On the computational efficiency of multiple objective metaheuristics. The knapsack problem case study

Article Abstract:

On the computational efficiency of multiple objective metaheuristics. The knapsack problem case studyAuthor: Andrzej JaszkiewiczBP: 418EP: 433NP: 16 The success of metaheuristics in the single objective optimization necessitated researchers in proposing the use of metaheuristics in multiple objective optimization (MOO). The multiple objective multiple constraints knapsack problem (MOMCKP) is a standard test problem for multiple objective metaheuristics (MOMHs). A computational experiment is presented to examine computational efficiency of three multiple objective evolutionary metaheuristics on the multiple constraints knapsack problem. The results indicate that computational efficiency of multiple objective evolutionary algorithm (EA) deteriorates with the growth of the number of objectives.

Europe, Mathematical optimization, Optimization theory, Mathematical programming

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A variable neighborhood search for graph coloring

Article Abstract:

An adaptation of the variable neighborhood method to the graph coloring problem is proposed. This algorithm benefits from the advantages of both the genetic algorithms and the local search techniques.

Graph theory

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Genetic local search with distance preserving recombination operator for a vehicle routing problem

Article Abstract:

A genetic local search algorithm has been applied to a real life vehicle routing problem. The results obtained are used to construct an appropriate distance preserving recombination operator.

Genetic algorithms

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