Tabu Search: A Comparative Study

Abstract

Problems encountered in fields like scheduling, assignment, vehicle routing are mostly NPhard. These problems need efficient solution procedures. If confronted with an NP-hard problem, one may have three ways to go: one chooses to apply an enumerative method that yields an optimum solution, or apply an approximation algorithm that runs in polynomial time, or one resorts to some type of heuristic technique without any a priori guarantee for quality of solution and time of computing (Aarts & Lenstra, 2003). Heuristics fall under the general heading of local search approaches. Hence, local search techniques are widely used to find “close-to-optimum” solutions to these problems in a “reasonable” amount of time. Tabu search (TS) is one of the most efficient heuristic techniques in the sense that it finds quality solutions in relatively short running time. This chapter will provide a basic description of TS giving insights for novice readers as well as introduce application areas and provide comparisons of TS to other meta-heuristic procedures for the readers with more experience on local search procedures. The chapter will be organized as follows: The second section is going to introduce the basic terminology. For example, definitions for global optimization, local search, heuristics, and meta-heuristics will be provided. The section will also provide brief descriptions of TS as well as the following meta-heuristics to which TS will be compared: simulated annealing (SA), genetic algorithms (GA), ant colony optimization (ACO), greedy randomized adaptive search procedure (GRASP), and particle swarm optimization (PSO). Second section is intended to give the readers a good overall view of the “local search” area and let them know that TS will be compared to several other meta-heuristic procedures. In the third section, basic steps of TS, SA, GA, ACO, GRASP and PSO will be described. As the mechanisms of these procedures are explained, differences and similarities between TS and each of the other procedures will be pointed out. Section three will familiarize the readers with the various meta-heuristic procedures that will be discussed throughout the chapter. The fourth section will be dedicated to identifying the different problems for which TS was used to generate solutions. For example; TS has been used to solve scheduling problems, routing problems, and assignment problems. We will try to generate a comprehensive list of the problems to which TS has been applied. This section will provide the reader with an understanding of how TS has been used. In the fifth section, efficiency and effectiveness of TS will be compared to other metaheuristic procedures. Reasons why TS is more efficient and/or effective than some of the O pe n A cc es s D at ab as e w w w .ite ch on lin e. co m