A Meta-Parallel Evolutionary System for Solving Optimization Problems

Abstract

The purpose of the Meta-Parallel Evolutionary System (MPES) is to develop fast, efficient parallel evolutionary systems for function optimization. Given an optimization problem and a set number of nodes available for the computation, the MPES searches for a strong, potentially heterogeneous combination of evolutionary algorithms to coordinate in order to effectively solve a problem. The Evolutionary Algorithms that are utilized in the parallel system are a Particle Swarm Optimizer (PSO), a variety of Genetic Algorithms (GAs), and an Evolutionary Hill-Climber Algorithm (EHC). The subpopulations communicate with each other via a centralized buffer. At a higher level exists the MPES, which uses evolutionary methods in order to discover parameters for effective parallel systems. This methodology provides an immediate benefit in the form of a strong tool to solve the optimization problem. Further, it provides a long-term benefit by identifying a system that has the potential to effectively solve other difficult optimization problems with a similar search space.