A Comparison of Diverse Approaches to Memetic Multiobjective Combinatorial Optimization

Memetic algorithms (MAs) are, at present, amongst the most successful approximate methods for combinatorial optimization. Recently , their range of application in this domain has been extended, with the introduction of several MAs for problems possessing multiple objectives. In this paper, we consider two of the newest of these MAs, the random directions multiple objective genetic local searcher (RD-MOGLS) of Jaszkiewicz, and the memetic Pareto archived evolution strategy (M-PAES), recently introduced by us. The two algorithms work in diierent ways: M-PAES employs a form of Pareto ranking in its selection mechanism, as used in several multiobjective evolutionary algorithms (MOEAs); whereas RD-MOGLS uses randomly weighted utility functions to judge solution quality, drawing from multiobjec-tive tabu search and simulated annealing approaches. These two diierent approaches to memetic multiobjective optimization are brieey described, and their possible strengths and weaknesses identiied. Finally, the two algorithms are applied to the multiobjective 0/1 knapsack problem. Their performance is compared on nine instances of the problem , using statistical methods developed previously .