New insertion and postoptimization procedures for the traveling salesman problem
Operations Research
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Evolutionary Algorithms for Solving Multi-Objective Problems
Evolutionary Algorithms for Solving Multi-Objective Problems
A Taxonomy of Hybrid Metaheuristics
Journal of Heuristics
The bi-objective covering tour problem
Computers and Operations Research
PISA: a platform and programming language independent interface for search algorithms
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
ParadisEO-MOEO: a framework for evolutionary multi-objective optimization
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
Performance assessment of multiobjective optimizers: an analysis and review
IEEE Transactions on Evolutionary Computation
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This paper discusses a new hybrid solution method for a bi-objective routing problem, namely the bi-objective ring star problem. The bi-objective ring star problem is a generalization of the ring star problem in which the assignment cost has been dissociated from the cost of visiting a subset of nodes. Here, we investigate the possible contribution of incorporating specialized TSP heuristics into a multi-objective evolutionary algorithm. Experiments show that the use of this hybridization scheme allows a strict improvement of the generated sets of non-dominated solutions.