Cooperative learning of homogeneous and heterogeneous particles in Area Extension PSO

Abstract

Particle Swarm Optimization with Area Extension (AEPSO) is a modified PSO that performs better than basic PSO in static, dynamic, noisy, and real-time environments. This paper investigates the effectiveness of cooperative learning AEPSO in a simulated environment. The environment is a 2D landscape planted with various types of bombs with arbitrary explosion times and locations. The simulated-robots’ task (i.e., swarm particles) is to disarm these bombs. Different bombs must be disarmed with appropriate robots (i.e., disarming skills and bomb types must correspond) and the robots (hereafter, referred to as agents) do not have full observations of the environment due to uncertainties in their perceptions. In this study, each agent has the ability to disarm different type of bombs in heterogeneous scenario while each agent has the ability to disarm all types of bombs in homogeneous scenario. We found that AEPSO shows reliable performance in both heterogeneous and homogeneous scenarios as compared to the basic PSO. We also found that the proposed cooperative learning is robust in environment where agents’ perception are distorted with noise.