A review on constraint handling strategies in particle swarm optimisation

Almost all real-world optimisation problems are constrained. Solving constrained problems is difficult for optimisation techniques. In this paper, different constraint handling strategies used in heuristic optimisation algorithms and especially particle swarm optimisation (PSO) are reviewed. Since PSO is a very common optimisation algorithm, this paper can provide a broad view to researchers in related field and help them to identify the appropriate constraint handling strategy for their own optimisation problem.

[1]  Min Kong,et al.  A Novel Particle Swarm Optimization for Constrained Optimization Problems , 2005, Australian Conference on Artificial Intelligence.

[2]  A. Abdulwhab,et al.  Constrained non-linear optimization by modified particle swarm optimization , 2007, 2007 10th international conference on computer and information technology.

[3]  A. Rezaee Jordehi,et al.  Particle swarm optimisation for discrete optimisation problems: a review , 2012, Artificial Intelligence Review.

[4]  A. Rezaee Jordehi,et al.  A comprehensive review on methods for solving FACTS optimization problem in power systems. , 2011 .

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

[6]  Jeng-Shyang Pan,et al.  An Improved Particle Swarm Optimization with Feasibility-Based Rules for Constrained Optimization Problems , 2009, IEA/AIE.

[7]  Xinghuo Yu,et al.  Power generation loading optimization using a multi-objective constraint-handling method via PSO algorithm , 2008, 2008 6th IEEE International Conference on Industrial Informatics.

[8]  A. Rezaee Jordehi,et al.  Enhanced leader PSO (ELPSO): A new algorithm for allocating distributed TCSC's in power systems , 2015 .

[9]  Chen Peng,et al.  Solving Constrained Optimization via Dual Particle Swarm Optimization with Stochastic Ranking , 2008, 2008 International Conference on Computer Science and Software Engineering.

[10]  A. Rezaee Jordehi,et al.  Optimal setting of TCSCs in power systems using teaching–learning-based optimisation algorithm , 2014, Neural Computing and Applications.

[11]  Ali R. Yildiz,et al.  Cuckoo search algorithm for the selection of optimal machining parameters in milling operations , 2012, The International Journal of Advanced Manufacturing Technology.

[12]  Morteza Alinia Ahandani,et al.  Opposition-based learning in shuffled frog leaping: An application for parameter identification , 2015, Inf. Sci..

[13]  Nan Li,et al.  Training support vector data descriptors using converging linear particle swarm optimization , 2012, Neural Computing and Applications.

[14]  Mauro Birattari,et al.  Swarm Intelligence , 2012, Lecture Notes in Computer Science.

[15]  Gary G. Yen,et al.  Solving constrained optimization using multiple swarm cultural PSO with inter-swarm communication , 2010, IEEE Congress on Evolutionary Computation.

[16]  A. Rezaee Jordehi,et al.  Parameter selection in particle swarm optimisation: a survey , 2013, J. Exp. Theor. Artif. Intell..

[17]  J. Jasni,et al.  Approaches for FACTS optimization problem in power systems , 2012, 2012 IEEE International Power Engineering and Optimization Conference Melaka, Malaysia.

[18]  Singiresu S. Rao.  Engineering Optimization : Theory and Practice , 2010 .

[19]  P. Eberhard,et al.  Using Augmented Lagrangian Particle Swarm Optimization for Constrained Problems in Engineering , 2009 .

[20]  Leandro dos Santos Coelho,et al.  Coevolutionary Particle Swarm Optimization Using Gaussian Distribution for Solving Constrained Optimization Problems , 2006, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[21]  Mahmood Joorabian,et al.  Optimal placement of Multi-type FACTS devices in power systems using evolution strategies , 2011, 2011 5th International Power Engineering and Optimization Conference.

[22]  Jinhua Wang,et al.  A ranking selection-based particle swarm optimizer for engineering design optimization problems , 2008 .

[23]  Siti Mariyam Hj. Shamsuddin,et al.  CAPSO: Centripetal accelerated particle swarm optimization , 2014, Inf. Sci..

[24]  Angel Eduardo Muñoz Zavala,et al.  Continuous Constrained Optimization with Dynamic Tolerance Using the COPSO Algorithm , 2009 .

[25]  Carlos A. Coello Coello,et al.  A Particle Swarm Optimizer for Constrained Numerical Optimization , 2006, PPSN.

[26]  Jan Paredis,et al.  Co-evolutionary Constraint Satisfaction , 1994, PPSN.

[27]  Tu Van Le,et al.  A Fuzzy Evolutionary Approach To Constrained Optimisation Problems , 1996, International Conference on Evolutionary Computation.

