Abstract-Combining ideas from evolutionary algorithms, decomposition approaches, and Pareto local search, this paper suggests a simple yet efficient memetic algorithm for combinatorial multiobjective optimization problems: memetic algorithm based on decomposition (MOMAD). It decomposes a combinatorial multiobjective problem into a number of single objective optimization problems using an aggregation method. MOMAD evolves three populations: 1) population P L for recording the current solution to each subproblem; 2) population P P for storing starting solutions for Pareto local search; and 3) an external population P E for maintaining all the nondominated solutions found so far during the search. A problem-specific single objective heuristic can be applied to these subproblems to initialize the three populations. At each generation, a Pareto local search method is first applied to search a neighborhood of each solution in P P to update P L and P E . Then a single objective local search is applied to each perturbed solution in P L for improving P L and P E , and reinitializing P P . The procedure is repeated until a stopping condition is met. MOMAD provides a generic hybrid multiobjective algorithmic framework in which problem specific knowledge, well developed single objective local search and heuristics and Pareto local search methods can be hybridized. It is a population based iterative method and thus an anytime algorithm. Extensive experiments have been conducted in this paper to study MOMAD and compare it with some other state-of-theart algorithms on the multiobjective traveling salesman problem and the multiobjective knapsack problem. The experimental results show that our proposed algorithm outperforms or performs similarly to the best so far heuristics on these two problems. Index Terms-Decomposition, local search, multiobjective knapsack problem, multiobjective optimization, multiobjective traveling salesman problem.
Besides, the multiobjective memetic algorithm based on decomposition (MOMAD) proposed by Ke et al. REF is one of the state-of-the-art algorithms for MCOPs.
Hybridization of Decomposition and Local Search for Multiobjective Optimization
IEEE Transactions on Cybernetics
IEEE Transactions on Cybernetics
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