Huang, Zen-KweiZen-KweiHuangSHENG-DE WANGKuo, Te-SonTe-SonKuo2009-02-042018-07-062009-02-042018-07-06199302533839http://ntur.lib.ntu.edu.tw//handle/246246/120747https://www.scopus.com/inward/record.uri?eid=2-s2.0-0027663879&doi=10.1080%2f02533839.1993.9677534&partnerID=40&md5=90e7b89405574f139e4dce9b7dcac8f3In this paper, we consider the multi-modal function optimization problem. An automata model with improved learning schemes is proposed to solve the global optimization problem. Theoretically, we prove that theautomaton converges to the global optimum with a probability arbitrarily closeto 1. The numerical simulation results show that the automata approach isbetter than both the well-known gradient approach and the simulated annealing method. The simulation results also show that our automata model converges faster than the other existing models in the literature. © 1993 Taylor & Francis Group, LLC.en-USLearning automata; Multi-modal optimization; Weak law of large numberApplications; Mathematical models; Numerical methods; Optimization; Simulation; Automata model; Global optimization; Multi modal function optimization; Automata theoryjournal article10.1080/02533839.1993.96775342-s2.0-0027663879