Tabu search performance on the symmetric traveling salesman problem
Computers and Operations Research - Special issue: heuristic, genetic and tabu search
An overview of evolutionary algorithms in multiobjective optimization
Evolutionary Computation
Pareto Simulated Annealing for Fuzzy Multi-Objective Combinatorial Optimization
Journal of Heuristics
A memetic random-key genetic algorithm for a symmetric multi-objective traveling salesman problem
Computers and Industrial Engineering
Design and analysis of stochastic local search for the multiobjective traveling salesman problem
Computers and Operations Research
A two-phase local search for the biobjective traveling salesman problem
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
Finding pareto-optimal set by merging attractors for a bi-objective traveling salesmen problem
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
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Solving the Tchebycheff program means optimizing a particular scalarizing function. When dealing with combinatorial problems, however, it is due to computational intractability often necessary to apply heuristics and settle for approximations to the optimal solution. The experiments in this paper suggest that for the multiobjective traveling salesman problem (moTSP) instances considered, heuristic optimization of the Tchebycheff program gives better results when using a substitute scalarizing function instead of the Tchebycheff based one to guide the local search path. Two families of substitute scalarizing functions are considered.