Particle Swarm Optimisation: A Historical Review Up to the Current Developments
Tóm tắt
Từ khóa
Tài liệu tham khảo
Eberhart, R., and Kennedy, J. (1995, January 4–6). A new optimizer using particle swarm theory. Proceedings of the 6th International Symposium on Micro Machine and Human Science (MHS), Nagoya, Japan.
Kennedy, J., and Eberhart, R. (December, January 27). Particle swarm optimization. Proceedings of the International Conference on Neural Networks (ICNN), Perth, Australia.
Bonyadi, 2017, Particle swarm optimization for single objective continuous space problems: A review, Evol. Comput., 25, 1, 10.1162/EVCO_r_00180
Løvbjerg, M., Rasmussen, T.K., and Krink, T. (2001, January 7–11). Hybrid particle swarm optimiser with breeding and subpopulations. Proceedings of the 3rd Annual Conference on Genetic and Evolutionary Computation (GECCO), San Francisco, CA, USA.
Kennedy, J. (1999, January 6–9). Small worlds and mega-minds: Effects of neighborhood topology on particle swarm performance. Proceedings of the Congress on Evolutionary Computation (CEC), Washington, WA, USA.
Clerc, M. (1999, January 6–9). The swarm and the queen: Towards a deterministic and adaptive particle swarm optimization. Proceedings of the Congress on Evolutionary Computation (CEC), Washington, WA, USA.
Kennedy, J., and Eberhart, R.C. (1997, January 12–15). A discrete binary version of the particle swarm algorithm. Proceedings of the IEEE International Conference on Systems, Man and Cybernetics (SMC), Orlando, FL, USA.
Rosendo, M., and Pozo, A. (2010, January 18–23). A hybrid particle swarm optimization algorithm for combinatorial optimization problems. Proceedings of the IEEE Congress on Evolutionary Computation (CEC), Barcelona, Spain.
Rosendo, M., and Pozo, A. (2010, January 23–28). Applying a discrete particle swarm optimization algorithm to combinatorial problems. Proceedings of the 11th Brazilian Symposium on Neural Networks (SBRN), São Paulo, Brazil.
Junliang, L., Wei, H., Huan, S., Yaxin, L., and Jing, L. (2017, January 18–20). Particle swarm algorithm based task scheduling for many-core systems. Proceedings of the 12th IEEE Conference on Industrial Electronics and Applications (ICIEA), Siem Reap, Cambodia.
Ozcan, E., and Mohan, C.K. (1998, January 1–4). Analysis of a simple particle swarm optimization system. Proceedings of the Intelligent Engineering Systems Through Artificial Neural Networks (ANNIE), St. Louis, MO, USA.
Ozcan, E., and Mohan, C.K. (1999, January 6–9). Particle swarm optimization: Surfing the waves. Proceedings of the Congress on Evolutionary Computation (CEC), Washington, WA, USA.
Sun, 2013, A new fitness estimation strategy for particle swarm optimization, Inf. Sci., 221, 355, 10.1016/j.ins.2012.09.030
Clerc, 2002, The particle swarm – Explosion, stability, and convergence in a multidimensional complex space, IEEE Trans. Evol. Comput., 6, 58, 10.1109/4235.985692
Shi, Y., and Eberhart, R.C. (1998, January 4–9). A modified particle swarm optimizer. Proceedings of the IEEE World Congress on Computational Intelligence (WCCI), Anchorage, AK, USA.
Van den Bergh, F. (2002). An Analysis of Particle Swarm Optimizers. [Ph.D. Thesis, University of Pretoria].
Sengupta, S., Basak, S., and Peters, R.A. (2018). Particle swarm optimization: A survey of historical and recent developments with hybridization perspectives. Mach. Learn. Knowl. Extr., 1.
Eberhart, R.C., and Shi, Y. (2001, January 27–30). Tracking and optimizing dynamic systems with particle swarms. Proceedings of the Congress on Evolutionary Computation (CEC), Seoul, Korea.
