems.