Abstract

Multi-objective optimization is a branch of mathematics used in a large range of applications. It deals with optimization problems involving two or more conflicting objective functions to be optimized. Consequently, there is not a single solution that simultaneously optimizes these objectives, but a set of compromise solutions. These compromise solutions are also called non-dominated, Pareto-optimal, efficient, or non-inferior solutions. The best solution of this set is the one closest point to the utopia point. There are several approaches to perform multi-objective optimization. Undoubtedly the future of multi-objective optimization programming is in artificial intelligence applications. One of the artificial intelligence models is the Corona algorithm. It aims to simulate the epidemic behavior of the Corona virus that affects people's health and its treatment. In this paper, the artificial Corona algorithm is introduced and expanded for solving multi-objective programming problems, in which other models are not effective. The algorithm operates by iteratively selecting the initial values for decision variables of a multi-objective programming problem. The values of objective functions and constraint(s) are calculated. This proposed approach depends on a linear formula to update the solution. An acceptable efficient solution that has a minimum distance value from the utopia point is selected as the best point. To demonstrate the effectiveness of the proposed approach, some illustrative examples are given. These examples include both linear and nonlinear problems. The results indicate that the proposed approach has a high speed and capability to obtain the best solution when compared with other similar works of literature.