More Web Proxy on the site http://driver.im/

research-article

Self regulating particle swarm optimization algorithm

Authors:

N. SundararajanAuthors Info & Claims

Information Sciences—Informatics and Computer Science, Intelligent Systems, Applications: An International Journal, Volume 294, Issue C

Pages 182 - 202

https://doi.org/10.1016/j.ins.2014.09.053

Published: 10 February 2015 Publication History

Abstract

In this paper, we propose a new particle swarm optimization algorithm incorporating the best human learning strategies for finding the optimum solution, referred to as a Self Regulating Particle Swarm Optimization (SRPSO) algorithm. Studies in human cognitive psychology have indicated that the best planners regulate their strategies with respect to the current state and their perception of the best experiences from others. Using these ideas, we propose two learning strategies for the PSO algorithm. The first one uses a self-regulating inertia weight and the second uses the self-perception on the global search direction. The self-regulating inertia weight is employed by the best particle for better exploration and the self-perception of the global search direction is employed by the rest of the particles for intelligent exploitation of the solution space. SRPSO algorithm has been evaluated using the 25 benchmark functions from CEC2005 and a real-world problem for a radar system design. The results have been compared with six state-of-the-art PSO variants like Bare Bones PSO (BBPSO), Comprehensive Learning PSO (CLPSO), etc. The two proposed learning strategies help SRPSO to achieve faster convergence and provide better solutions in most of the problems. Further, a statistical analysis on performance evaluation of the different algorithms on CEC2005 problems indicates that SRPSO is better than other algorithms with a 95% confidence level.

References

[1]

A. Banks, J. Vincent, C. Anyakoha, A review of particle swarm optimization, Part II: Hybridization, combinatorial, multicriteria and constrained optimization and indicative applications, Nat. Comput., 7 (2008).

Digital Library

[2]

B. Alatas, E. Akin, A.B. Ozer, Chaos embedded particle swarm optimization algorithms, Chaos Solit. Fract., 40 (2009) 1715-1734.

[3]

P. Bajpai, S.N. Singh, Fuzzy adaptive particle swarm optimization for bidding strategy in uniform price spot market, IEEE Trans. Power Syst., 22 (2007) 2152-2160.

[4]

G.Q. Bao, K.F. Mao, Particle swarm optimization algorithm with asymmetric time-varying acceleration coefficients, in: Proc. of IEEE International Conference on robotics and biomimetics, 2009, pp. 2134-2139.

Digital Library

[5]

Z. Beheshti, S.M. Hj Shamsuddin, CAPSO: centripetal accelerated particle swarm optimization, Inform. Sci., 258 (2014) 54-79.

Digital Library

[6]

F.V.D. Bergh, Engelbrecht, P. Andries, A cooperative approach to particle swarm optimization, IEEE Trans. Evol. Comput., 8 (2004) 225-239.

Digital Library

[7]

J.G. Borkowski, M. Carr, E. Rellinger, M. Pressley, Self-Regulated Cognition: Interdependence of Metacognition, Dimensions of Thinking and Cognitive Instruction, 2013, p. 53.

[8]

L. Cagnina, M. Errecalde, D. Ingaramo, P. Rosso, An efficient particle swarm optimization approach to cluster short texts, Inform. Sci., 265 (2014) 36-49.

Digital Library

[9]

S. Chatterjee, D. Goswami, S. Mukherjee, S. Das, Behavioral analysis of the leader particle during stagnation in a particle swarm optimization algorithm, Inform. Sci., 279 (2014) 18-36.

[10]

W.N. Chen, J. Zhang, Y. Lin, N. Chen, Z.H. Zhan, H.S.H. Chung, Y. Li, Y.H. Shi, Particle swarm optimization with an aging leader and challengers, IEEE Trans. Evol. Comput., 17 (2013) 241-258.

Digital Library

[11]

X. Chu, M. Hu, T. Wu, J.D. Weir, Q. Lu, AHPS2: an optimizer using adaptive heterogeneous particle swarms, Inform. Sci., 280 (2014) 26-52.

[12]

M. Clerc, J. Kennedy, The particle swarm - explosion, stability, and convergence in a multidimensional complex space, IEEE Trans. Evol. Comput., 6 (2002) 58-73.

