Indonesia Conference Directory

Particle Swarm Optimization (PSO), one of the versatile nature-inspired optimization algorithm, continue to suffer from premature convergence regardless of the rigorous amount of research. Many research had tried to address this issue but often use a complex algorithm which tax on computational time and complexity. This research introduced a novel perturbation method to mitigate premature convergence / to increase exploration while keeping the computational cost at a minimum. The particles memories (i.e the position of personal and global best) are modified by a random multiplier which in turn will -perturb- the particles- velocity. The implementation of this novel perturbation method in early iterations had resulted in 100% success rate in finding global optima in our benchmark tests (Sphere, Schwefel 2.22, Rosenbrock, Rastrigin, and Ackley problem) – whereas the original PSO failed in all benchmark tests – without adding a significant amount of computational complexity and time.