Rational swarm for global optimisation. (27th November 2012)

• Record Type: Journal Article
• Authors: Li, Chenyang; Garcia, Alfredo
• Is Part Of: International journal of system control and information processing. Volume 1:Number 1(2012)
• Page Start: 3
• Page End: 28
• Publication Date: 2012-11-27
• Subjects: metaheuristics -- cooperative search -- global optimisation -- swarm intelligence -- bio–inspired computation -- multi–agent systems -- MAS -- agent–based systems -- multiple agents -- simulated annealing -- local search -- rational swarm; System design -- Data processing -- Periodicals; Information technology -- Periodicals; 003.5
• Languages: English
• ISSNs: 1759-9334

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.