An application of swarm optimization to nonlinear programming

✍ Scribed by Ying Dong; Jiafu Tang; Baodong Xu; Dingwei Wang

• Publisher: Elsevier Science
• Year: 2005
• Tongue: English
• Weight: 972 KB
• Volume: 49
• Category: Article
• ISSN: 0898-1221
• DOI: 10.1016/j.camwa.2005.02.006

✦ Synopsis

Particle swarm optimization (PSO) m an optimzzatlon techmque based on population, whmh has mmzlaritms to other evolutionary algorithms It is initialized with a population of random solutions and searches for optima by updating generations Partmle swarm optimization has become the hotspot of evolutionary computation because of Its excellent performance and szmple implementation. After introducing the basra principle of the PSO, a particle swarm optimization algorithm embedded with constraint fitness priority-based ranking method zs proposed m this paper to solve nonlinear programming problem By designmg the fitness function and constraints-handling method, the proposed PSO can evolve with a dynamic neighborhood and varied inertia weighted value to find the global optzmum The results from thzs prehminary mvestlgatmn are qmte promising and show that this algorithm zs reliable and apphcable to almost all of the problems in multiple-dimensional, nonhnear and complex constrained programming. It is proved to be efficient and robust by testing some example and benchmarks of the constrained nonhnear programming problems.