Combining Hybrid Metaheuristics and Populations for the Multiobjective Optimisation of Space Allocation Problems

Some recent successful techniques to solve multiobjective optimisation problems are based on variants of evolutionary algorithms and use recombination and self-adaptation to evolve the population. We present an approach that incorporates a population of solutions into a hybrid metaheuristic with no recombination. The population is evolved using self-adaptation, a mutation operator and an information-sharing mechanism. Since the main component in our approach is a simulated annealing algorithm, the cooling schedule for the whole population becomes critical. A common cooling schedule for the whole population is determined based on an evolutionary process. Results are presented using a real-world multiobjective combinatorial optimisation problem, namely space allocation with two conflicting criteria. These results suggest that this approach is a suitable alternative not only for combinatorial multiobjective optimisation problems, but also for obtaining a population of locally optima solutions in singleobjective optimisation problems.