Abstract
In optimization problems it is often necessary to perform an optimization based on more than one objective. The goal of the multiobjective optimization is usually to find an approximation of the Pareto front which contains solutions that represent the best possible trade-offs between the objectives. In a multiobjective scenario it is important to both improve the solutions in terms of the objectives and to find a wide variety of available options. Evolutionary algorithms are often used for multiobjective optimization because they maintain a population of individuals which directly represent a set of solutions of the optimization problem. multiobjective evolutionary algorithm based on decomposition (MOEA/D) is one of the most effective multiobjective algorithms currently used in the literature. This paper investigates several methods which increase the selective pressure to the outside of the Pareto front in the case of the MOEA/D algorithm. Experiments show that by applying greater selective pressure in the outwards direction the quality of results obtained by the algorithm can be increased. The proposed methods were tested on nine test instances with complicated Pareto sets. In the tests the new methods outperformed the original MOEA/D algorithm.
Highlights
One of the steps of a decision making process is finding optimal solutions of various optimization problems
The multiobjective evolutionary algorithm based on decomposition (MOEA/D) was proposed by Zhang and Li [27]
The investigated methods change the way in which weight vectors affect the working of the multiobjective evolutionary algorithm based on decomposition (MOEA/D)
Summary
One of the steps of a decision making process is finding optimal solutions of various optimization problems. Contrary to the algorithms that base their selection process on the Pareto domination relation the MOEA/D algorithm is a decompositionbased algorithm In this algorithm the multiobjective optimization problem is decom-. It works at a different level than the approach proposed in this paper which modifies the working of the MOEA/D algorithm itself Hybrid approaches, such as described in papers [1,14,16,26] could use the weight generation scheme presented in this paper instead of the original one. The authors of the MOEA/D algorithm proposed the following way of generating weight vectors [27]. Concepts involved in the working of the MOEA/D algorithm are presented in Fig. 1 which shows the population (black dots) and optimization directions determined by the weight vectors (arrows) for a bi-objective case
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