zbMATH — the first resource for mathematics

Examples
Geometry Search for the term Geometry in any field. Queries are case-independent.
Funct* Wildcard queries are specified by * (e.g. functions, functorial, etc.). Otherwise the search is exact.
"Topological group" Phrases (multi-words) should be set in "straight quotation marks".
au: Bourbaki & ti: Algebra Search for author and title. The and-operator & is default and can be omitted.
Chebyshev | Tschebyscheff The or-operator | allows to search for Chebyshev or Tschebyscheff.
"Quasi* map*" py: 1989 The resulting documents have publication year 1989.
so: Eur* J* Mat* Soc* cc: 14 Search for publications in a particular source with a Mathematics Subject Classification code (cc) in 14.
"Partial diff* eq*" ! elliptic The not-operator ! eliminates all results containing the word elliptic.
dt: b & au: Hilbert The document type is set to books; alternatively: j for journal articles, a for book articles.
py: 2000-2015 cc: (94A | 11T) Number ranges are accepted. Terms can be grouped within (parentheses).
la: chinese Find documents in a given language. ISO 639-1 language codes can also be used.

Operators
a & b logic and
a | b logic or
!ab logic not
abc* right wildcard
"ab c" phrase
(ab c) parentheses
Fields
any anywhere an internal document identifier
au author, editor ai internal author identifier
ti title la language
so source ab review, abstract
py publication year rv reviewer
cc MSC code ut uncontrolled term
dt document type (j: journal article; b: book; a: book article)
Cellular particle swarm optimization. (English) Zbl 1242.68295
Summary: This paper proposes a cellular particle swarm optimization (CPSO), hybridizing cellular automata (CA) and particle swarm optimization (PSO) for function optimization. In the proposed CPSO, a mechanism of CA is integrated in the velocity update to modify the trajectories of particles to avoid being trapped in the local optimum. With two different ways of integration of CA and PSO, two versions of CPSO, i.e. CPSO-inner and CPSO-outer, have been discussed. For the former, we devised three typical lattice structures of CA used as neighborhood, enabling particles to interact inside the swarm; and for the latter, a novel CA strategy based on ”smart-cell” is designed, and particles employ the information from outside the swarm. Theoretical studies are made to analyze the convergence of CPSO, and numerical experiments are conducted to compare the proposed algorithm with different variants of PSO. According to the experimental results, the proposed method performs better than other variants of PSO on benchmark test functions.
MSC:
68T20AI problem solving (heuristics, search strategies, etc.)
68Q80Cellular automata (theory of computing)
90C59Approximation methods and heuristics
References:
[1]Abido, M. A.: Optimal design of power-system stabilizers using particle swarm optimization, IEEE transactions on energy conversion 17, No. 3, 406-413 (2002)
[2]P. Angeline, Using selection to improve particle swarm optimization, In: Proceedings of IEEE International Conference on Evolutionary Computation, 1998, pp. 84 – 89.
[3]Chang, J. -F.; Shi, P.: Using investment satisfaction capability index based particle swarm optimization to construct a stock portfolio, Information sciences 181, 2989-2999 (2011)
[4]Clerc, M.; Kennedy, J.: The particle swarm-explosion, stability, and convergence in a multidimensional complex space, IEEE transactions on evolutionary computation 6, 58-73 (2002)
[5]Du, W. L.; Li, B.: Multi-strategy ensemble particle swarm optimization for dynamic optimization, Information sciences 178, 3096-3109 (2008)
[6]El-Abd, M.; Kamel, M. S.: A cooperative particle swarm optimizer with migration of heterogeneous probabilistic models, Swarm intelligence 4, No. 1, 57-89 (2010)
[7]Fan, S. -K.S.; Zahara, E.: A hybrid simplex search and particle swarm optimization for unconstrained optimization, European journal of operational research 181, 527-548 (2007) · Zbl 1121.90116 · doi:10.1016/j.ejor.2006.06.034
[8]Gaing, Z. -L.: Particle swarm optimization to solving the economic dispatch considering the generator constraints, IEEE transactions on power systems 18, No. 3, 1187-1195 (2003)
[9]Header, A.; Fukushima, M.: Tabu search directed by direct search methods for nonlinear global optimization, European journal of operational research 170, 329-349 (2006) · Zbl 1093.90091 · doi:10.1016/j.ejor.2004.05.033
[10]N. Higashi, H. Iba, Particle swarm optimization with Gaussian mutation, In: Proceedings of IEEE Swarm Intelligence Symposium, 2003, pp. 72 – 79.
[11]Kathiravan, R.; Ganguli, R.: Strength design of composite beam using gradient and particle swarm optimization, Composite structures 81, 471-479 (2007)
[12]J. Kennedy, R.C. Eberhart, Particle swarm optimization, In: Proceedings of IEEE International Conference on Neural Networks, Perth, Australia, 1995, pp. 1942 – 1948.
[13]J. Kennedy, R.C. Eberhart, A discrete binary version of the particle swarm algorithm, In: Proceedings of IEEE Conference on Systems, Man, and Cybernetics, 1997, pp. 4104-4109.
[14]J. Kenndy, Small worlds and mega-minds: effects of neighborhood topology on particle swarm performance, In: Proceedings of IEEE Congress on Evolutionary Computation, 1999, pp. 1931 – 1938.