Shi, Y., and Eberhart, R.C. (2001, January 27–30). Fuzzy adaptive particle swarm optimization. Proceedings of the IEEE Congress on Evolutionary Computation (CEC), Seoul, Korea.
Eberhart, R.C., and Shi, Y. (2000, January 16–19). Comparing inertia weights and constriction factors in particle swarm optimization. Proceedings of the Congress on Evolutionary Computation (CEC), La Jolla, CA, USA.
Xin, J., Chen, G., and Hai, Y. (2009, January 24–26). A particle swarm optimizer with multi-stage linearly-decreasing inertia weight. Proceedings of the International Joint Conference on Computational Sciences and Optimization (CSO), Sanya, China.
Chatterjee, 2006, Nonlinear inertia weight variation for dynamic adaptation in particle swarm optimization, Comput. Oper. Res., 33, 859, 10.1016/j.cor.2004.08.012
Eberhart, R.C., and Shi, Y. (2001, January 27–30). Particle swarm optimization: Developments, applications and resources. Proceedings of the Congress on Evolutionary Computation (CEC), Seoul, Korea.
Kar, R., Mandal, D., Bardhan, S., and Ghoshal, S.P. (2011, January 25–28). Optimization of linear phase FIR band pass filter using particle swarm optimization with constriction factor and inertia weight approach. Proceedings of the IEEE Symposium on Industrial Electronics and Applications (ICIEA), Langkawi, Malaysia.
Kennedy, J., and Mendes, R. (2002, January 12–17). Population structure and particle swarm performance. Proceedings of the Congress on Evolutionary Computation (CEC), Honolulu, HI, USA.
Suganthan, P.N. (1999, January 6–9). Particle swarm optimiser with neighbourhood operator. Proceedings of the Congress on Evolutionary Computation (CEC), Washington, WA, USA.
Kennedy, J. (2000, January 16–19). Stereotyping: Improving particle swarm performance with cluster analysis. Proceedings of the Congress on Evolutionary Computation (CEC), La Jolla, CA, USA.
Veeramachaneni, K., Peram, T., Mohan, C., and Osadciw, L.A. (2003, January 12–16). Optimization using particle swarms with near neighbor interactions. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO), Chicago, IL, USA.
Peram, T., Veeramachaneni, K., and Mohan, C.K. (2003, January 26). Fitness-distance-ratio based particle swarm optimization. Proceedings of the IEEE Swarm Intelligence Symposium (SIS), Indianapolis, IN, USA.
Van den Bergh, F., and Engelbrecht, A.P. (2002, January 6–9). A new locally convergent particle swarm optimiser. Proceedings of the IEEE International Conference on Systems, Man and Cybernetics (SMC), Yasmine Hammamet, Tunisia.
Brits, R., Engelbrecht, A.P., and van den Bergh, F. (2002, January 18–22). A niching particle swarm optimizer. Proceedings of the 4th Conference on Simulated Evolution and Learning (SEAL), Singapore.
Higashi, N., and Iba, H. (2003, January 26). Particle swarm optimization with Gaussian mutation. Proceedings of the IEEE Swarm Intelligence Symposium (SIS), Indianapolis, IN, USA.
Tang, H., Yang, X., and Xiong, S. (2013, January 16–18). Modified particle swarm algorithm for vehicle routing optimization of smart logistics. Proceedings of the 2nd International Conference on Measurement, Information and Control (ICMIC), Harbin, China.
Engelbrecht, A.P. (2014, January 9–12). Asynchronous particle swarm optimization with discrete crossover. Proceedings of the IEEE Symposium on Swarm Intelligence (SIS), Orlando, FL, USA.
Peer, E.S., van den Bergh, F., and Engelbrecht, A.P. (2003, January 26). Using neighbourhoods with the guaranteed convergence PSO. Proceedings of the IEEE Swarm Intelligence Symposium (SIS), Indianapolis, IN, USA.
