Abstract
Cooperation search algorithm (CSA) is a new metaheuristic algorithm inspired from the team cooperation behaviors in modern enterprises and is characterized by fast convergence. However, for complex multimodal problems, it may get trapped into local optima and suffer from premature convergence for the shortcoming of population updating guided only by leading individuals. In this paper, the issue of low convergence efficiency and convergence accuracy of the CSA algorithm on complex multimodal problems is dramatically alleviated by integrating the mutation and crossover operators in DE algorithm. Experimental results demonstrate the better performance of CCSA on convergence speed and accuracy as compared to other existing optimizers. Furthermore, aiming at the problem that there is no universal approach for the multi-degree reduction in Ball Bézier surfaces under different interpolation constrains, we propose a new method to solve this problem by introducing metaheuristic methods, where the change of interpolation constrains is treated as the change of decision variables. The modeling examples show that the proposed method is effective and easy to implement under different interpolation constrains, which can achieve the multi-degree reduction in Ball Bézier surfaces at one time and can simplify the degree reduction procedure significantly.
Similar content being viewed by others
Data availability
The authors confirm that the data supporting the findings of this study are available within the article.
References
Awad N.H, Ali M.Z, Liang J.J, Qu B.Y, Suganthan P.N (2016) Problem definitions and evaluation criteria for the CEC 2017 special session and competition on single objective bound constrained real-Parameter numerical optimization. Technical report, Nanyang Technological University Singapore,
Chen Y, Wu H, Deng J (2007) Degree reduction of ball-control-point Bézier surfaces over triangular domain. J. Univ. Sci. Technol. Chin. 37:777–784 ((in Chinese))
Chen H, Yang C, Heidari AA, Zhao X (2020) An efficient double adaptive random spare reinforced whale optimization algorithm. Expert Syst. Appl. 154:113018
Das S, Suganthan PN (2011) Differential evolution: a survey of the state-of-the-art. IEEE Trans. Evol. Comput. 15:4–31
Eberhart R, Kennedy J(1995) A new optimizer using particle swarm theory, in: Proceedings of the Sixth International Symposium on Micro Machine and Human Science, IEEE, 39–43
Elaziz MA, Oliva D, Xiong S (2017) An improved opposition-based sine cosine algorithm for global optimization. Expert Syst. Appl. 90:484–500
Emami H, Emami S, Parsa J (2022) A Walnut optimization algorithm applied to discharge coefficient prediction on labyrinth weirs. Soft Comput. 26:12197–12215
Fausto F, Cuevas E, Valdivia A, González A (2017) A global optimization algorithm inspired in the behavior of selfish herds. Biosystems. 160:39–55
Feng Z, Niu W, Liu S (2021) Cooperation search algorithm: A novel metaheuristic evolutionary intelligence algorithm for numerical optimization and engineering optimization problems. Appl. Soft Comput. 98:106734
Fu Q, Wu Z, Wang X, Zhou M, Zheng J, Wang X et al (2018) An algorithm for finding intersection between ball B-spline curves. J. Comput. Appl. Math. 327:260–273
Gan J, Xie X, Zhai Y, He G et al (2022) Facial beauty prediction fusing transfer learning and broad learning system. Soft Computing. https://doi.org/10.1007/s00500-022-07563-1
Ghaemi M, Feizi-Derakhshi MR (2014) Forest optimization algorithm. Expert Syst. Appl. 41:6676–6687
Guo W, Wang Y, Dai F, Xu P (2020) Improved sine cosine algorithm combined with optimal neighborhood and quadratic interpolation strategy. Eng. Appl. Artif. Intel. 94:103779
Gupta S, Deep K (2020) A memory-based grey wolf optimizer for global optimization tasks. Appl. Soft Comput. 93:106367
