Asynchronous Strategy of Parallel Hybrid Approach of GA and EDA for Function Optimization

Abstract

This paper adapts parallel master-slave estimation of distribution and genetic algorithms (GAs and EDAs) hybridization. The master selects portions of the search space, and slaves perform, in parallel and independently, a GA that solves the problem on the assigned portion of the search space. The master's work is to progressively narrow the areas explored by the slave's GAs, using parallel dynamic K-means clustering to determine the basins of attraction of the search space. Coordination of activities between master and slaves is done in an asynchronous way (i.e. no waiting is entertained among the processes). The proposed asynchronous model has managed to reduce computation time while maintaining the quality of solutions.

Distributed evolutionary algorithms and their models: A survey of the state-of-the-art

“…Most existing master-slave dEAs are synchronous that the master stops and waits to receive the information of all slaves before proceeding to the next generation. Some are asynchronous, where the selection operations on the master node perform on a fraction of the population only [106,107]. In an experimental study of a master-slave PSO algorithm [110], it is shown that synchronization plays a vital role in algorithm performance on load-balanced problems, whilst asynchronous dEAs are more efficient for load-imbalanced problems.…”