A probability-driven search algorithm for solving multi-objective optimization problems

Abstract

This paper proposes a new probabilistic algorithm for solving multi-objective optimization problems - Probability-Driven Search Algorithm. The algorithm uses probabilities to control the process in search of Pareto optimal solutions. Especially, we use the absorbing Markov Chain to argue the convergence of the algorithm. We test this approach by implementing the algorithm on some benchmark multi-objective optimization problems, and find very good and stable results.