He W, Qi X, Liu L (2021) A novel hybrid particle swarm optimization for multi-UAV cooperate path planning. Applied intelligence. 51:7350–7364
Holland JH (1975) Adaptation in natural and artificial systems. University of Michigan Press. Ann Arbor, MI
Hu H, Zhang L, Bai Y, Wang P, Tan X (2019) A hybrid algorithm based on squirrel search algorithm and invasive weed optimization for optimization. IEEE Access. 7:105652–105668
Huan T, Kulkarni A, Kanesan J, Huang C, Abraham A (2017) Ideology algorithm: a socio-inspired optimization methodology. Neural Comput. Appl. 28:845–876
Hu Q, Wang G (2008) Exact boundary of ball Bézier surface and its approximation by polynomial form. J. Zhejiang. Univ. (Eng. Sci.) 42 , 1906-1909, (in Chinese)
Hu G, Zhu X, Wei G, Chang C.-T (2021)An improved marine predators algorithm for shape optimization of developable Ball surfaces. Eng. Appl. Artif. Intel. 105 , 104417
Ibrahim RA, Elaziz MA, Lu S (2018) Chaotic opposition-based grey-wolf optimization algorithm based on differential evolution and disruption operator for global optimization. Expert Syst. Appl. 108:1–27
Jahani E, Chizari M (2017) Tackling global optimization problems with a novel algorithm-mouth brooding fish algorithm. Appl. Soft Comput. 62:987–1002
Jain M, Singh V, Rani A (2019) A novel nature-inspired algorithm for optimization: Squirrel search algorithm. Swarm Evol. Comput. 44:148–175
Juhász I, Róth Á (2019) Adjusting the energies of curves defined by control points. Comput.-Aided Des. 107 , 77-88
Khattab H, Sharieh A, Mahafzah BA (2019) Most valuable player algorithm for solving minimum vertex cover problem. Int. J. Adv. Comput. Sci. Appl. 10:159–167
Lam A, Li V (2010) Chemical-reaction-inspired metaheuristic for optimization. IEEE Trans. Evol. Comput. 14:381–399
Leng C, Wu Z, Zhou M (2011) Reconstruction of tubular object with ball b-spline curve. In: Proceedings of Computer Graphics International
Liu H, Deng J (2008) Fitting scattered data with Disk/Ball Bézier and B-Spline curves/surfaces. J. Univ. Sci. Technol. Chin. 38:113–120 ((in Chinese))
Liu X, Wang X, Wu Z, Zhang D, Liu X (2020) Extending Ball B-spline by B-spline. Comput. Aided Geom. Design. 82:101926
Mehrabian AR, Lucas C (2006) A novel numerical optimization algorithm inspired from weed colonization. Ecol. Inform. 1:355–366
Mirjalili S (2016) A sine cosine algorithm for solving optimization problems. Knowl.-Based Syst. 96 , 120-133
Mirjalili S, Mirjalili SM, Hatamlou A (2016) Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Comput. Appl. 27:495–513
Mirjalili S, Gandomi AH, Mirjalili SZ, Saremi S, Faris H, Mirjalili SM (2017) Salp swarm algorithm: A bio-inspired optimizer for engineering design problems. Adv. Eng. Softw. 114:163–191
Mirjalili S, Mirjalili S.M(2014) A. Lewis, Grey wolf optimizer. Adv. Eng. Softw. 69, 46-61
Molina D, Poyatos J, Ser JD, García S, Hussain A, Herrera F (2020) Comprehensive taxonomies of nature-and bio-inspired optimization: inspiration versus algorithmic behavior, critical analysis and recommendations. Cognitive computation. 12:897–939
Nedic N, Stojanovic V, Djordjevic V et al (2015) Optimal control of hydraulically driven parallel robot platform based on firefly algorithm. Nonlinear Dyn. 82:1457–1473
Nematollahi AF, Rahiminejad A, Vahidi B (2017) A novel physical based meta-heuristic optimization method known as Lightning Attachment Procedure Optimization. Appl. Soft Comput. 59:596–621
Nenavath H, Kumar RJ, Das S (2018) A synergy of the sine-cosine algorithm and particle swarm optimizer for improved global optimization and object tracking. Swarm Evol. Comput. 43:1–30
Pasko A, Adzhiev V, Comninos P (2008) Heterogeneous Objects Modelling and Applications: Collection of Papers on Foundations and Practice. Springer 4889