Digital Library

[13]

Y. del Valle, G.K. Venayagamoorthy, S. Mohagheghi, J.C. Hernandez, R.G. Harley, Particle swarm optimization: basic concepts, variants and applications in power systems, IEEE Trans. Evol. Comput., 12 (2008) 171-195.

Digital Library

[14]

J. Demšar, Statistical comparisons of classifiers over multiple data sets, Machine Learn. Res., 7 (2006) 1-30.

Digital Library

[15]

J. Ding, J. Liu, K.R. Chowdhury, W. Zhang, Q. Hu, J. Lei, A particle swarm optimization using local stochastic search and enhancing diversity for continuous optimization, Neurocomputing, 137 (2014) 261-267.

[16]

M. Dukic, Z. Dobrosavljevic, A method of spread-spectrum radar polyphase code design, IEEE J. Select. Areas Commun., 8 (1990) 748-749.

Digital Library

[17]

R.C. Eberhart, Y. Shi, Particle swarm optimization: developments, applications and resources, in: Proc. of Cong. on Evolutionary Computation, 2001, pp. 81-86.

[18]

Engelbrecht, P. Andries, Computational Intelligence: An Introduction, John Wiley and Sons, 2007.

Digital Library

[19]

M.G. Epitropakis, V.P. Plagianakos, M.N. Vrahatis, Evolving cognitive and social experience in particle swarm optimization through differential evolution: a hybrid approach, Inform. Sci., 216 (2012) 50-92.

Digital Library

[20]

M. Eslami, H. Shareef, M. Khajehzadeh, A. Mohamed, A survey of the state of the art in particle swarm optimization, Res. J. Appl. Sci. Eng. Technol., 4 (2012) 1181-1197.

[21]

L. Guo, X. Chen, A novel particle swarm optimization based on the self-adaptation strategy of acceleration coefficients, in: Proc. of the IEEE Int. Conf. on Computational Intelligence and Security, 2009, pp. 277-281.

Digital Library

[22]

H. Hakli, H. Ug¿uz, A novel particle swarm optimization algorithm with levy flight, Appl. Soft Comput. (2014).

Digital Library

[23]

W. Han, P. Yang, H. Ren, J. Sun, Comparison study of several kinds of inertia weight for PSO, in: Proc. of the IEEE Int. Conf. on Progress in Informatics and Computing, 2010, pp. 280-284.

[24]

W.A. Hassan, M.B. Fayek, S.I. Shaheen, PSOSA: an optimised particle swarm technique for solving the urban planning problem, in: Proc. of the IEEE Int. Conf. of Computer Engineering and Systems, 2006, pp. 401-405.

[25]

N. Higashi, H. Iba, Particle swarm optimization with Gaussian mutation, in: Proc. of IEEE SIS, 2003, pp. 72-79.

[26]

A.K. Husseinzadeh, M.K. Husseinzadeh, S. Karimiyan, A particle swarm optimizer for grouping problems, Inform. Sci., 252 (2013) 81-95.

Digital Library

[27]

S. Janson, M. Middendorf, A hierarchical particle swarm optimizer and its adaptive variant, IEEE Trans. Syst. Man Cybernet. Part B: Cybernet., 35 (2005) 1272-1282.

Digital Library

[28]

W. Jianxin, H.X. Xin, Z. Weiguo, W. Rui, Exponential inertia weight particle swarm algorithm for dynamic optimization of electromechanical coupling system, in: Proc. of the IEEE Int. Conf. on Intelligent Computing and Intelligent Systems, 2009, pp. 479-483.

[29]

J. Kennedy, Small worlds and mega-minds: effects of neighborhood topology on particle swarm performance, in: Proc. of Cong. on Evolutionary Computation, 1999.

[30]

J. Kennedy, Bare bones particle swarms, in: Proc. of IEEE SIS, 2003, pp. 80-87.

[31]

J. Kennedy, R.C. Eberhart, Particle swarm optimization, in: Proc. of IEEE Int. Conf. on Neural Networks, 1995, pp. 1942-1948.

