Multi-agent simulated annealing algorithm based on particle swarm optimisation algorithm

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.

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