Particle Swarm Optimization Applications to Mechanical Engineering- A Review

被引:62
作者
Kulkarni, Ninad K. [1 ]
Patekar, Sujata [1 ]
Bhoskar, Trupti [1 ]
Kulkarni, Omkar [1 ]
Kakandikar, G. M. [2 ]
Nandedkar, V. M. [3 ]
机构
[1] Dnyanganga Coll Engn & Res, Mech Engn, Pune 41, Maharashtra, India
[2] Savitribai Phule Pune Univ, Dnyanganga Coll Engn & Res, Mech Engn, Pune 41, Maharashtra, India
[3] Shri Guru Gobind Singhji Inst Engn & Technol, Prod Engn, Nanded, Maharashtra, India
关键词
Particle Swarm Optimization; Variants; Applications;
D O I
10.1016/j.matpr.2015.07.223
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
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. (C) 2015 Elsevier Ltd. All rights reserved.
引用
收藏
页码:2631 / 2639
页数:9
相关论文
共 30 条
[1]  
Anghinolfi Davide, 2007, 8 IA TABOO JOINT WOR
[2]  
Chakraborty P, 2010, IEEE C EVOL COMPUTAT, P1
[3]  
Chen W., 2013, RES J APPL SCI ENG T, V6, P2316
[4]   Multi-objective particle swarm optimization of binary geothermal power plants [J].
Clarke, Joshua ;
McLeskey, James T., Jr. .
APPLIED ENERGY, 2015, 138 :302-314
[5]  
Cus F., 2007, Journal of Achievements in Materials and Manufacturing Engineering, V22, P75
[6]  
Del Valle Y., 2008, IEEE T EVOLUTIONARY, V12
[7]  
Eberhart R., P 6 INT S MICROMACHI, P39, DOI DOI 10.1109/MHS.1995.494215
[8]  
Gao Liang, 2006, SEAL P 6 INT C SIM A, P321
[9]   Particle swarm optimization of a neural network model in a machining process [J].
Garg, Saurabh ;
Patra, Karali ;
Pal, Surjya K. .
SADHANA-ACADEMY PROCEEDINGS IN ENGINEERING SCIENCES, 2014, 39 (03) :533-548
[10]  
Guoa Y.W., 2009, ROBOTICS INTEGRATED, V25, P280