Established and Recently Proposed Variants of Particle Swarm Optimization

Thus, we excluded variants based on complicated hybrid schemes that combine other algorithms, where it is not evident which algorithm triggers which effect, as well as over-specialized schemes that refer only to one problem type or instance. Under this prism, we selected the following methods: unified PSO, memetic PSO, composite PSO, vector evaluated PSO, guaranteed convergence PSO, cooperative PSO, niching PSO, TRIBES, and quantum PSO. Albeit possibly omitting an interesting approach, the aforementioned variants sketch a rough picture of the current status in PSO literature, exposing the main ideas and features that constitute the core of research nowadays.

[1]  Roger J.-B. Wets,et al.  Minimization by Random Search Techniques , 1981, Math. Oper. Res..

[2]  Frans van den Bergh,et al.  An analysis of particle swarm optimizers , 2002 .

[3]  Wenbo Xu,et al.  Particle swarm optimization with particles having quantum behavior , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[4]  Michael N. Vrahatis,et al.  Entropy-based Memetic Particle Swarm Optimization for computing periodic orbits of nonlinear mappings , 2007, 2007 IEEE Congress on Evolutionary Computation.

[5]  Helen G. Cobb Is the Genetic Algorithm a Cooperative Learner? , 1992, FOGA.

[6]  David G. Stork,et al.  Evolution and Learning in Neural Networks: The Number and Distribution of Learning Trials Affect the Rate of Evolution , 1990, NIPS 1990.

[7]  L. Coelho A quantum particle swarm optimizer with chaotic mutation operator , 2008 .

[8]  Miao Li,et al.  Study of population diversity of multiobjective evolutionary algorithm based on immune and entropy principles , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[9]  Wenbo Xu,et al.  An improved quantum-behaved particle swarm optimization algorithm with weighted mean best position , 2008, Appl. Math. Comput..

[10]  Michael N. Vrahatis,et al.  Parameter selection and adaptation in Unified Particle Swarm Optimization , 2007, Math. Comput. Model..

[11]  M. Clerc,et al.  Particle Swarm Optimization , 2006 .

[12]  Michael N. Vrahatis,et al.  Enhanced Learning in Fuzzy Simulation Models Using Memetic Particle Swarm Optimization , 2007, 2007 IEEE Swarm Intelligence Symposium.

[13]  W. Hart Adaptive global optimization with local search , 1994 .

[14]  Claude E. Shannon,et al.  The Mathematical Theory of Communication , 1950 .

[15]  Pablo Moscato,et al.  On Evolution, Search, Optimization, Genetic Algorithms and Martial Arts : Towards Memetic Algorithms , 1989 .

[16]  Singiresu S. Rao,et al.  Optimization Theory and Applications , 1980, IEEE Transactions on Systems, Man, and Cybernetics.

[17]  Maurice Clerc,et al.  The particle swarm - explosion, stability, and convergence in a multidimensional complex space , 2002, IEEE Trans. Evol. Comput..

[18]  Kenneth A. De Jong,et al.  Cooperative Coevolution: An Architecture for Evolving Coadapted Subcomponents , 2000, Evolutionary Computation.

[19]  Jun Sun,et al.  A global search strategy of quantum-behaved particle swarm optimization , 2004, IEEE Conference on Cybernetics and Intelligent Systems, 2004..

[20]  Xiaodong Li,et al.  Adaptively choosing niching parameters in a PSO , 2006, GECCO.

[21]  Michael N. Vrahatis,et al.  Modification of the Particle Swarm Optimizer for Locating All the Global Minima , 2001 .

[22]  M. N. Vrahatis,et al.  Particle swarm optimization method in multiobjective problems , 2002, SAC '02.

[23]  Michael N. Vrahatis,et al.  Memetic particle swarm optimization , 2007, Ann. Oper. Res..

[24]  Wenbo Xu,et al.  Culture conditions optimization of hyaluronic acid production by Streptococcus zooepidemicus based on radial basis function neural network and quantum-behaved particle swarm optimization algorithm , 2009 .

[25]  Geoffrey E. Hinton,et al.  How Learning Can Guide Evolution , 1996, Complex Syst..

[26]  Jeffrey Horn,et al.  The nature of niching: genetic algorithms and the evolution of optimal, cooperative populations , 1997 .

[27]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[28]  Kenneth A. De Jong,et al.  A Cooperative Coevolutionary Approach to Function Optimization , 1994, PPSN.

[29]  Konstantinos E. Parsopoulos,et al.  UPSO: A Unified Particle Swarm Optimization Scheme , 2019, International Conference of Computational Methods in Sciences and Engineering 2004 (ICCMSE 2004).

[30]  Andries Petrus Engelbrecht,et al.  A Cooperative approach to particle swarm optimization , 2004, IEEE Transactions on Evolutionary Computation.

[31]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[32]  Thomas Stützle,et al.  Stochastic Local Search: Foundations & Applications , 2004 .

[33]  Tsung-Ying Lee Short term hydroelectric power system scheduling with wind turbine generators using the multi-pass iteration particle swarm optimization approach , 2008 .

[34]  R. Belew,et al.  Evolutionary algorithms with local search for combinatorial optimization , 1998 .

[35]  A.A. Kishk,et al.  Quantum Particle Swarm Optimization for Electromagnetics , 2006, IEEE Transactions on Antennas and Propagation.

[36]  Jing Tang,et al.  Adaptation for parallel memetic algorithm based on population entropy , 2006, GECCO '06.

[37]  Graham Kendall,et al.  Diversity in genetic programming: an analysis of measures and correlation with fitness , 2004, IEEE Transactions on Evolutionary Computation.

[38]  Konstantinos E. Parsopoulos,et al.  MULTIOBJECTIVE OPTIMIZATION USING PARALLEL VECTOR EVALUATED PARTICLE SWARM OPTIMIZATION , 2003 .

[39]  Michael N. Vrahatis,et al.  Recent approaches to global optimization problems through Particle Swarm Optimization , 2002, Natural Computing.

[40]  Heinz Mühlenbein,et al.  Evolution algorithms in combinatorial optimization , 1988, Parallel Comput..

[41]  Robert Hooke,et al.  `` Direct Search'' Solution of Numerical and Statistical Problems , 1961, JACM.

[42]  Ioan Cristian Trelea,et al.  The particle swarm optimization algorithm: convergence analysis and parameter selection , 2003, Inf. Process. Lett..

[43]  Natalio Krasnogor,et al.  Studies on the theory and design space of memetic algorithms , 2002 .