Multipoint-based tabu search using proximate optimality principle

This paper presents a new method for combinatorial optimization problems. Most of the actual problems that have discrete structure can be formulated as combinatorial optimization problems. It is experientially known that proximate optimality principle (POP) holds in most of the actual combinatorial optimization problems. The concept of proximate optimality principle says that good solutions of most real combinatorial optimization problems have the structural similarity in parts of solution. In this paper, we propose a new optimization method based on tabu search. In the proposed algorithm, POP is taken into consideration. The proposed algorithm is applied to some knapsack problems and traveling salesman problems, which are typical combinatorial optimization problems in order to verify the performance of the proposed algorithm.