Handbook of Heuristics: Multi-Start Methods [1 Chapter Only]

*THIS IS NOT THE FULL BOOK! Only 1 Chapter*

Multi-Start Methods by Rafael Martí, Jose A. Lozano, Alexander Mendiburu, Leticia Hernando 

Contents: Global Optimization; Combinatorial Optimization; Estimating the Number of Local Optima; Methods Proposed in the Metaheuristics Arena; Methods Proposed in the Field of Statistics; Experiments; Experimental Design; Results; Conclusions; Cross-References; References; Abstract 

Multi-start procedures were originally conceived as a way to exploit a local or neighborhood search procedure, by simply applying it from multiple random initial solutions. Modern multi-start methods usually incorporate a powerful form.

Heuristic search procedures that aspire to find global 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. 

In this chapter we describe the best known multi-start methods for solving optimization problems. 

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 linear ordering problem in terms of solution quality and diversification power. 

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*Full Book is 3000 Pages with 285 Figures & 120 Tables*

     —> Supplementary Material: sn.pub/extras

Heuristics are strategies using readily accessible, loosely applicable information to control problem solving. 

Algorithms, for example, are a type of heuristic. By contrast, Metaheuristics are methods used to design Heuristics and may coordinate the usage of several Heuristi