Combining Hybrid Metaheuristics and Populations for the Multiobjective Optimisation of Space Allocation Problems
Edmund Burke · 2001
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. 1