Neural Network Enhancement of Multiobjective Evolutionary Search
Haluk Yapıcıoğlu, Gerry Vernon Dozier, Alice E. Smith · 2006
In this study, a novel approach is used to identify nondominated solutions to multiobjective optimization problems. The method is composed of a Particle Swarm Optimizer (PSO) coupled with a neural network. The PSO is used to find an initial set of nondominated solutions. These nondominated solutions are then used to construct a general regression neural network that generates a considerably larger set of nondominated solutions. Our neural network enhancement process is demonstrated on a test suite of six instances of bi-criteria semidesirable facility location problems. Results show that the set of nondominated solutions developed by the neural network is, on average, 25 times larger than the initial set found by PSO, and in many instances dominate those identified by PSO. The method developed within is straightforward and general and is a new alternative to multiobjective optimization with decision variables in continuous space.