Engelbrecht, 2000, Cooperative learning in neural networks using particle swarm optimizers, S. Afr. Comput. J., 26, 84
Engelbrecht, 2004, A cooperative approach to particle swarm optimization, IEEE Trans. Evol. Comput., 8, 225, 10.1109/TEVC.2004.826069
Zhan, 2009, Adaptive particle swarm optimization, IEEE Trans. Syst. Man Cybern., 39, 1362, 10.1109/TSMCB.2009.2015956
Parsopoulos, K.E., and Vrahatis, M.N. (2002). Particle swarm optimization method for constrained optimization problem. Intelligent Technologies: From Theory to Applications, IOS Press.
Hu, X., and Eberhart, R. (2002, January 14–18). Solving constrained nonlinear optimization problems with particle swarm optimization. Proceedings of the 6th World Multiconference on Systemics, Cybernetics and Informatics (SCI), Orlando, FL, USA.
Hu, X., Eberhart, R.C., and Shi, Y. (2003, January 26). Engineering optimization with particle swarm. Proceedings of the IEEE Swarm Intelligence Symposium (SIS), Indianapolis, IN, USA.
Coath, G., and Halgamuge, S.K. (2003, January 8–12). A comparison of constraint-handling methods for the application of particle swarm optimization to constrained nonlinear optimization problems. Proceedings of the Congress on Evolutionary Computation (CEC), Canberra, Australia.
He, 2004, An improved particle swarm optimizer for mechanical design optimization problems, Eng. Optim., 36, 585, 10.1080/03052150410001704854
Sun, 2011, An improved vector particle swarm optimization for constrained optimization problems, Inf. Sci., 181, 1153, 10.1016/j.ins.2010.11.033
Hu, X., and Eberhart, R.C. (2002, January 12–17). Multiobjective optimization using dynamic neighborhood particle swarm optimization. Proceedings of the Congress on Evolutionary Computation (CEC), Honolulu, HI, USA.
Coello Coello, C.A., and Salazar Lechuga, M. (2002, January 12–17). MOPSO: A proposal for multiple objective particle swarm optimization. Proceedings of the Congress on Evolutionary Computation (CEC), Honolulu, HI, USA.
2004, Handling multiple objectives with particle swarm optimization, IEEE Trans. Evol. Comput., 8, 256, 10.1109/TEVC.2004.826067
Fieldsend, J.E., and Singh, S. (2002, January 2–4). A multi-objective algorithm based upon particle swarm optimisation, an efficient data structure and turbulence. Proceedings of the UK Workshop on Computational Intelligence (UKCI), Birmingham, UK.
Mostaghim, S., and Teich, J. (2003, January 26). Strategies for finding good local guides in multi-objective particle swarm optimization (MOPSO). Proceedings of the IEEE Swarm Intelligence Symposium (SIS), Indianapolis, IN, USA.
Parsopoulos, K.E., Plagianakos, V.P., Magoulas, G.D., and Vrahatis, M.N. (2001, January 6–7). Stretching technique for obtaining global minimizers through particle swarm optimization. Proceedings of the PSO Workshop (PSOW), Indianapolis, IN, USA.
Parsopoulos, K.E., and Vrahatis, M.N. (2001, January 22–25). Modification of the particle swarm optimizer for locating all the global minima. Proceedings of the International Conference on Artificial Neural Nets and Genetic Algorithms (ICANNGA), Prague, Czech Republic.
Brits, R., Engelbrecht, A.P., and van den Bergh, F. (2002, January 6–9). Solving systems of unconstrained equations using particle swarm optimization. Proceedings of the IEEE International Conference on Systems, Man and Cybernetics (SMC), Yasmine Hammamet, Tunisia.
Parsopoulos, 2004, On the computation of all global minimizers through particle swarm optimization, IEEE Trans. Evol. Comput., 8, 211, 10.1109/TEVC.2004.826076
Blackwell, T., and Branke, J. (2004, January 5–7). Multi-swarm optimization in dynamic environments. Proceedings of the Workshops on Applications of Evolutionary Computation, Coimbra, Portugal.
Kennedy, J. (1997, January 13–16). The particle swarm: Social adaptation of knowledge. Proceedings of the IEEE International Conference on Evolutionary Computation (ICEC), Indianapolis, IN, USA.
