免疫機構を導入したシミュレーテッドアニーリングの二次割当問題への応用

抄録

Simulated Annealing (SA) is known as one of useful heuristic optimization techniques. However the performance of standard SA depends on an initial state at starting temperature because of one-point search of SA. In this paper, to overcome the dependency on the initial state, we propose Immunity-based SA (ISA) that has some features of immune system, combining clonal selection, immunology memory, and idiotypic network with Multipath SA (MSA) which searches the solution space for optimal solutions in parallel starting from many initial states. ISA is expected to improve the local and global search ability and maintain the diversity of the population. We demonstrate the efficiency of ISA by applying it to the Quadratic Assignment Problems. Experimental results show that ISA performs better than SA and MSA.