[32]

J. Kennedy, R. Mendes, Population structure and particle swarm performance, in: Proc. of Cong. on Evolutionary Computation, 2002, pp. 1671-1676.

Digital Library

[33]

M.S. Leu, M.F. Yeh, Grey particle swarm optimization, Appl. Soft Comput., 12 (2012) 2985-2996.

Digital Library

[34]

C. Li, S. Yang, T.T. Nguyen, A self-learning particle swarm optimizer for global optimization problems, IEEE Trans. Syst. Man Cybernet. Part B: Cybernet., 42 (2012) 627-646.

Digital Library

[35]

L. Li, B. Xue, B. Niu, Y. Chai, J. Wu, The novel nonlinear strategy of inertia weight in particle swarm optimization, in: Proc. of the IEEE Int. Conf. on Bio-inspired computation, 2009, pp. 1-5.

[36]

J.J. Liang, A.K. Qin, P.N. Suganthan, S. Baskar, Comprehensive learning particle swarm optimizer for global optimization of multimodal functions, IEEE Trans. Evol. Comput., 10 (2006) 281-295.

Digital Library

[37]

J.J. Liang, P.N. Suganthan, Dynamic multi-swarm particle swarm optimizer, in: Proc. of IEEE SIS, 2005, pp. 210-224.

[38]

W.H. Lim, N.A.M. Isa, An adaptive two-layer particle swarm optimization with elitist learning strategy, Inform. Sci., 273 (2014) 49-72.

[39]

W.H. Lim, N.A.M. Isa, Bidirectional teaching and peer-learning particle swarm optimization, Inform. Sci., 280 (2014) 111-134.

[40]

W.H. Lim, N.A.M. Isa, Teaching and peer-learning particle swarm optimization, Appl. Soft Comput., 18 (2014) 39-58.

Digital Library

[41]

G.Y Lin, D.Y. Hong, A new particle swarm optimization algorithm with random inertia weight and evolution strategy, in: Proc. of the IEEE Int. Conf. on CIS, 2007, pp. 199-203.

Digital Library

[42]

C. Liu, C. Ouyang, P. Zhu, W. Tang, An adaptive fuzzy weight PSO algorithm, in: Proc. of the IEEE Conf. on Genetic and Evolutionary Computation, 2010, pp. 8-10.

Digital Library

[43]

M. Lovbjerg, T.K. Rasmussen, T. Krink, Hybrid particle swarm optimizer with breeding and subpopulations, in: Proc. of the IEEE Int. Conf. on Genetic and Evolutionary Computation, 2001, pp. 469-476.

[44]

M.J. Mahmoodabadi, Z.M. Salahshoor, A. Bagheri, HEPSO: high exploration particle swarm optimization, Inform. Sci., 273 (2014) 101-111.

[45]

R. Mendes, J. Kennedy, J. Neves, The fully informed particle swarm: simpler, maybe better, IEEE Trans. Evol. Comput., 8 (2004) 204-210.

Digital Library

[46]

G. Napoles, I. Grau, R. Bello, Particle swarm optimization with random sampling in variable neighbourhoods for solving global minimization problems, in: Swarm Intelligence, Springer, Berlin, Heidelberg, 2012, pp. 352-353.

Digital Library

[47]

T.O. Nelson, L. Narens, Metamemory: a theoretical framework and new findings, Psychol. Learn. Motivation, 26 (1990) 125-141.

[48]

F. Neri, E. Mininno, G. Iacca, Compact particle swarm optimization, Inform. Sci., 239 (2013) 96-121.

Digital Library

[49]

E. Ozcan, C. Mohan, Particle swarm optimization: surfing the waves, in: Proc. of Cong. on Evolutionary Computation, 1999, pp. 1939-1944.

[50]

M. Pant, T. Radha, V.P. Singh, Particle swarm optimization using Gaussian inertia weight, in: Proc. of the IEEE Int. Conf. on Computational Intelligence and Multimedia, 2007, pp. 97-102.

Digital Library

[51]

D. Parrott, X. Li, Locating and tracking multiple dynamic optima by a particle swarm model using speciation, IEEE Trans. Evol. Comput., 10 (2006) 440-458.

