This paper deals with a multiobjective combinatorial optimization problem called Extended Knapsack Problem. By applying multi-start search and path relinking we rapidly guide the search toward the most balanced zone of the Pareto-optimal front. The Pareto relation is applied in order to designate a subset of the best generated solutions to be the current efficient set of solutions. The max-min criterion with the Hamming distance is used as a measure of dissimilarity in order to find diverse solutions to be combined. The performance of our approach is compared with several state-of-the-art MOEAs for a suite test problems taken from the literature.
In this paper, a Tabu Search Approach for the weighted tardiness single machine problem with sequence-dependent setups is proposed. The main contribution is the balance obtained between intensification and diversification strategies. The strategy of combine large step optimization, frequency-based memory, intensification by decomposition supplementing this with an additional intensification using path relinking produce good solutions with a low computational cost. Our Tabu Search approach is compared with a re-start method that employs the all-pairs neighborhood. Results of computational experiments are reported for a set of randomly generated test problems.Keywords: Tabu Search, scheduling problems, weighted tardiness, sequence dependsetups.
ResumenEn este artículo, se propone un enfoque basado en Búsqueda Tabú para el problema de una sola máquina, con retardo ponderado, con puestas a punto que dependen de la sucesión. La principal contribución es el balance obtenido entre las estrategias de intensificación y diversificación. La estrategia de combinar amplios pasos de optimización, memoria basada en la frecuencia, intensificación por descomposición con una intensificación adicional que usa religamen de caminos, produce buenas soluciones con un costo computacional bajo. Nuestro enfoque de Búsqueda Tabú es comparado con el método de inicio múltiple que emplea el vecindario de todos los pares. Se reportan resultados de experimentos computacionales para un conjunto de problemas test generados aleatoriamente.
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