MATLAB Implementation of Tabu Search Algorithm

Development Background Tabu Search (TS), whose concept was first introduced by Glover in 1986, extends local neighborhood search as a global stepwise optimization algorithm that simulates human cognitive processes. Key Technology The TS algorithm utilizes a flexible memory structure and corresponding tabu criteria to prevent redundant searches, while employing aspiration criteria to override tabu restrictions for high-quality solutions. This mechanism ensures effective diversified exploration leading to global optimization. As a meta-heuristic algorithm, TS demonstrates distinct search characteristics compared to simulated annealing and genetic algorithms. In MATLAB implementation, this typically involves maintaining a tabu list using queue data structures and implementing neighborhood search through systematic solution perturbation. To date, TS has achieved remarkable success in combinatorial optimization, production scheduling, machine learning, circuit design, and neural networks. Recent years have seen increased research on TS for function global optimization, indicating strong development potential. The core principle involves maintaining a tabu list that records recently visited solutions and restricts certain moves under specific conditions to avoid local optima. By incorporating appropriate tabu criteria, the algorithm preserves promising solutions while ensuring exploration diversity. Beyond traditional applications in combinatorial optimization and production scheduling, TS has gained significant attention for function global optimization. Recent research has developed improved algorithms and strategies for function optimization applications, yielding impressive results. The algorithm's implementation typically involves key components: neighborhood generation functions, objective function evaluation, tabu list management, and aspiration criteria checking. Despite substantial achievements across various domains, TS still faces challenges including optimal selection of tabu criteria and tabu length, parameter tuning, and overcoming inherent algorithm limitations. Code implementations often require careful configuration of these parameters through experimental validation. These issues warrant further research to advance TS algorithm development and application.