Improved Particle Swarm Optimization for Real World Problems

Particle Swarm optimization (PSO) is one of the widely used and effective optimization techniques to address real-world problems. This paper presents a hybrid PSO-BBO algorithm that incorporates BBO's random mutation operator within PSO's framework to promote exploration in constrained search domains. In contrast to other hybrids, the approach natively addresses both linear and non-linear constraint handling through Deb's feasibility rules, with the benefit of an improved exploration-exploitation trade-off. Its efficiency is illustrated on real-world inspired problems to yield higherquality and robust solutions compared to PSO and LX-BBO.