Egyptian Mosquito Optimization Algorithm: A Novel Swarm-based Metaheuristic Algorithm to Solve Electronical and industrial Problems

This paper pioneers a novel swarm-based metaheuristics algorithm, the Egyptian Mosquito Optimization Algorithm (EMOA), which can be used to solve well-known global optimization problems. The EMOA is inspired by the natural behavior of Egyptian mosquitoes and achieves a balance between exploitation and exploration in the solution search space, effectively tackling optimization challenges. The effectiveness of EMOA is demonstrated using benchmark functions and the CEC-BC-2017 test suite, with further investigations extending to classical real-world measurement problems in the industrial and electronic domains. Finally, EMOA’s performance is compared to several well-known metaheuristic algorithms, with statistical results showing that EMOA outperforms recent algorithms in the literature, effectively solving complex real-world problems with unknown search spaces.