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Tabu Search Algorithms for Multimodal and Multi-Objective Function Optimizations


Masakazu Takahashi, Setsuya Kurahashi


Vol. 7  No. 10  pp. 257-264


The integration of genetic algorithms (GAs) and tabu search is one of the traditional problems in function optimization in the GA literature. However, most of the proposed methods have utilized genetic algorithms to explore global candidates and tabu search to exploit local optimal points. Unlike such methods so far, this paper proposes a new algorithm to directly store individuals into multiple tabu lists during GA-iterations. The tabu lists inhibit similar solution candidates from being selected so often. The proposed algorithm is so simple but strong that we can solve both multimodal and multi-objective problems in the same manner. This paper describes the basic idea, algorithms, and experimental results. One of the applications for this algorithm will be suitable for the recommendation of the grocery items among the retail businesses.


Generic Algorithms, Tabu Search, Multi-modal Function Optimization, Multi-Objective Problems, Social System Simulation