Function Optimization via a Continuous Action-Set Reinforcement Learning Automata Model

Abstract

Learning automata as a tool for machine learning, could search the optimal state adaptively in random environment. Function optimization is a fundamental issue and many practical models are ultimately the mathematical optimization problems. In this paper, we apply the basic continuous action-set reinforcement learning automata (CARLA) model to function optimization. An application model called equiCARLA is constructed by means of equidistant discretization and linear interpolation, and it presents a superiority over the existing algorithms not only in speed but also in precision. The experimental results demonstrate the effectiveness and efficiency of our model for function optimization.

Particle Swarm Optimization for Continuous Function Optimization Problems

“…There are abundant test functions used in the literature to measure the proposed algorithms' performance by the scientists. Many heuristic methods and techniques are proposed and applied for solving these continuous functions like discrete filled function [3], dynamic random search technique [4], ant colony optimization [5], a heuristic random optimization [6], an adaptive random search technique [7], random selection walk [8], fruit fly optimization [9], biogeography-based optimization algorithm [10], flower pollination algorithm [11], continuous action-set reinforcement learning automata model [12], artificial bee colony algorithm [13], genetic algorithm [14] respectively. PSO is proved to be successful approach to solve continuous optimization problems.…”