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Multiobjective Combinatorial Optimization by Using Decomposition and Ant Colony

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Executive Summary

Combining Ant Colony Optimization (ACO) and Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D), this paper proposes a multi-objective evolutionary algorithm, MOEA/D-ACO. Following other MOEA/D-like algorithms, MOEA/D-ACO decomposes a multi-objective optimization problem into a number of single objective optimization problems. Each ant (i.e., agent) is responsible for solving one sub-problem. All the ants are divided into a few groups and each ant has several neighboring ants. An ant group maintains a pheromone matrix and an individual ant has a heuristic information matrix. During the search, each ant also records the best solution found so far for its sub-problem. To construct a new solution, an ant combines information from its group's pheromone matrix, its own heuristic information matrix and its current solution.

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