Optimization of Multi-modal Performance Criteria by Learning Automata
Zen-Kwei Hung, Te‐Son Kuo, Sheng‐De Wang · 1992
In 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. From the numerical simulation results, it shows that the automata approach is better than the well known gradient approach because the gradient approach is easy to be trapped into the local optimal states. Theoretically, we prove that the automaton converges to the global optimum with a probability arbitrarily close to 1. The simulation result also shows that our automata model converges faster than the existing models in the literatures.