Digital Library

[52]

K.E. Parsopoulos, M.N. Vrahatis, Empirical study of particle swarm optimizer with an increasing inertia weight, in: Proc. of Cong. on Evolutionary Computation, 2004, pp. 868-873.

[53]

K.E. Parsopoulos, M.N. Vrahatis, On the computation of all global minimizers through particle swarm optimization, IEEE Trans. Evol. Comput., 8 (2004) 211-224.

Digital Library

[54]

K.E. Parsopoulos, M.N. Vrahatis, UPSO: a unified particle swarm optimization scheme, in: Lecture Series on Computer and Computational Sciences: Proc. of Int. Conf. of Computational Methods in Sciences and Engineering, 2004, pp. 868-873.

[55]

T. Peram, K. Veeramachaneni, C.K. Mohan, Fitness-distance-ratio based particle swarm optimization, in: Proc. of IEEE SIS, 2003, pp. 174-181.

[56]

A.M. Perez-Bellido, S. Salcedo-Sanz, E.G. Ortiz-Garcia, A. Portilla-Figueras, A hybrid evolutionary programming algorithm for spread spectrum radar polyphase codes design, in: Proc. of the 9th Annual Conf. on Genetic and Evolutionary Computation, 2007, pp. 682-688.

Digital Library

[57]

R. Poli, Analysis of the publications on the applications of particle swarm optimization, Artif. Evol. Appl., 28 (2008) 1-10.

[58]

B.Y. Qu, P.N. Suganthan, S. Das, Distance-based locally informed particle swarm model for multimodal optimization, IEEE Trans. Evol. Comput., 17 (2013) 387-402.

Digital Library

[59]

A. Ratnaweera, S.K. Halgamuge, H.C. Watson, Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients, IEEE Trans. Evol. Comput., 8 (2004) 240-255.

Digital Library

[60]

Y. Shi, R.C. Eberhart, A modified particle swarm optimizer, in: Proc. of WCCI, 1998, pp. 69-73.

[61]

Y. Shi, R.C. Eberhart, Empirical study of particle swarm optimization, in: Proc. of Cong. on Evolutionary Computation, 1999, pp. 1945-1950.

[62]

Y. Shi, H. Liu, L. Gao, G. Zhang, Cellular particle swarm optimization, Inform. Sci., 181 (2013) 4460-4493.

Digital Library

[63]

Y. Song, Z. Chen, Z. Yuan, New chaotic PSO-based neural network predictive control for nonlinear process, IEEE Trans. Neural Netw., 18 (2007) 595-601.

Digital Library

[64]

P.N. Suganthan, N. Hansen, J.J. Liang, Y.P. Chen, K. Deb, A. Auger, S. Tiwari, Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization, Technical Report: Nanyang Technological University AND KanGAL Report (2005005), 2005.

[65]

C. Sun, J. Zeng, J. Pan, S. Xue, Y. Jin, A new fitness estimation strategy for particle swarm optimization, Inform. Sci., 221 (2013) 355-370.

Digital Library

[66]

J. Sun, B.W. Xu, W. Fang, Diversity-guided quantum-behaved particle swarm optimization algorithm, in: Proc. of Int. Conf. on Simulated Evolution and Learning, 2006, pp. 497-504.

Digital Library

[67]

S. Suresh, K. Dong, J.H. Kim, A sequential learning algorithm for self-adaptive resource allocation network classifier, Neurocomputing, 73 (2010) 3012-3019.

Digital Library

[68]

S. Suresh, R. Savitha, N. Sundararajan, A sequential learning algorithm for complex-valued self-regulating resource allocation network-CSRAN, IEEE Trans. Neural Netw., 22 (2011) 1061-1072.

Digital Library

[69]

S. Suresh, P.B. Sujit, A.K. Rao, Particle swarm optimization approach for multi-objective composite box-beam design, Compos. Struct., 81 (2007) 598-605.

[70]

M.R. Tanweer, S. Suresh, Human cognition inspired particle swarm optimization algorithm, in: IEEE 9th Int. Conf. on Intelligent Sensors, Sensor Networks and Information Processing, 2014, pp. 1-6.

