In this paper we propose a general variable neighborhood search approach for the balanced location problem. Next to large shaking neighborhoods, the embedded variable neighborhood descent utilizes three neighborhood structures that focus on different solution aspects. By a computational study, we show that this VNS outperforms existing methods with respect to average solution quality and stabil...

This paper presents the investigation of an evolutionary multi-objective simulated annealing algorithm with variable neighborhoods to solve the multi-objective multicast routing problems in telecommunications. The hybrid algorithm aims to carry out a more flexible and adaptive exploration in the complex search space by using features of the variable neighborhood search to find more non-dominate...

In this paper, a capacitated vehicle routing problem is discussed which occurs in the context of glass waste collection. Supplies of several different product types (glass of different colors) are available at customer locations. The supplies have to be picked up at their locations and moved to a central depot at minimum cost. Different product types may be transported on the same vehicle, howe...

In this paper, we propose a new genetic algorithm for multi-objective optimization problems. That is called “Neighborhood Cultivation Genetic Algorithm (NCGA)”. NCGA includes the mechanisms of other methods such as SPEA2 and NSGA-II. Moreover, NCGA has the mechanism of neighborhood crossover. Because of the neighborhood crossover, the effective search can be performed and good results can be de...

This paper presents a very large scale neighborhood (VLSN) search algorithm for the capacitated facility location problem with single-source constraints. The neighborhood structures are induced by customer multi-exchanges and by facility moves. We consider both single-customer multi-exchanges, detected on a suitably de ned customer improvement graph, and multi-customer multi-exchanges, detected...

Medical data sets consist of a huge amount of data organized in instances, where each one contains several attributes. The quality of the models obtained from a database strongly depends on the information previously stored on it. For this reason, these data sets must be preprocessed in order to have fairly information about patients. Data sets are preprocessed reducing the amount of data. For ...