Adaptive algorithms of metaheuristics with application to optimal allocation problems

Proposes a method of adaptive metaheuristics that is based on a class of neighborhood search, such as the local search, simulated annealing and the tabu search. This method uses an adaptive memory, which reflects search history. Its construction is simple so that the method realizes an easy coding of an optimization problem. This method is applied to a class of optimal allocation problems of allocating irregular shapes onto a sheet so that the waste of the sheet is minimized. This problem is known to NP-complete and therefore is difficult to solve. The experimental results show the effectiveness of the use of this memory in order to find an optimal solution