Local search algorithms for solving the combinatorial optimization and constraint satisfaction problems

Abstract

Local search is a metaheuristic for solving computationally hard optimization problems. In the past three decades, local search has grown from a simple heuristic idea into a mature field of research in combinatorial optimization that is attracting ever-increasing attention. It is still the method of choice for NP-hard problems as it provides a robust approach for obtaining high-quality solutions to problems of a realistic size in reasonable time. Optimization problems such as the shortest path, the traveling salesman, pin packing, and the Knapsack problems. Local search techniques have been successful in solving large and tight constraint satisfaction problems. Local search algorithms turn out to be effective in solving many constraint satisfaction problems. This chapter gives an introduction to the local search algorithms, the optimization and the constraint satisfaction problems, and the local search methods used to solve them.