Title: Analysis of learning-based multi-agent simulated annealing algorithm for function optimisation problems
Authors: Yiwen Zhong; Juan Lin; Hui Yang; Hui Zhang
Addresses: College of Computer and Information Science, Fujian Agriculture and Forestry University, Fuzhou, 350002, China ' College of Computer and Information Science, Fujian Agriculture and Forestry University, Fuzhou, 350002, China ' College of Computer and Information Science, Fujian Agriculture and Forestry University, Fuzhou, 350002, China ' Pervasive Technology Institute, Indiana University Indianapolis, IN 46202, USA
Abstract: Canonical simulated annealing (SA) algorithm is extremely slow in convergence, and the implementation and efficiency of parallel SA algorithms are typically problem-dependent. Multi-agent SA (MSA) algorithms, which use learned knowledge to guide its sampling, can overcome such intrinsic limitations naturally. Learning strategy, which decides the representation, selection, and usage of knowledge, may affect the performance of MSA algorithms significantly. Using the current population as learned knowledge, we design three different knowledge selection schemes, selecting from better agents, selecting from worse agents and selecting from all agents randomly, to select knowledge to guide sampling. A differential perturbation operator is designed to generate candidate solution from the selected knowledge. Comparison was carried on four widely used benchmark functions, and the results show that learning-based MSA algorithm has good performance in terms of convergence speed and solution accuracy. Furthermore, simulation results also show that even learning from worse agents significantly outperforms not learning at all.
Keywords: learning based sampling; knowledge selection schemes; differential perturbation operator; function optimisation; multi-agent systems; MAS; agent-based systems; simulated annealing; simulation.
International Journal of Computing Science and Mathematics, 2013 Vol.4 No.4, pp.382 - 391
Received: 20 May 2013
Accepted: 10 Jul 2013
Published online: 01 Dec 2013 *