Particle Swarm Optimization Applications to Mechanical Engineering- A Review

Particle swarm optimization (PSO) is a population based stochastic optimization technique developed by Dr. Eberhart and Dr. Kennedy in 1995, inspired by social behaviour of bird flocking or fish schooling. The particle swarm optimization concept consists of, at each time step, changing the velocity of (accelerating) each particle toward its pbest and lbest locations (local version of PSO). In past several years, PSO has been successfully applied in many research and application areas. This paper reviews the applications of PSO algorithm in mechanical domain. The applications of PSO include optimal weight design of a gear train, Simultaneous Optimization of Design and Machining Tolerances, Process Parameter Optimization in Casting, and Machine Scheduling Problem. The paper also describes the improved version of PSO algorithm namely: Hybrid PSO, Multiobjective PSO, Adaptive PSO and Discrete PSO.

[1]  James T. McLeskey,et al.  Multi-objective particle swarm optimization of binary geothermal power plants , 2015 .

[2]  Chunming Yang,et al.  A new particle swarm optimization technique , 2005, 18th International Conference on Systems Engineering (ICSEng'05).

[3]  Salman Mohagheghi,et al.  Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems , 2008, IEEE Transactions on Evolutionary Computation.

[4]  Maryam Yarmohamadi,et al.  Improvement of Robot Path Planning Using Particle Swarm Optimization in Dynamic Environments with Mobile Obstacles and Target , 2011 .

[5]  Yue Shi,et al.  A modified particle swarm optimizer , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[6]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[7]  A. Mileham,et al.  Applications of particle swarm optimisationin integrated process planning and scheduling , 2009 .

[8]  Martin Middendorf,et al.  A hierarchical particle swarm optimizer and its adaptive variant , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[9]  Shu,et al.  Aluminum-zinc alloy squeeze casting technological parameters optimization based on PSO and ANN , 2007 .

[10]  Chaoxing Yan,et al.  Hybrid particle swarm optimization algorithm and its application in nuclear engineering , 2014 .

[11]  Siti Mariyam Shamsuddin,et al.  Particle Swarm Optimization: Technique, System and Challenges , 2011 .

[12]  B. Surekha,et al.  Multi-Objective Optimization of Green Sand Mould System Parameters using Particle Swarm Optimization , 2013 .

[13]  Seyed Ebrahim Vahdat,et al.  Optimization of Bone Implant Selection with Price Analysis , 2013 .

[14]  Ketan Tamboli,et al.  Optimal Design of a Heavy Duty Helical Gear Pair Using Particle Swarm Optimization Technique , 2014 .

[15]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

[16]  U. Natarajan,et al.  Particle Swarm Optimisation of hardness in nickel diamond electro composites , 2009 .

[17]  A. Engelbrecht,et al.  A new locally convergent particle swarm optimiser , 2002, IEEE International Conference on Systems, Man and Cybernetics.

[18]  SAURABH GARG,et al.  Particle swarm optimization of a neural network model in a machining process , 2014 .

[19]  Liang Gao,et al.  Particle Swarm Optimization for Simultaneous Optimization of Design and Machining Tolerances , 2006, SEAL.