[28]  Jeffrey Horn,et al.  Handbook of evolutionary computation , 1997 .

[29]  Patrick D. Surry,et al.  A Multi-objective Approach to Constrained Optimisation of Gas Supply Networks: the COMOGA Method , 1995, Evolutionary Computing, AISB Workshop.

[30]  Z. Michalewicz,et al.  Genocop III: a co-evolutionary algorithm for numerical optimization problems with nonlinear constraints , 1995, Proceedings of 1995 IEEE International Conference on Evolutionary Computation.

[31]  Tetsuyuki Takahama,et al.  Constrained optimization by applying the /spl alpha/ constrained method to the nonlinear simplex method with mutations , 2005, IEEE Transactions on Evolutionary Computation.

[32]  A. Rezaee Jordehi.  A chaotic artificial immune system optimisation algorithm for solving global continuous optimisation problems , 2014, Neural Computing and Applications.

[33]  Ahmad Rezaee Jordehi,et al.  Heuristic methods for solution of FACTS optimization problem in power systems , 2011, 2011 IEEE Student Conference on Research and Development.

[34]  A. Rezaee Jordehi,et al.  A chaotic-based big bang–big crunch algorithm for solving global optimisation problems , 2014, Neural Computing and Applications.

[35]  A. Rezaee Jordehi,et al.  Enhanced leader PSO (ELPSO): A new PSO variant for solving global optimisation problems , 2015, Appl. Soft Comput..

[36]  P. Eberhard,et al.  Using augmented Lagrangian particle swarm optimization for constrained problems in engineering">Using augmented Lagrangian particle swarm optimization for constrained problems in engineering , 2006 .

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

[38]  Xiaohui Hu,et al.  Engineering optimization with particle swarm , 2003, Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS'03 (Cat. No.03EX706).

[39]  Ahmad Rezaee Jordehi,et al.  Particle swarm optimisation applications in FACTS optimisation problem , 2013, 2013 IEEE 7th International Power Engineering and Optimization Conference (PEOCO).

[40]  Shang He,et al.  An improved particle swarm optimizer for mechanical design optimization problems , 2004 .

[41]  Rolf Wanka,et al.  Particle Swarm Optimization in High-Dimensional Bounded Search Spaces , 2007, 2007 IEEE Swarm Intelligence Symposium.

[42]  Zbigniew Michalewicz,et al.  Handbook of Evolutionary Computation , 1997 .

[43]  Carlos A. Coello Coello,et al.  Boundary Search for Constrained Numerical Optimization Problems With an Algorithm Inspired by the Ant Colony Metaphor , 2009, IEEE Transactions on Evolutionary Computation.

[44]  T. Ray,et al.  A swarm with an effective information sharing mechanism for unconstrained and constrained single objective optimisation problems , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[45]  C. Worasucheep,et al.  Solving constrained engineering optimization problems by the constrained PSO-DD , 2008, 2008 5th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology.

[46]  Haiyan Lu,et al.  Self-adaptive velocity particle swarm optimization for solving constrained optimization problems , 2008, J. Glob. Optim..

[47]  Christopher R. Houck,et al.  On the use of non-stationary penalty functions to solve nonlinear constrained optimization problems with GA's , 1994, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence.

[48]  Carlos A. Coello Coello,et al.  Handling Constraints in Particle Swarm Optimization Using a Small Population Size , 2007, MICAI.

[49]  Ángel Fernando Kuri Morales,et al.  A UNIVERSAL ECLECTIC GENETIC ALGORITHM FOR CONSTRAINED OPTIMIZATION , 2022 .

[50]  Ali R. Yildiz,et al.  A new hybrid differential evolution algorithm for the selection of optimal machining parameters in milling operations , 2013, Appl. Soft Comput..

[51]  Xin Yao,et al.  Stochastic ranking for constrained evolutionary optimization , 2000, IEEE Trans. Evol. Comput..

[52]  Shaohua Xu,et al.  A Modified Quantum-Behaved Particle Swarm Optimization for Constrained Optimization , 2008, 2008 International Symposium on Intelligent Information Technology Application Workshops.

[53]  Keith E. Mathias,et al.  In Parallel Problem Solving from Nature-PPSN III , 1994 .

[54]  N. Hansen,et al.  Markov Chain Analysis of Cumulative Step-Size Adaptation on a Linear Constrained Problem , 2015, Evolutionary Computation.

[55]  T. Takahama.  Constrained Optimization by Combining the α Constrained Method with Particle Swarm Optimization , 2004 .

[56]  James C. Bean,et al.  A Genetic Algorithm for the Multiple-Choice Integer Program , 1997, Oper. Res..

