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 simulated annealing (MSA) algorithm to address continuous function optimisation problems. In MSA, a population of agents run SA algorithm collaboratively, exploiting the mutation operator formulas of differential evolution (DE) algorithm for candidate solution generation. Our MSA algorithm can achieve significantly better intensification ability by taking advantage of the learning ability from DE algorithm; meanwhile the probability acceptation rule of SA algorithm can keep MSA from premature stagnation. The MSA algorithm is population based, so it can be paralleled problem-independently and easily. Simulation experiments were carried on four typical benchmark functions, and the results show that MSA algorithm has good performance in terms of convergence speed and solution accuracy.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