[15]J. Kennedy, R. Mendes, Population structure and particle swarm performance, In: Proceedings of IEEE Congress on Evolutionary Computation, 2002, pp. 1671 – 1676.
[16]Kutrib, M.: Cellular automata — a computational point of view, Studies in computational intelligence 113, 183-227 (2008) · Zbl 1156.68488
[17]A. Lazinica, Particle Swarm Optimization, IN-TECH, 2009.
[18]Li, X. D.: Niching without niching parameters: particle swarm optimization using a ring topology, IEEE transactions on evolutionary computation 14, No. 1, 150-169 (2010)
[19]Lian, Z. G.; Jiao, B.; Gu, X. S.: A similar particle swarm optimization algorithm for job-shop scheduling to minimize makespan, Applied mathematics and computation 182, No. 2, 1008-1017 (2006) · Zbl 1112.90029 · doi:10.1016/j.amc.2006.05.168
[20]Liang, J. J.; Qin, A. K.; Suganthan, P. N.; Baskar, S.: Comprehensive learning particle swarm optimizer for global optimization of multimodal functions, IEEE transactions on evolutionary computation 10, No. 3, 281-295 (2006)
[21]M. Løvbjerg, T.K. Rasmussen, T. Krink, Hybird particle swarm optimiser with breeding and subpopulations, In: Proceedings of the GECCO, 2001, pp. 469 – 476.
[22]Mendes, R.; Kennedy, J.; Neves, J.: The fully informed particle swarm: simpler, maybe better, IEEE transactions on evolutionary computation 8, 204-210 (2004)
[23]S. Nakano, A. Ishigame, K. Yasuda, Particle swarm optimization based on the concept of Tabu search, In: Proceedings of IEEE Congress on Evolutionary Computation, 2007, pp. 3258 – 3263.
[24]Ozcan, E.; Mohan, C. K.: Analysis of a simple particle swarm optimization system, Intelligent engineering systems through artificial neural networks 8, 253-258 (1998)
[25]E. Ozcan, C.K. Mohan, Particle swarm optimization: surfing the waves, In: Proceedings of IEEE Congress on Evolutionary Computation, 1999, pp. 1939 – 1944.
[26]Parsopoulos, K. E.; Vrahatis, M. N.: UPSO – a unified particle swarm optimization scheme, Lecture series on computational sciences, 868-873 (2004)
[27]T. Peram, K. Veeramachaneni, C.K. Mohan, Fitness – distance-ratio based particle swarm optimization, In: Proceedings of Swarm Intelligence Symposium, 2003, pp. 174 – 181.
[28]Rasmussen, T. K.; Krink, T.: Improved hidden Markov model training for multiple sequence alignment by a particle swarm optimization-evolutionary algorithm hybrid, Biosystems 72, 289-301 (2003)
[29]Schiff, J. L.: Cellular automata: A discrete view of the world, (2007)
[30]Sha, D. Y.; Hsu, C. Y.: A hybrid particle swarm optimization for job shop scheduling problem, Computers and industrial engineering 51, No. 4, 791-808 (2006)
[31]Y. Shi, R.C. Eberhart, A modified particle swarm optimizer, In: Proceedings of IEEE International Conference on Evolutionary Computation, Anchorage, Alaska,1998, pp. 66 – 73.
[32]Y. Shi, R.C. Eberhart, Empirical study of particle swarm optimization, In: Proceedings of the IEEE Congress on Evolutionary Computation, 1999, pp. 1945 – 1950.
[33]P.N. Suganthan, Particle swarm optimiser with neighborhood operator, In: Proceedings of IEEE Congress on Evolutionary Computation, 2002, pp. 1958 – 1961.
[34]Tripathi, P. K.; Bandyopadhyay, S.; Pal, S. K.: Multi-objective particle swarm optimization with time variant inertia and acceleration coefficients, Information sciences 177, 5033-5049 (2007) · Zbl 1121.90130 · doi:10.1016/j.ins.2007.06.018
[35]Den Bergh, F. Van; Engelbrecht, A. P.: A cooperative approach to particle swarm optimization, IEEE transactions on evolutionary computation 8, No. 3, 225-239 (2004)
[36]Den Bergh, F. Van; Engelbrecht, A. P.: A study of particle swarm optimization particle trajectories, Information sciences 176, 937-971 (2006) · Zbl 1093.68105 · doi:10.1016/j.ins.2005.02.003
[37]Vassiliadis, V.; Dounias, G.: Nature-inspired intelligence: a review of selected methods and applications, International journal on artificial intelligence tools 18, No. 4, 487-516 (2009)
[38]Wang, Y. J.; Yang, Y. P.: Particle swarm optimization with preference order ranking for multi-objective optimization, Information sciences 179, 1944-1959 (2009)
[39]Wolfram, S.: A new kind of science, (2002) · Zbl 1022.68084
[40]Yao, X.; Liu, Y.; Lin, G. M.: Evolutionary programming made faster, IEEE transactions on evolutionary computation 3, No. 1, 82-102 (1999)
[41]Yin, P. Y.; Glover, F.; Laguna, M.; Zhu, J. X.: Cyber swarm algorithms – improving particle swarm optimization using adaptive memory strategies, European journal of operational research 201, No. 2, 377-389 (2010) · Zbl 1175.90433 · doi:10.1016/j.ejor.2009.03.035