Abstract
Multi-objective optimization problems are often found in everyday life, such as in the tradeoff between the cost and quality of a product. As the simultaneous multiple objectives may conflict with each other, the optimization problem is very challenging, and there rarely is a single global optimum. There are two common approaches for multi-objective optimization problems. The first focuses on transforming the problem into a single objective problem through the use of normalization or combination techniques, whereas the second uses multi-objective evolutionary algorithms that construct a non-dominated solution set. As an alternative, in this paper we present a sequential Monte Carlo algorithm with two-stage sampling for multi-objective optimization problems. In the first stage, sampling is executed from within the non-dominated solution set generated by the algorithm; while in the second stage, sampling is performed from within the extreme points of the non-dominated solution set and some of the closest extreme points of the search space. The proposed approach has been benchmarked against well-known multi-objective optimization algorithms. It has performed better than the alternatives in problems where the Pareto front presents convexity, or multimodality; while producing promising results in instances with discontinuity along the Pareto front. [PUBLICATION ABSTRACT]