Schoeman, I.L., and Engelbrecht, A.P. (2004, January 1–3). Using vector operations to identify niches for particle swarm optimization. Proceedings of the IEEE Conference on Cybernetics and Intelligent Systems (CIS), Singapore.
Schoeman, I.L., and Engelbrecht, A.P. (2005, January 21–23). A parallel vector-based particle swarm optimizer. Proceedings of the 7th International Conference on Adaptive and Natural Computing Algorithms (ICANNGA), Coimbra, Portugal.
Li, X. (2007, January 7–11). A multimodal particle swarm optimizer based on fitness euclidean-distance ratio. Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation (GECCO), London, UK.
Li, 2007, An efficient fine-grained parallel particle swarm optimization method based on GPU-acceleration, Int. J. Innov. Comput. Inf. Control., 3, 1707
Li, X. (2004, January 26–30). Adaptively choosing neighbourhood bests using species in a particle swarm optimizer for multimodal function optimization. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO), Seattle, WA, USA.
Parrott, D., and Li, X. (2004, January 19–23). A particle swarm model for tracking multiple peaks in a dynamic environment using speciation. Proceedings of the Congress on Evolutionary Computation (CEC), Portland, OR, USA.
Parrott, 2006, Locating and tracking multiple dynamic optima by a particle swarm model using speciation, IEEE Trans. Evol. Comput., 10, 440, 10.1109/TEVC.2005.859468
Li, 2010, Niching without niching parameters: Particle swarm optimization using a ring topology, IEEE Trans. Evol. Comput., 14, 150, 10.1109/TEVC.2010.2050024
Yue, 2018, A multi-objective particle swarm optimizer using ring topology for solving multimodal multi-objective problems, IEEE Trans. Evol. Comput., 22, 805, 10.1109/TEVC.2017.2754271
Mendes, 2004, The fully informed particle swarm: Simpler, maybe better, IEEE Trans. Evol. Comput., 8, 204, 10.1109/TEVC.2004.826074
Lalwani, 2019, A survey on parallel particle swarm optimization algorithms, Arab. J. Sci. Eng., 44, 2899, 10.1007/s13369-018-03713-6
Gupta, M., and Deep, K. (2009, January 9–11). A state-of-the-art review of population-based parallel meta-heuristics. Proceedings of the World Congress on Nature and Biologically Inspired Computing (NaBIC), Coimbatore, India.
Gies, D., and Rahmat-Samii, Y. (2003, January 22–27). Reconfigurable array design using parallel particle swarm optimization. Proceedings of the IEEE Antennas and Propagation Society International Symposium, Columbus, OH, USA.
Baskar, S., and Suganthan, P.N. (2004, January 19–23). A novel concurrent particle swarm optimization. Proceedings of the Congress on Evolutionary Computation (CEC), Portland, OR, USA.
Chu, S.C., and Pan, J.S. (2006). Intelligent parallel particle swarm optimization algorithms. Parallel Evolutionary Computations, Springer.
Chang, 2005, A parallel particle swarm optimization algorithm with communication strategies, J. Inf. Sci. Eng., 21, 809
Schutte, J.F., Fregly, B.J., Haftka, R.T., and George, A.D. (2003). A Parallel Particle Swarm Optimizer, Department of Electrical and Computer Engineering, University of Florida. Technical Report.
Schutte, 2004, Parallel global optimization with the particle swarm algorithm, Int. J. Numer. Methods Eng., 61, 2296, 10.1002/nme.1149
Venter, 2006, Parallel particle swarm optimization algorithm accelerated by asynchronous evaluations, J. Aerosp. Comput. Inf. Commun., 3, 123, 10.2514/1.17873
Koh, 2006, Parallel asynchronous particle swarm optimization, Int. J. Numer. Meth. Eng., 67, 578, 10.1002/nme.1646
McNabb, A.W., Monson, C.K., and Seppi, K.D. (2007, January 25–28). Parallel PSO using MapReduce. Proceedings of the IEEE Congress on Evolutionary Computation (CEC), Singapore.
