Affiliations: [a]
Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| [b]
School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran
| [c] Arts et Métiers Paris Tech, LCFC, Metz, France
| [d]
Department of Mathematics, Science and Research Branch, Islamic Azad University, Tehran, Iran
Correspondence:
[*]
Corresponding author. R. Tavakkoli-Moghaddam; E-mail: [email protected].
Abstract: This paper proposes a new multi-objective interior search algorithm (MOISA) for solving multi-objective optimization problems. Multi-objective complex mathematical models need to be solved by meta-heuristic algorithms in such a way that Pareto-optimal solutions are obtained; therefore, a new algorithm is presented in this paper for solving such models. The process of the interior search algorithm (ISA) is based on principles of interior design and decoration. This algorithm divides all elements, except the most suitable one, into two groups. In the first group, which is called the artistic composition group, algorithm changes the composition of elements to achieve a more desirable view. In the second group, which is called the mirror group, the algorithm places a mirror between the group elements and the most suitable element to find a better view. This paper uses the principles of the ISA in conjunction with the concepts of the non-dominated sorting and crowding distance to present the proposed MOISA, which is capable of obtaining near-optimal non-dominated solutions from solution space and identifying accurate Pareto fronts. To evaluate the performance of the foregoing algorithm, the related results of solving six models and a maximal covering location-allocation model are compared with several standard multi-objective evolutionary algorithms in terms of different metrics. This comparison shows that the results of the proposed MOISA are better than those obtained from other tested algorithms. Based on the solved numerical examples, the algorithm presented in this paper has many advantages over existing algorithms.
