Memory control of tabu search with genetic fuzzy systems

This paper introduces a genetic fuzzy system to control short and long term memory of tabu search algorithms. The genetic fuzzy system involves learning of the knowledge base and a rule selection procedure. The aim is to trade-off exploration and exploitation behavior of the search, and to handle high dimensional optimization problems. The genetic fuzzy system approach introduces a high level of autonomy in tabu search algorithms in a systematic and efficient way. An application example using the classic vehicle routing problem with time windows is included to evaluate the genetic fuzzy system performance. Experimental results show that GFS-controlled tabu search improves search trajectory when compared against current genetic and tabu search approaches.