Aljarah, I., and Ludwig, S.A. (2012, January 5–9). Parallel particle swarm optimization clustering algorithm based on MapReduce methodology. Proceedings of the 4th World Congress on Nature and Biologically Inspired Computing (NaBIC), Mexico City, Mexico.
Han, F., Cui, W., Wei, G., and Wu, S. (2008, January 26–29). Application of parallel PSO algorithm to motion parameter estimation. Proceedings of the 9th International Conference on Signal Processing (SIP), Beijing, China.
Kodaz, 2015, A novel parallel multi-swarm algorithm based on comprehensive learning particle swarm optimization, Eng. Appl. Artif. Intell., 45, 33, 10.1016/j.engappai.2015.06.013
Cao, 2017, Distributed parallel particle swarm optimization for multi-objective and many-objective large-scale optimization, IEEE Access, 5, 8214, 10.1109/ACCESS.2017.2702561
Lorion, Y., Bogon, T., Timm, I.J., and Drobnik, O. (April, January 30). An agent based parallel particle swarm optimization—APPSO. Proceedings of the IEEE Swarm Intelligence Symposium (SIS), Nashville, TN, USA.
Dali, 2015, GPU-PSO: Parallel particle swarm optimization approaches on graphical processing unit for constraint reasoning: Case of Max-CSPs, Procedia Comput. Sci., 60, 1070, 10.1016/j.procs.2015.08.152
Rymut, B., and Kwolek, B. (2010, January 20–22). GPU-supported object tracking using adaptive appearance models and particle swarm optimization. Proceedings of the International Conference on Computer Vision and Graphics (ICCVG), Warsaw, Poland.
Zhou, Y., and Tan, Y. (2009, January 18–21). GPU-based parallel particle swarm optimization. Proceedings of the IEEE Congress on Evolutionary Computation (CEC), Trondheim, Norway.
Hussain, M.M., Hattori, H., and Fujimoto, N. (2016, January 24–27). A CUDA implementation of the standard particle swarm optimization. Proceedings of the 18th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC), Timisoara, Romania.
Mussi, 2011, Evaluation of parallel particle swarm optimization algorithms within the CUDA™ architecture, Inf. Sci., 181, 4642, 10.1016/j.ins.2010.08.045
Zhu, H., Pu, C., Eguchi, K., and Gu, J. (2009, January 1–3). Euclidean particle swarm optimization. Proceedings of the 2nd International Conference on Intelligent Networks and Intelligent Systems (ICINIS), Tianjin, China.
Zhu, H., Guo, Y., Wu, J., Gu, J., and Eguchi, K. (2011, January 1–3). Paralleling Euclidean particle swarm optimization in CUDA. Proceedings of the 4th International Conference on Intelligent Networks and Intelligent Systems (ICINIS), Kunming, China.
Awwad, 2013, Distributed topology control in large-scale hybrid RF/FSO networks: SIMT GPU-based particle swarm optimization approach, Int. J. Commun. Syst., 26, 888, 10.1002/dac.1376
Hung, 2012, Accelerating parallel particle swarm optimization via GPU, Optim. Methods Softw., 27, 33, 10.1080/10556788.2010.509435
Angeline, P.J. (1998, January 4–9). Using selection to improve particle swarm optimization. Proceedings of the IEEE World Congress on Computational Intelligence (WCCI), Anchorage, AK, USA.
Yang, B., Chen, Y., and Zhao, Z. (June, January 30). A hybrid evolutionary algorithm by combination of PSO and GA for unconstrained and constrained optimization problems. Proceedings of the IEEE International Conference on Control and Automation (ICCA), Guangzhou, China.
Jana, 2019, Repository and mutation based particle swarm optimization (RMPSO): A new PSO variant applied to reconstruction of gene regulatory network, Appl. Soft Comput., 74, 330, 10.1016/j.asoc.2018.09.027
Stacey, A., Jancic, M., and Grundy, I. (2003, January 8–12). Particle swarm optimization with mutation. Proceedings of the Congress on Evolutionary Computation (CEC), Canberra, Australia.
