Balanced multi-objective optimization algorithm using improvement based reference points approach

Abstract

• We suggest an enhancement for exploration and exploitation factors of the Equilibrium Optimizer (EO) to balance between exploration and exploitation operators in the proposed multi-objective equilibrium optimizer. • We propose an improvement-based reference points method increasing the diversity between the population members in multi-objective optimization problems. • The CEC 2020, CEC 2009, DTLZ, and ZDT test problems are solved using a multi-objective equilibrium optimizer algorithm. • Our proposed algorithm outperforms the other algorithms in terms of the spread and inverted generational distance measures for different test problems that satisfy all characteristics for optimization problems. In this work, we explore a novel multi-objective optimization algorithm to identify a set of solutions that could be optimal for more than one task. The proposed approach is used to generate a set of solutions that balance the tradeoff between convergence and diversity in multi-objective optimization problems. Equilibrium Optimizer (EO) algorithm is a novel developed meta-heuristic algorithm inspired by the physics laws. In this paper, we propose a Multi-objective Equilibrium Optimizer Algorithm (MEOA) for tackling multi-objective optimization problems. We suggest an enhancement for exploration and exploitation factors of the EO algorithm to randomize the values of these factors with decreasing the initial value of the exploration factor with the iteration and increasing the exploitation factor to accelerate the convergence toward the best solution. To achieve good convergence and well-distributed solutions, the proposed algorithm is integrated with the Improvement-Based Reference Points Method (IBRPM). The proposed approach is applied to the CEC 2020, CEC 2009, DTLZ, and ZDT test functions. Also, the inverted generational and spread spacing metrics are used to compare the proposed algorithm with the most recent evolutionary algorithms. It's obvious from the results that the proposed algorithm is better in both convergence and diversity.