[71]

R. Thangaraj, M. Pant, A. Abraham, P. Bouvry, Particle swarm optimization: hybridization perspectives and experimental illustrations, Appl. Math. Comput., 217 (2011) 5208-5226.

[72]

H. Wang, H. Sun, C. Li, S. Rahnamayan, J.S. Pan, Diversity enhanced particle swarm optimization with neighborhood search, Inform. Sci., 223 (2013) 119-135.

Digital Library

[73]

H. Wang, Z. Wu, S. Rahnamayan, C. Li, S. Zeng, D. Jiangand, Particle swarm optimization with simple and efficient neighbourhood search strategies, Int. J. Innovat. Comput. Appl., 3 (2011) 97-104.

Digital Library

[74]

L. Wang, B. Yang, Y. Chen, Improving particle swarm optimization using multi-layer searching strategy, Inform. Sci., 274 (2014) 70-94.

[75]

X. Zhao, Z. Liu, X. Yang, A multi-swarm cooperative multistage perturbation guiding particle swarm optimizer, Appl. Soft Comput., 22 (2014) 77-93.

Digital Library

[76]

D. Yazdani, B. Nasiri, S.M. Alireza, M.R. Meybodi, A novel multi-swarm algorithm for optimization in dynamic environments based on particle swarm optimization, Appl. Soft Comput., 13 (2013) 77-93.

Digital Library

[77]

X. Yu, X. Zhang, Enhanced comprehensive learning particle swarm optimization, Appl. Math. Comput., 242 (2014) 265-276.

[78]

C. Zeng, A particle swarm optimization algorithm with rich social cognition, in: IEEE 5th International Conference on Natural Computation, 2009, pp. 186-189.

Digital Library

[79]

Z.H. Zhan, J. Zhang, Y. Li, H.S.H. Chung, Adaptive particle swarm optimization, IEEE Trans. Syst. Man Cybernet. Part B: Cybernet., 39 (2009) 1362-1381.

Digital Library

[80]

W.J. Zhang, X.F. Xie, DEPSO: hybrid particle swarm with differential evolution operator, in: IEEE Int. Conf. Systems, Man, and Cybernetics, 2003, pp. 3816-3821.

[81]

Y.L. Zheng, L.H. Ma, L.Y. Zhang, J.X. Qian, A unified particle swarm optimization scheme, in: Proc. of the IEEE Int. Conf. of Computational Methods in Sciences and Engineering, 2003, pp. 221-226.

[82]

Z. Zhi-Hui, Z. Jun, L. Yun, S. Yu-Hui, Orthogonal learning particle swarm optimization, IEEE Trans. Evol. Comput., 15 (2011) 832-847.

[83]

T. Ziyu, Z. Dingxue, A modified particle swarm optimization with adaptive acceleration coefficients, in: Proc. of the IEEE Int. Conf. on Information Processing, 2009, pp. 330-332.

Digital Library

[84]

F. Zou, L. Wang, X. Hei, D. Chen, D. Yang, Teaching-learning-based optimization with dynamic group strategy for global optimization, Inform. Sci., 273 (2014) 112-131.

Cited By

Zhang CLiu MZhong PYang SLiang ZSong Q(2024)Optimization of Biped Robot Walking Based on the Improved Particle Swarm AlgorithmInternational Journal of Intelligent Systems10.1155/2024/66890712024Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1155/2024/6689071
Ma ZChen JGuo HMa YGong YLi XHandl J(2024)Auto-configuring Exploration-Exploitation Tradeoff in Evolutionary Computation via Deep Reinforcement LearningProceedings of the Genetic and Evolutionary Computation Conference10.1145/3638529.3653996(1497-1505)Online publication date: 14-Jul-2024
https://dl.acm.org/doi/10.1145/3638529.3653996
Wang LTian DGou XShi Z(2024)Hybrid particle swarm optimization with adaptive learning strategySoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-024-09814-928:17-18(9759-9784)Online publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1007/s00500-024-09814-9
Show More Cited By

Self regulating particle swarm optimization algorithm
1. Information systems
  1. Data management systems
    1. Database administration
2. Theory of computation
  1. Design and analysis of algorithms

