Smart Particle Swarm Optimization

This paper proposes Smart Particle Optimization (SPSO) which is fusion of Supervisor-Student Model in Particle Swarm Optimization (SSMPSO) and Emotional PSO (EPSO) known as Smart Particle Optimization (SPSO). SPSO is used to optimize unimodal as well as multimodal function. In Smart Particle Optimization, particle position and direction are updated based on emotions of EPSO such as sad and joyful. The joyful particle always hold better optima and it will try to improve accuracy to optima while sad particle does not have better optima and it will change its current position to achieve better optima than previous one. At each generation, momentum factor of SSMPSO pull up the particles in initialized search area to avoid infeasible solutions. The momentum factor is used to reduce computational cost. Three standard functions are used to validate the efficiency SPSO. Its perforamce is compared with PSO variants such as SSMPSO and LDWPSO. SPSO evaluates accurate resonant frequency of rectangular microstrip patch antenna of various dimensions as compared to other compared techniques. The results of benchmark functions and rectangular microstrip patch antenna prove that SPSO is more efficient optimization technique suitable for any application.