MOIMPA: multi-objective improved marine predators algorithm for solving multi-objective optimization problems

Abstract This paper introduces a multi-objective variant of the marine predators algorithm (MPA) called improved (MOIMPA), which incorporates concepts from Quantum theory. By leveraging theory, MOIMPA aims to enhance MPA’s ability balance between exploration and exploitation find optimal solutions. The utilizes concept inspired by Schrödinger wave function determine position particles in search space. modification improves both exploitation, resulting enhanced performance. Additionally, proposed Pareto dominance mechanism. It stores non-dominated solutions repository employs roulette wheel strategy select repository, considering their coverage. To evaluate effectiveness efficiency MOIMPA, tests are conducted on various benchmark functions, including ZDT DTLZ, as well using evolutionary computation 2009 (CEC’09) test suite. is also evaluated engineering design problems. A comparison made approach other well-known optimization methods, such MOMPA, ant lion optimizer, multi-verse optimization. statistical results demonstrate robustness approach, measured metrics like inverted generational distance, generalized spacing, delta. Furthermore, qualitative experimental confirm that provides highly accurate approximations true fronts.