Recommendations

Self-Regulating and Self-Perception Particle Swarm Optimization with Mutation Mechanism
Abstract
Particle swarm optimization (PSO) is widely used to solve various optimization problems, such as robotics visual perception and intelligent control under uncertainties, due to its simple rules and easy implementation. However, the PSO has ...
An improved cooperative quantum-behaved particle swarm optimization

Particle swarm optimization (PSO) is a population-based stochastic optimization. Its parameters are easy to control, and it operates easily. But, the particle swarm optimization is a local convergence algorithm. Quantum-behaved particle swarm ...
An enhanced particle swarm optimization with levy flight for global optimization

Enhanced PSO with levy flight.Random walk of the particles.High convergence rate.Provides solution accuracy and robust. Hüseyin Haklı and Harun Uguz (2014) proposed a novel approach for global function optimization using particle swarm optimization with ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image Information Sciences: an International Journal

Information Sciences: an International Journal Volume 294, Issue C

February 2015

683 pages

ISSN:0020-0255

Issue’s Table of Contents

Copyright © Elsevier Inc.

Publisher

Elsevier Science Inc.

United States

Publication History

Published: 10 February 2015

Author Tags

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

68
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 24 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Zhang CLiu MZhong PYang SLiang ZSong Q(2024)Optimization of Biped Robot Walking Based on the Improved Particle Swarm AlgorithmInternational Journal of Intelligent Systems10.1155/2024/66890712024Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1155/2024/6689071
Ma ZChen JGuo HMa YGong YLi XHandl J(2024)Auto-configuring Exploration-Exploitation Tradeoff in Evolutionary Computation via Deep Reinforcement LearningProceedings of the Genetic and Evolutionary Computation Conference10.1145/3638529.3653996(1497-1505)Online publication date: 14-Jul-2024
https://dl.acm.org/doi/10.1145/3638529.3653996
Wang LTian DGou XShi Z(2024)Hybrid particle swarm optimization with adaptive learning strategySoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-024-09814-928:17-18(9759-9784)Online publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1007/s00500-024-09814-9
Wang RHao KChen LLiu XZhu XZhao C(2024)A modified hybrid particle swarm optimization based on comprehensive learning and dynamic multi-swarm strategySoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-023-09332-028:5(3879-3903)Online publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1007/s00500-023-09332-0
You QSun JPalade VPan F(2023)Quantum-behaved particle swarm optimization with dynamic grouping searching strategyIntelligent Data Analysis10.3233/IDA-22675327:3(769-789)Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.3233/IDA-226753
Xia XSong HZhang YGui LXu XLi KLi Y(2023)A Particle Swarm Optimization With Adaptive Learning Weights Tuned by a Multiple-Input Multiple-Output Fuzzy Logic ControllerIEEE Transactions on Fuzzy Systems10.1109/TFUZZ.2022.322746431:7(2464-2478)Online publication date: 1-Jul-2023
https://dl.acm.org/doi/10.1109/TFUZZ.2022.3227464
Wang YWang ZWang G(2023)Hierarchical learning particle swarm optimization using fuzzy logicExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.120759232:COnline publication date: 1-Dec-2023
https://dl.acm.org/doi/10.1016/j.eswa.2023.120759
Zhang CLiu MZhong PSong QLiang ZZhang ZWang X(2023)An adaptive balance optimization algorithm and its engineering applicationAdvanced Engineering Informatics10.1016/j.aei.2023.10190855:COnline publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1016/j.aei.2023.101908
Darekar RChavan MSharanyaa SRanjan N(2023)A hybrid meta-heuristic ensemble based classification technique speech emotion recognitionAdvances in Engineering Software10.1016/j.advengsoft.2023.103412180:COnline publication date: 1-Jun-2023
https://dl.acm.org/doi/10.1016/j.advengsoft.2023.103412
Cao HZheng HHu G(2023)An improved cooperation search algorithm for the multi-degree reduction in Ball Bézier surfacesSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-023-07847-027:16(11687-11714)Online publication date: 11-Feb-2023
https://dl.acm.org/doi/10.1007/s00500-023-07847-0
Show More Cited By

View Options

View options

Figures

Tables

Media

View Issue’s Table of Contents