Abstract
This paper proposes a method of adaptive simulated annealing that can effectively be used for solving combinational optimization problems. This method is based on simulated annealing and performs an adaptive search but without knowledge base. It recognizes characteristics of a problem from search history and adaptively changes its search strategies. The method thus performs search in the neighborhood not randomly but by making use of the search history. This method is applied to an optimal allocation problem of irregular shapes. This problem arises in many industrial production processes, e.g., sheet metal process and cloth cutting process. The problem is known to be very difficult, since many irregular shapes must be handled. A new algorithm for locating irregular shapes is proposed, which is based on people's way of thinking in placing such shapes. Simulation examples show that this algorithm provides natural and good locations for the shapes. The adaptive simulated annealing and the other meta-heuristics, i.e., local search, simulated annealing, genetic algorithm and tabu search, are compared using simulation experiments. The result shows that the adaptive simulated annealing is better than the other mesa-heuristics.
