Parallel hybrid particle swarm optimization and applications in geotechnical engineering.

Abstract

A novel parallel hybrid particle swarm optimization algorithm named hmPSO is presented. The new algorithm combines particle swarm optimization (PSO) with a local search method which aims to accelerate the rate of convergence. The PSO provides initial guesses to the local search method and the local search accelerates PSO with its solutions. The hybrid global optimization algorithm adjusts its searching space through the local search results. Parallelization is based on the client-server model, which is ideal for asynchronous distributed computations. The server, the center of data exchange, manages requests and coordinates the time-consuming objective function computations undertaken by individual clients which locate in separate processors. A case study in geotechnical engineering demonstrates the effectiveness and efficiency of the proposed algorithm.