Abstract
In this paper, we show that the techniques widely used in multi-objective optimization can help a single-objective local search procedure escape from local optima and find better solutions. The Traveling Salesman Problem (TSP) is selected as a case study. Firstly the original TSP \(f_0\) is decomposed into two TSPs \(f_1\) and \(f_2\) such that \(f_0 = f_1\,+\,f_2\). Then we propose the Non-Dominance Search (NDS) method which applies the non-domination concept on \((f_1,f_2)\) to guide a local search out of the local optima of \(f_0\). In the experimental study, NDS is combined with Iterated Local Search (ILS), a well-known metaheuristic for the TSP. Experimental results on some selected TSPLIB instances show that the proposed NDS can significantly improve the performance of ILS.
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The work described in this paper was supported by the National Natural Science Foundation of China under Grant 61876163.
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Shi, J., Sun, J., Zhang, Q. (2019). Multi-objective Techniques for Single-Objective Local Search: A Case Study on Traveling Salesman Problem. In: Deb, K., et al. Evolutionary Multi-Criterion Optimization. EMO 2019. Lecture Notes in Computer Science(), vol 11411. Springer, Cham. https://doi.org/10.1007/978-3-030-12598-1_10
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