Abstract
Multi-objective Ant Colony Optimization (MOACO) is a popular algorithm in solving the multi-objective combinational optimization problem. Many variants were introduced to solve different types of multi-objective optimization problem. However, MOACO does not guarantee to generate a good approximation of solution in a predefined termination time. In this paper, Two-Phase Local Search (TPLS) was introduced to cooperate with the MOACO, a two-phase strategy that creates a very good approximation of Pareto Front at the beginning of the algorithm and then further explores the Pareto Front iteratively. We also propose Iterated Local Search – Variable Neighborhood Search (ILS-VNS) as the first phase in TPLS, an iterative improvement process that allows finding improving solutions from adaptively sized neighborhood space. A series of experiments were performed to investigate the performance improvement on the solutions. At the same time, we studied the effect of Weighted Local Search (WLS) and Pareto Local Search (PLS) in the proposed algorithm. The results showed that the newly proposed algorithm obtains a larger area on hypervolume space and exhibits a significantly larger accuracy rate in obtaining the true Pareto-optimal solutions. Additionally, a statistical testing was also performed to verify the significance of the result.
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Leung, CW., Ng, SC., Lui, A.K. (2018). Combining Two-Phase Local Search with Multi-objective Ant Colony Optimization. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11302. Springer, Cham. https://doi.org/10.1007/978-3-030-04179-3_50
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