Using an outward selective pressure for improving the search quality of the MOEA/D algorithm

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.