Fitness based particle swarm optimization

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• 作者：Kavita Sharma ; Varsha Chhamunya ; P C Gupta…
• 关键词：Particle swarm optimization ; Artificial Bee Colony ; Swarm intelligence ; Fitness based position updating ; Optimization
• 刊名：International Journal of Systems Assurance Engineering and Management
• 出版年：2015
• 出版时间：September 2015
• 年：2015
• 卷：6
• 期：3
• 页码：319-329
• 全文大小：622 KB
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• 作者单位：Kavita Sharma (1)
  Varsha Chhamunya (2)
  P C Gupta (3)
  Harish Sharma (4)
  Jagdish Chand Bansal (5)
  
  1. Government Polytechnic College, Kota, India
  2. Gurukul Institute of Engineering & Technology, Kota, India
  3. University of Kota, Kota, India
  4. Rajasthan Technical University, Kota, India
  5. South Asian University, New Delhi, India
  
• 刊物类别：Engineering
• 刊物主题：Operating Procedures and Materials Treatment
  Quality Control, Reliability, Safety and Risk
  
• 出版者：Springer India

文摘

Particle swarm optimization (PSO) is a popular population based approach used to solve nonlinear and complex optimization problems. It is simple to implement and swarm based probabilistic algorithm but, it also has drawbacks like it easily falls into local optima and suffers from slow convergence in the later stages. In order to reduce the chance of stagnation, while improving the convergence speed, a new position updating phase is incorporated with PSO, namely fitness based position updating in PSO. The proposed phase is inspired from the onlooker bee phase of Artificial Bee Colony (ABC) algorithm. In the proposed position updating phase, solutions update their positions based on probability which is a function of fitness. This strategy provides more position updating chances to the better solutions in the solution search process. The proposed algorithm is named as fitness based particle swarm optimization (FPSO). To show the efficiency of FPSO, it is compared with standard PSO 2011 and ABC algorithm over 15 well known benchmark problems and three real world engineering optimization problems.