Enhanced MPA for solving robot gripper and engineering design problems

This paper proposes a novel Enhanced Marine Predators Algorithm (EMPA) that integrates several innovative enhancements to improve original MPA algorithm performance to solve complex optimization problems such as engineering design problems. EMPA introduces dynamic parameter control, dynamic population size adjustment, and the elite pool to EMPA. These enhancements enable EMPA to better balance exploration and exploitation, leading to superior optimization results. The algorithm’s performance is rigorously tested using benchmark functions from the IEEE CEC 2014, CEC 2017, and CEC 2022 competitions, where it consistently outperforms other state-of-the-art optimization algorithms. EMPA’s has been applied to various engineering design problems, including material minimization in cantilever beams and structural optimization in three-bar trusses, demonstrating its practical effectiveness and adaptability. The results demonstrate that the performance of EMPA with varying dimensions outperforms the compared optimizers. For instance, in experiments with a dimension equal to 10, EMPA achieved the best performance in 9 functions on the CEC2022 benchmark, 27 functions on the CEC2017 benchmark, and achieving the first or second-best optimizer ranking in 27 functions on the CEC2014 benchmark. • EMPA introduced novel enhancements to the original Marine Predators Algorithm (MPA) including: (a) A dynamic parameter control for balancing exploration and exploitation. (b) A dynamic population size adjustment strategy. (c) An elite pool mechanism to maintain top solutions and prevent premature convergence. • EMPA Design includes a phase-based optimization strategy: exploration, transition, and exploitation. • EMPA uses an improved mechanism to influence prey movements under certain conditions for enhanced exploration. • EMPA results Showcased superior performance through comprehensive benchmark testing. • EMPA Demonstrated practical effectiveness in optimizing engineering design problems and solving robot gripper problem.