A tabu search based on simulation of adaptive fish behavior and its application to flow shop scheduling problems

Tabu search (TS) is a stochastic global optimization algorithm, which has been proved to be efficient in solving various combinatorial optimization problems. In this paper a TS with a new technique restricting the neighbors of the current solution is proposed to solve flow shop scheduling problems. An idea obtained from a simulation study on the adaptive behavior of a fish school is used to determine a parameter necessary for neighborhood construction. In order to obtain a better suboptimal solution in a reasonable computation time, the parameter is determined depending on the characteristics of the job data. The validity of the proposed TS is examined through computer experiments. Comparison of the result is made successfully among the proposed TS, the genetic algorithm, and another TS proposed in our earlier paper.