A multiplier adjustment method for the generalized assignment problem
Management Science
Genetic algorithms and tabu search: hybrids for optimization
Computers and Operations Research - Special issue on genetic algorithms
A genetic algorithm for the generalised assignment problem
Computers and Operations Research
Intensification and diversification with elite tabu search solutions for the linear ordering problem
Computers and Operations Research
A dynamic tabu search for large-scale generalised assignment problems
Computers and Operations Research
A Template for Scatter Search and Path Relinking
AE '97 Selected Papers from the Third European Conference on Artificial Evolution
Multiobjective Landscape Analysis and the Generalized Assignment Problem
Learning and Intelligent Optimization
Plateau connection structure and multiobjective metaheuristic performance
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Improvement in the performance of island based genetic algorithms through path relinking
HM'06 Proceedings of the Third international conference on Hybrid Metaheuristics
Expert Systems with Applications: An International Journal
Statistical analysis of distance-based path relinking for the capacitated vehicle routing problem
Computers and Operations Research
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The Generalized Assignment Problem (GAP) consists in finding a maximum-profit assignment of tasks to agents with capacity constraints. In this paper, a path relinking heuristic is proposed for the GAP. The main feature of our path relinking is that both feasible and infeasible solutions are inserted in the reference set of elite solutions, trade-off between feasibility and infeasibility being ruled through a penalty coefficient for infeasibility. Since exploration of the solution space is very sensitive to this penalty coefficient, the coefficient is dynamically updated at each round of combinations so that a balance is kept between feasible and infeasible solutions in the reference set. Numerical experiments reported on classical testbed instances of the OR-library show that the algorithm compares favorably to several other methods in the literature. In particular, more than 95% of the instances in the test-file were solved to optimality with short computation time.