An improved marine predator algorithm based on epsilon dominance and Pareto archive for multi-objective optimization

Solving multi-objective optimization problems plays an important role in several applications. Recently, the Marine Predator Algorithm (MPA) was introduced for solving single objective optimization problems inspired by the behaviour of marine predator in their search for a prey. This paper proposes a modified MPA based framework called Guided Multi-objective Marine Predator Algorithm (GMOMPA) to solve multi-objective optimization problems. The proposed GMOMPA incorporates an external archive to help store the optimal Pareto set solution and guide the particles during the exploitation of the search space. For obtaining non -dominated solutions, the Pareto dominance concept is utilized while the epsilon dominance is considered to update the archive's sorted solutions. In this context, the epsilon dominance concept extends the diversity and exploration of the solutions. Further, a fast non-dominated solution and crowding distance are introduced to update the particle's position, while maintaining the diversity and ensuring a fast convergence towards the Pareto optimal. The proposed GMOMPA is evaluated on several different benchmark test functions including multi-objective ZDT, DTLZ, UF, and WFG test functions as well as the recent multi-objective multimodal CEC 2020 test functions. Moreover, the performance of the proposed GMOMPA is compared with well-known multi -objective optimization algorithms. The obtained results show that the proposed GMOMPA is a good tool for multi-objective optimization and has significant advantages over several state-of-the-art algorithms in almost all of the test functions.

Automated summary

This paper proposes a modified GMOMPA algorithm to solve multi-objective optimization problems, which incorporates an external archive to store the optimal Pareto set solution and guide particle exploration. The algorithm utilizes the concepts of Pareto dominance and epsilon dominance to obtain non-dominated solutions and update the archive's sorted solutions. Furthermore, it introduces fast non-dominated solution and crowding distance to update particle positions and maintain diversity. The proposed algorithm is evaluated on various benchmark test functions and outperforms state-of-the-art algorithms in most cases.