Imran, M., Jabeen, H., Ahmad, M., Abbas, Q., and Bangyal, W. (2010, January 22–24). Opposition based PSO and mutation operators. Proceedings of the 2nd International Conference on Education Technology and Computer (ICETC), Shanghai, China.
Miranda, V., and Fonseca, N. (2002, January 12–17). EPSO—Best-of-two-worlds meta-heuristic applied to power system problems. Proceedings of the Congress on Evolutionary Computation (CEC), Honolulu, HI, USA.
Darwin, C. (1998). The Origin of Species, Oxford University Press.
Wang, 2013, Diversity enhanced particle swarm optimization with neighborhood search, Inf. Sci., 223, 119, 10.1016/j.ins.2012.10.012
Robinson, J., Sinton, S., and Rahmat-Samii, Y. (2002, January 16–21). Particle swarm, genetic algorithm, and their hybrids: Optimization of a profiled corrugated horn antenna. Proceedings of the IEEE Antennas and Propagation Society International Symposium, San Antonio, TX, USA.
Juang, 2004, A hybrid of genetic algorithm and particle swarm optimization for recurrent network design, IEEE Trans. Syst. Man Cybern., 34, 997, 10.1109/TSMCB.2003.818557
Valdez, F., Melin, P., and Castillo, O. (2009, January 20–24). Evolutionary method combining particle swarm optimization and genetic algorithms using fuzzy logic for decision making. Proceedings of the IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Jeju Island, Korea.
Alba, E., Garcia-Nieto, J., Jourdan, L., and Talbi, E. (2007, January 25–28). Gene selection in cancer classification using PSO/SVM and GA/SVM hybrid algorithms. Proceedings of the IEEE Congress on Evolutionary Computation (CEC), Singapore.
Fu, 2013, Route planning for unmanned aerial vehicle (UAV) on the sea using hybrid differential evolution and quantum-behaved particle swarm optimization, IEEE Trans. Syst. Man Cybern. Syst., 43, 1451, 10.1109/TSMC.2013.2248146
Yang, 2014, Task allocation for wireless sensor network using modified binary particle swarm optimization, IEEE Sens. J., 14, 882, 10.1109/JSEN.2013.2290433
Tian, 2016, Dual-objective scheduling of rescue vehicles to distinguish forest fires via differential evolution and particle swarm optimization combined algorithm, IEEE Trans. Intell. Transp. Syst., 17, 3009, 10.1109/TITS.2015.2505323
Senthilnath, 2016, A novel approach for multispectral satellite image classification based on the Bat algorithm, IEEE Geosci. Remote. Sens. Lett., 13, 599, 10.1109/LGRS.2016.2530724
Storn, 1997, Differential evolution—A simple and efficient heuristic for global optimization over continuous spaces, J. Glob. Optim., 11, 341, 10.1023/A:1008202821328
Das, 2011, Differential evolution: A survey of the state-of-the-art, IEEE Trans. Evol. Comput., 15, 4, 10.1109/TEVC.2010.2059031
Hendtlass, T. (2001, January 4–7). A combined swarm differential evolution algorithm for optimization problems. Proceedings of the International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA/AIE), Budapest, Hungary.
Zhang, W.J., and Xie, X.F. (2003, January 5–8). DEPSO: Hybrid particle swarm with differential evolution operator. Proceedings of the IEEE International Conference on Systems, Man and Cybernetics (SMC), Washington, WA, USA.
Luitel, B., and Venayagamoorthy, G.K. (2008, January 1–6). Differential evolution particle swarm optimization for digital filter design. Proceedings of the IEEE Congress on Evolutionary Computation (CEC), Hong Kong, China.
Talbi, H., and Batouche, M. (2004, January 8–10). Hybrid particle swarm with differential evolution for multimodal image registration. Proceedings of the IEEE International Conference on Industrial Technology (ICIT), Hammamet, Tunisia.
Xu, R., Xu, J., and Wunsch, D.C. (2010, January 18–23). Clustering with differential evolution particle swarm optimization. Proceedings of the IEEE Congress on Evolutionary Computation (CEC), Barcelona, Spain.
