Hybrid Genetic Algorithm and Simulated Annealing (HGASA) in Global Function Optimization

Authors:
Dingjun Chen;Chung-Yeol Lee;Cheol Hoon Park
Affiliations:
Korean Advanced Institute of Science and Technology;Korean Advanced Institute of Science and Technology;Korean Advanced Institute of Science and Technology
Venue:
ICTAI '05 Proceedings of the 17th IEEE International Conference on Tools with Artificial Intelligence
Year:
2005

Citing 0
Cited 3

Parallel scalable hardware implementation of asynchronous discrete particle swarm optimization

Engineering Applications of Artificial Intelligence
A distributed hybrid heuristics of mean field annealing and genetic algorithm for load balancing problem

RSCTC'06 Proceedings of the 5th international conference on Rough Sets and Current Trends in Computing
A hybrid genetic algorithm for the discrete time-cost trade-off problem

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

We have implemented the sequential HGASA on a Sun Workstation machine; its performance seems to be very good in finding the global optimum of a sample function optimization problem as compared with some sequential optimization algorithms that offer low efficiency and limited reliability. However, the sequential HGASA generally needs a long run time cost. So we implemented a parallel HGASA using Message Passing Interface (MPI) on a high performance computer and performed many tests using a set of frequently used function optimization problems. The performance analysis of this parallel approach has been done on IBM Beowulf PCs Cluster in terms of program execution time, relative speed up and efficiency.