Decentralized Pursuit Learning Automata in Batch Mode

Learning Automata (LA) and Genetic Algorithms (GA) have been used for a long time to solve problems in different domains. However, there is criticism that LA has slow rate of convergence and both LA and GA have the problem of getting stuck in local optima. In this paper we tried to solve the multi-objective problems using LA in batch mode to make the learning faster and more accurate. We used Decentralized pursuit learning automaton as LA and NSGA2 as GA. Problems where evaluation of fitness function is a bottleneck like SWAT, evaluation of individuals in parallel can give considerable speed-up. In the multi-objective LA, different weight pairs and individual designs can be evaluated independently. So we created their parallel versions to make them practically faster in learning and computations and extended the parallelization concept with the batch mode learning.