Miranda, V., and Alves, R. (2013, January 8–11). Differential evolutionary particle swarm optimization (DEEPSO): A successful hybrid. Proceedings of the BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence (BRICS-CCI & CBIC), Ipojuca, Brazil.
Abdullah, A., Deris, S., Hashim, S.Z.M., Mohamad, M.S., and Arjunan, S.N.V. (2011, January 5–8). An improved local best searching in particle swarm optimization using differential evolution. Proceedings of the 11th International Conference on Hybrid Intelligent Systems (HIS), Malacca, Malaysia.
Omran, M.G.H., Engelbrecht, A.P., and Salman, A. (2007, January 1–5). Differential evolution based particle swarm optimization. Proceedings of the IEEE Swarm Intelligence Symposium (SIS), Honolulu, HI, USA.
Pant, M., Thangaraj, R., Grosan, C., and Abraham, A. (2008, January 13–16). Hybrid differential evolution—Particle swarm optimization algorithm for solving global optimization problems. Proceedings of the 3rd International Conference on Digital Information Management, London, UK.
Epitropakis, M.G., Plagianakos, V.P., and Vrahatis, M.N. (2010, January 18–23). Evolving cognitive and social experience in particle swarm optimization through differential evolution. Proceedings of the IEEE Congress on Evolutionary Computation (CEC), Barcelona, Spain.
Zhang, 2009, A novel hybrid differential evolution and particle swarm optimization algorithm for unconstrained optimization, Oper. Res. Lett., 37, 117, 10.1016/j.orl.2008.12.008
Xiao, L., and Zuo, X. (2012, January 10–15). Multi-DEPSO: A DE and PSO based hybrid algorithm in dynamic environments. Proceedings of the IEEE Congress on Evolutionary Computation (CEC), Brisbane, Australia.
Omran, 2009, Bare bones differential evolution, Eur. J. Oper. Res., 196, 128, 10.1016/j.ejor.2008.02.035
Das, S., Konar, A., and Chakraborty, U.K. (2007, January 25–28). Annealed differential evolution. Proceedings of the IEEE Congress on Evolutionary Computation (CEC), Singapore.
Yang, G., Chen, D., and Zhou, G. (2006, January 16–19). A new hybrid algorithm of particle swarm optimization. Proceedings of the International Conference on Intelligent Computing (ICICA), Kunming, China.
Wang, X.H., and Li, J.J. (2004, January 26–29). Hybrid particle swarm optimization with simulated annealing. Proceedings of the International Conference on Machine Learning and Cybernetics (ICMLC), Shanghai, China.
Zhao, F., Zhang, Q., Yu, D., Chen, X., and Yang, Y. (2005, January 23–26). A hybrid algorithm based on PSO and simulated annealing and its applications for partner selection in virtual enterprise. Proceedings of the International Conference on Intelligent Computing (ICICA), Hefei, China.
Sadati, N., Zamani, M., and Mahdavian, H.R.F. (2006, January 6–10). Hybrid particle swarm-based-simulated annealing optimization techniques. Proceedings of the 32nd Annual Conference on IEEE Industrial Electronics (IECON), Paris, France.
Xia, 2006, A hybrid particle swarm optimization approach for the job-shop scheduling problem, Int. J. Adv. Manuf. Technol., 29, 360, 10.1007/s00170-005-2513-4
Chu, S.C., Tsai, P.W., and Pan, J.S. (2006). Parallel particle swarm optimization algorithms with adaptive simulated annealing. Stigmergic Optimization, Springer.
Shieh, 2011, Modified particle swarm optimization algorithm with simulated annealing behavior and its numerical verification, Appl. Math. Comput., 218, 4365
Deng, 2015, An improved PSO algorithm based on mutation operator and simulated annealing, Int. J. Multimed. Ubiquitous Eng., 10, 369, 10.14257/ijmue.2015.10.10.36
Dong, X., Ouyang, D., Cai, D., Zhang, Y., and Ye, Y. (2010, January 10–11). A hybrid discrete PSO-SA algorithm to find optimal elimination orderings for bayesian networks. Proceedings of the 2nd International Conference on Industrial and Information Systems (ICIIS), Dalian, China.
