Thermal generation scheduling strategy using binary clustered particle swarm optimization algorithm

This paper presents a multi-population binary clustered particle swarm optimization (BCPSO) algorithm to solve short term thermal generation scheduling problem. The potential solution schedules are distributed among several clusters based on their corresponding fitness values. Each cluster contains a cluster best schedule. Each solution of a particular population then flies through to its cluster space towards the cluster best as well as personal best solution instead of only global best (unlike trivial PSO). Therefore, this algorithm provides a way to explore larger search space and thus reduces the probability of local trapping. An intelligent mutation operator is applied on the solutions of the highest fitted cluster for a particular population. This mutation rate is adjusted dynamically based on the population number to reduce solution disturbance. Simulation result is provided to show the effectiveness of BCPSO.