Abstract
In this paper, we propose a novel bio–inspired multi–agent co–operative searching methodology for global optimisation, named Rational Swarm algorithm. It can be used both as a meta–heuristic guiding local search algorithm and as a high–level multi–agent co–operative searching strategy to coordinate multiple agents using meta–heuristics. In this work, the Rational Swarm methodology has been applied to a popular meta–heuristics Simulated Annealing (SA) and a pure local search algorithm Monotonic Sequential Basin Hopping (MSBH). Numerical experiments on various continuous optimisation problems show Rational Swarm can improve the performance of applied meta–heuristics/heuristics in terms of solution quality and robustness under the same computational budget. Convergence analysis gives the theoretical insights about why the proposed Rational Swarm Methodology will work.
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 callMore From: International Journal of System Control and Information Processing
Disclaimer: 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.