He, 2007, A hybrid particle swarm optimization with a feasibility-based rule for constrained optimization, Appl. Math. Comput., 186, 1407
Shelokar, 2007, Particle swarm and ant colony algorithms hybridized for improved continuous optimization, Appl. Math. Comput., 188, 129
Ghodrati, A., and Lotfi, S. (2011, January 20–22). A hybrid CS/GA algorithm for global optimization. Proceedings of the International Conference on Soft Computing for Problem Solving (SocProS), Roorkee, India.
Shi, X., Li, Y., Li, H., Guan, R., Wang, L., and Liang, Y. (2010, January 10–12). An integrated algorithm based on artificial bee colony and particle swarm optimization. Proceedings of the 6th International Conference on Natural Computation (ICNC), Yantai, China.
Eberhart, R.C., and Hu, X. (2002, January 6). Human tremor analysis using particle swarm optimization. Proceedings of the Congress on Evolutionary Computation (CEC), Washington, WA, USA.
Engelbrecht, 1999, Training product unit neural networks, Stab. Control Theory Appl., 2, 59
Zhang, C., Shao, H., and Li, Y. (2000, January 8–11). Particle swarm optimisation for evolving artificial neural network. Proceedings of the IEEE International Conference on Systems, Man and Cybernetics (SMC), Nashville, TN, USA.
Chatterjee, 2005, A particle-swarm-optimized fuzzy-neural network for voice-controlled robot systems, IEEE Trans. Ind. Electron., 52, 1478, 10.1109/TIE.2005.858737
Gudise, V.G., and Venayagamoorthy, G.K. (2003, January 26). Comparison of particle swarm optimization and backpropagation as training algorithms for neural networks. Proceedings of the IEEE Swarm Intelligence Symposium (SIS), Indianapolis, IN, USA.
Mendes, R., Cortez, P., Rocha, M., and Neves, J. (2002, January 12–17). Particle swarms for feedforward neural network training. Proceedings of the International Joint Conference on Neural Networks (IJCNN), Honolulu, HI, USA.
Ince, 2009, A generic and robust system for automated patient-specific classification of ECG signals, IEEE Trans. Biomed. Eng., 56, 1415, 10.1109/TBME.2009.2013934
Pehlivanoglu, 2013, A new particle swarm optimization method enhanced with a periodic mutation strategy and neural networks, IEEE Trans. Evol. Comput., 17, 436, 10.1109/TEVC.2012.2196047
Quan, 2014, Short-term load and wind power forecasting using neural network-based prediction intervals, IEEE Trans. Neural Netw. Learn. Syst., 25, 303, 10.1109/TNNLS.2013.2276053
Garro, B.A., Sossa, H., and Vazquez, R.A. (2009, January 14–19). Design of artificial neural networks using a modified particle swarm optimization algorithm. Proceedings of the International Joint Conference on Neural Networks (IJCNN), Atlanta, GA, USA.
Al-Kazemi, B., and Mohan, C.K. (2002, January 18–22). Training feedforward neural networks using multi-phase particle swarm optimization. Proceedings of the 9th International Conference on Neural Information Processing (ICONIP), Singapore.
Al-Kazemi, B., and Mohan, C.K. (2002, January 8–15). Multi-phase discrete particle swarm optimization. Proceedings of the 6th Joint Conference on Information Sciences (JCIS), Research Triange Park, NC, USA.
Al-Kazemi, B., and Mohan, C. (2005). Discrete multi-phase particle swarm optimization. Information Processing with Evolutionary Algorithms: From Industrial Applications to Academic Speculations, Springer.
Conforth, M., and Meng, Y. (2008, January 14–17). Toward evolving neural networks using bio-inspired algorithms. Proceedings of the International Conference on Artificial Intelligence (IC-AI), Las Vegas, NV, USA.
Hamada, M., and Hassan, M. (2018). Artificial neural networks and particle swarm optimization algorithms for preference prediction in multi-criteria recommender systems. Informatics, 5.
