Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes

The book documents 25 papers collected from the Special Issue “Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes”, highlighting recent research trends in complex industrial processes. The book aims to stimulate the research field and be of benefit to readers from both academic institutes and industrial sectors

Effects of Communication Range, Noise and Help Request Signal on Particle Swarm Optimization with Area Extension (AEPSO)

Particle Swarm Optimization (PSO) method is an Evolutionary algorithm, which outperformed other evolutionary algorithms, such as; GA. PSO method is inspired by animal's group work and social behaviors. Particle Swarm Optimization with Area Extension (AEPSO) was introduced to solve the weaknesses of Basic PSO in static, dynamic optimization tasks (i.e. a group of robots disarm a set of time bomb placed at random in environment). This paper, investigated the effectiveness of AEPSO in a Real-Time problem with a noisy environment. We also explored the effectiveness of different communication ranges and help request on AEPSO