Andrzej Jaszkiewicz

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The paper presents a new genetic local search algorithm for multi-objective combinatorial optimization. The goal of the algorithm is to generate in a short time a set of approximately efficient solutions that will allow the decision maker to choose a good compromise solution. In each iteration, the algorithm draws at random a utility function and constructs(More)
The growing interest in hard multiple objective combinatorial and non-linear problems resulted in a significant number of heuristic methods aiming at generating sets of feasible solutions as approximations to the set of non-dominated solutions. The issue of evaluating these approximations is addressed. Such evaluations are useful when performing(More)
Multiple-objective metaheuristics, e.g., multiple-objective evolutionary algorithms, constitute one of the most active fields of multiple-objective optimization. Since 1985, a significant number of different methods have been proposed. However, only few comparative studies of the methods were performed on largescale problems. In this paper, we continue two(More)
This chapter describes various approaches to the use of evolutionary algorithms and other metaheuristics in interactive multiobjective optimization. We distinguish the traditional approach to interactive analysis with the use of single objective metaheuristics, the semi-a posteriori approach with interactive selection from a set of solutions generated by a(More)
The paper describes a comparative study of multiple-objective metaheuristics on the bi-objective set covering problem. Ten representative methods based on genetic algorithms, multiple start local search, hybrid genetic algorithms and simulated annealing are evaluated in the computational experiment. Nine of the methods are well known from the literature.(More)
The paper presents a comparative experiment with fo ur multiple objective evolutionary algorithms on a real ife co mbinatorial optimization problem. The test problem corresponds to the design of adistribution system. The experiment compares performance of a Pareto ran king based multiple objective genetic algorithm (Pareto GA), multiple o bjective multiple(More)