Intelligent multi-start methods

Abstract

Heuristic search procedures aimed at finding globally optimal solutions to hard combinatorial optimization problems usually require some type of diversification to overcome local optimality. One way to achieve diversification is to re-start the procedure from a new solution once a region has been explored, which constitutes a multi-start procedure. In this chapter we describe the best known multi-start methods for solving optimization problems. We also describe their connections with other metaheuristic methodologies. We propose classifying these methods in terms of their use of randomization, memory and degree of rebuild. We also present a computational comparison of these methods on solving the Maximum Diversity Problem to illustrate the efficiency of the multi-start methodology in terms of solution quality and diversification power.