Improving Local Optima of Local Search with Adjustable Multipliers

In this paper, we give two improved algorithms of Guided Local Search (GLS) to improve the local optima of local search. In the GLS-like algorithm, a new penalty principle is proposed to further improve the effectiveness of GLS. The Objective function Adjustment (OA) algorithm is an improved algorithm of GLS-like using multipliers which can be adjusted during the search process. The simulation results based on some TSPLIB benchmark problems showed that the OA algorithm could find better solutions than the local search, guided local search, Tabu Search and GLS-like.