A Brief Survey of Self Organized Particle Swarm Optimization

This paper transplants a new variant of PSO i.e. SOPSO( self organizing PSO) for improving the performance of PSO. SOPSO emphasizes the information interactions between the particle-lever and the swarm-lever, and introduce feedback to simulate the function. Through the feedback information, the particles can perceive the swarmlever state and adopt favorable behavior model to modify their behavior, which not only can modify the exploitation and the exploration of the algorithm adaptively, but also can vary the diversity of the swarm and contribute to a global optimum output in the swarm. This paper presents a brief introduction to the new variant of PSO i.e. Self Organizing PSO (SOPSO) which alleviates the premature convergence. The paper aims at providing the difference between SOPSO and original PSO, giving the merits of SOPSO as compared to PSO. An algorithm and mechanism for the working of SOPSO has been presented here. The particles in SOPSO can adjust its moving mode according to the swarm states. The dynamics of the particles search moving in iterations is also analyzed. Keywordsfeedback , information, , Particle swarm optimization, self organized.