Authors: Yiwen Zhong; Jing Ning; 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. ' Pervasive Technology Institute, Indiana University Purdue University Indianapolis, Indianapolis, IN 46202, USA
Abstract: Simulated annealing (SA) algorithm is extremely slow in convergence, and the implementation and efficiency of parallel SA algorithms are typically problem-dependent. To overcome such intrinsic limitations, this paper presents a multi-agent SA (MSA) algorithm to address continuous function optimisation problems. In MSA, a population of agents run SA algorithm collaboratively, exploiting the velocity and position update formulas of particle swarm optimisation (PSO) algorithm for candidate solution generation. Our MSA algorithm can achieve significantly better intensification ability by taking advantage of the learning ability from PSO algorithm, meanwhile opposite velocity is introduced to keep MSA from premature stagnation. The MSA algorithm is population based, so it can be paralleled easily. Simulation experiments were carried on four benchmark functions, and the results show that MSA algorithm has good performance in terms of convergence speed and solution accuracy.
Keywords: multi-agent simulated annealing; MSA; particle swarm optimisation; PSO; continuous function optimisation; opposite velocity; multi-agent systems; agent-based systems.
International Journal of Computer Applications in Technology, 2012 Vol.43 No.4, pp.335 - 342
Received: 08 May 2021
Accepted: 12 May 2021
Published online: 31 May 2012 *