Chapter 5 - Local Search Methods

Abstract

This chapter examines that local search is one of the fundamental paradigms for solving computationally hard combinatorial problems, including the constraint satisfaction problem (CSP). It provides the basis for some of the most successful and versatile methods for solving the large and difficult problem instances encountered in many real-life applications. Despite impressive advances in systematic, complete search algorithms, local search methods in many cases represent the only feasible way for solving these large and complex instances. Local search algorithms are also naturally suited for dealing with the optimisation criteria arising in many practical applications. The chapter discusses that different local search methods vary in the way in which improvements are achieved in the way in which situations are handled in which no direct improvement is possible. Most of these methods use randomisation to ensure that the search process does not stagnate with unsatisfactory candidate solutions and are therefore referred to as stochastic local search (SLS) methods. The chapter also focuses on widely known and high-performing algorithms for the general CSP and for the propositional satisfiability problem (SAT), a special case of CSP, which plays an important role not only in constraint programming and reasoning research, but also in many other areas of computing science and beyond.