[57]  Peter Eberhard,et al.  Constrained Particle Swarm Optimization of Mechanical Systems , 2005 .

[58]  A. Rezaee Jordehi.  Particle swarm optimisation for dynamic optimisation problems: a review , 2014, Neural Computing and Applications.

[59]  O. Nelles,et al.  An Introduction to Optimization , 1996, IEEE Antennas and Propagation Magazine.

[60]  Angel Eduardo Muñoz Zavala,et al.  Constrained optimization via particle evolutionary swarm optimization algorithm (PESO) , 2005, GECCO '05.

[61]  Susan E. Carlson,et al.  Annealing a genetic algorithm over constraints , 1998, SMC'98 Conference Proceedings. 1998 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.98CH36218).

[62]  Carlos A. Coello Coello,et al.  Use of a self-adaptive penalty approach for engineering optimization problems , 2000 .

[63]  Seyed Hossein Hosseinian,et al.  Generation and reserve dispatch in a competitive market using constrained particle swarm optimization , 2010 .

[64]  Rolf Wanka,et al.  Heterogeneous constraint handling for particle swarm optimization , 2011, 2011 IEEE Symposium on Swarm Intelligence.

[65]  Abdollah Homaifar,et al.  Constrained Optimization Via Genetic Algorithms , 1994, Simul..

[66]  Wei Zheng,et al.  Co-evolutionary particle swarm optimization to solve constrained optimization problems , 2009, Comput. Math. Appl..

[67]  Michael N. Vrahatis,et al.  Particle Swarm Optimization Method for Constrained Optimization Problems , 2002 .

[68]  Minghao Yin,et al.  Animal migration optimization: an optimization algorithm inspired by animal migration behavior , 2014, Neural Computing and Applications.

[69]  T. Takahama,et al.  Solving Constrained Optimization Problems by the ε Constrained Particle Swarm Optimizer with Adaptive Velocity Limit Control , 2006, 2006 IEEE Conference on Cybernetics and Intelligent Systems.

[70]  K. Deb.  An Efficient Constraint Handling Method for Genetic Algorithms , 2000 .

[71]  Russell C. Eberhart,et al.  Solving Constrained Nonlinear Optimization Problems with Particle Swarm Optimization , 2002 .

[72]  del Valle,et al.  Optimization of Power System Performance Using Facts Devices , 2010 .

[73]  Jing J. Liang,et al.  Dynamic Multi-Swarm Particle Swarm Optimizer with a Novel Constraint-Handling Mechanism , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[74]  Enrique Raúl Villa Diharce,et al.  PESO+for Constrained Optimization , 2006, IEEE Congress on Evolutionary Computation.

[75]  A. Rezaee Jordehi,et al.  Optimal setting of TCSCs in power systems using teaching–learning-based optimisation algorithm , 2015 .

[76]  Ling Wang,et al.  A hybrid particle swarm optimization with a feasibility-based rule for constrained optimization , 2007, Appl. Math. Comput..

[77]  Yew-Soon Ong,et al.  Advances in Natural Computation, First International Conference, ICNC 2005, Changsha, China, August 27-29, 2005, Proceedings, Part I , 2005, ICNC.

[78]  Michael N. Vrahatis,et al.  Unified Particle Swarm Optimization for Solving Constrained Engineering Optimization Problems , 2005, ICNC.

[79]  Gary G. Yen,et al.  Constrained Multiple-Swarm Particle Swarm Optimization Within a Cultural Framework , 2012, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[80]  A. Rezaee Jordehi,et al.  Chaotic bat swarm optimisation (CBSO) , 2015, Appl. Soft Comput..

[81]  Carlos A. Coello Coello,et al.  A constraint-handling mechanism for particle swarm optimization , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[82]  Jinhua Zheng,et al.  An Improved Particle Swarm Algorithm for Solving Nonlinear Constrained Optimization Problems , 2007, Third International Conference on Natural Computation (ICNC 2007).

[83]  Efrén Mezura-Montes,et al.  Looking Inside Particle Swarm Optimization in Constrained Search Spaces , 2008, MICAI.

[84]  Frank Hoffmeister,et al.  Problem-Independent Handling of Constraints by Use of Metric Penalty Functions , 1996, Evolutionary Programming.

[85]  S. Halgamuge,et al.  A comparison of constraint-handling methods for the application of particle swarm optimization to constrained nonlinear optimization problems , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[86]  Zbigniew Michalewicz,et al.  Adaptive evolutionary planner/navigator for mobile robots , 1997, IEEE Trans. Evol. Comput..

[87]  Haiyan Lu,et al.  Dynamic-objective particle swarm optimization for constrained optimization problems , 2006, J. Comb. Optim..