Nature-inspired particle mechanics algorithm for multi-objective optimization

Abstract

In many real world optimization problems, several optimization goals have to be considered in parallel. For this reason, there has been a growing interest in multi-objective optimization (MOO) in the past many years. Several new approaches have recently been proposed, which produced very good results. However, existing techniques have solved mainly problems of "low dimension", i.e., with less than 10 optimization objectives. This chapter proposes a new computational algorithm whose design is inspired by particle mechanics in physics. The algorithm is capable of solving MOO problems of high dimensions. There is a deep and useful connection between particle mechanics and high dimensional MOO. This connection exposes new information and provides an unfamiliar perspective on traditional optimization problems and approaches. The alternative of particle mechanics algorithm (PMA) to traditional approaches can deal with a variety of complicated, large scale, high dimensional MOO problems. © 2009 Springer-Verlag Berlin Heidelberg.