Intelligent Multi-Start Methods

Rafael Martí (), Ricardo Aceves (), Maria Teresa León (), Jose M. Moreno-Vega () and Abraham Duarte ()
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Rafael Martí: Universidad de Valencia
Ricardo Aceves: Universidad Nacional Autónoma de México
Maria Teresa León: Universidad de La Laguna
Jose M. Moreno-Vega: Universidad Rey Juan Carlos
Abraham Duarte: Universidad de Valencia

Chapter Chapter 7 in Handbook of Metaheuristics, 2019, pp 221-243 from Springer

Abstract: 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.

Keywords: Multi-start Method; Maximum Diversity Problem (MDP); Greedy Adaptive Search Procedure (GRASP); Multistart Algorithm; Persistent Attractiveness (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/978-3-319-91086-4_7

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