Punnathanam V, Kotecha P (2016) Yin-Yang-pair optimization: A novel lightweight optimization algorithm. Eng. Appl. Artif. Intel. 54:62–79
Rashedi E, Nezamabadi-Pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf. Sci. 179:2232–2248
Ruidas S, Seikh MR, Nayak PK, Sarkar B (2019) A single period production inventory model in interval environment with price revision. Int. J. Appl. Comput. Math. 5:7
Ruidas S, Seikh MR, Nayak PK (2021) A production inventory model with interval-valued carbon emission parameters under price-sensitive demand. Comput. Ind. Eng. 154:107154
Ruidas S, Seikh M.R, Nayak P.K (2022) Application of particle swarm optimization technique in an interval-valued EPQ model. Meta-heuristic optimization techniques,
Saxena MA, Kumar R, Das S (2019) \(\beta \)-chaotic map enabled grey wolf optimizer. Appl. Soft Comput. 75:84–105
Song Y, Yang Z, Liu Y, Deng J (2018) Function representation based slicer for 3D printing. Comput. Aided Geom. Design. 62:276–293
Stojanovic V, Nedic N (2016) Robust identification of OE model with constrained output using optimal input design. J. Franklin I(353):576–593
Stojanovic V, Nedic N (2016) A nature inspired parameter tuning approach to cascade control for hydraulically driven parallel robot platform. J. Optim. Theory Appl. 168:332–347
Tanweer MR, Suresh S, Sundararajan N (2015) Self regulating particle swarm optimization algorithm. Inform. Sci. 294:182–202
Wang H, Liu Y, Zeng S (2007) Opposition-based particle swarm algorithm with Cauchy mutation. IEEE congress on evolutionary computation 4425095:4750–4756
Wang X, Wu Z, Shen J et al (2016) Repairing the cerebral vascular through blending Ball B-Spline curves with \(G^{2}\) continuity. Neurocomputing. 173:768–777
Wang F, Zhang H, Li K et al (2018) A hybrid particle swarm optimization algorithm using adaptive learning strategy. Inform. Sci. 436:162–177
Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans. Evol. Comput. 1:67–82
Wu H, Deng J (2006) Degree reduction of Bézier surfaces with ball control points. J. Univ. Sci. Technol. China. 36 , 582-589,(in Chinese)
Wu Z, Seah H, Zhou M (2007) Skeleton based parametric solid models: Ball B-Spline curves. In: 2007 10th IEEE International Conference on Computer-Aided Design and Computer Graphics, 421-424
Wu Z, Zhou M, Wang X, et al. (2007) An interactive system of modeling 3D trees with ball b-spline curves. In: 2007 10th IEEE International Conference on Computer-Aided Design and Computer Graphics. , 259-265
Xu X, Leng C, Wu Z(2011) Rapid 3D human modeling and animation based on sketch and motion database. In: 2011 Workshop on Digital Media and Digital Content Management (DMDCM), 121-124
Yang X, Deb S (2009) Cuckoo search via Lévy flights. In: World Congress on Nature Biologically Inspired Computing. NaBIC , 210-214
Yazdani R, Alipour-Vaezi M, Kabirifar K, Kojour AS, Soleimani F (2022) A lion optimization algorithm for an integrating maintenance planning and production scheduling problem with a total absolute deviation of completion times objective. Soft Comput. 26:13953–13968
Zhu T, Tian F, Zhou Y et al (2008) Plant modeling based on 3D reconstruction and its application in digital museum. Int. J. Virt. Real. 7:81–88
Funding
This work is funded by the National Natural Science Foundation of China (Grant No. 51875454).
Author information
Authors and Affiliations
Contributions
We confirm that this manuscript has not been published elsewhere and is not under consideration by another journal. All authors have approved the manuscript and agree with its submission to Soft Computing.
Corresponding author
Ethics declarations
Conflict of interest
All author states that there is no conflict of interest.
Ethical approval
Humans/Animals are not involved in this work.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Cao, H., Zheng, H. & Hu, G. An improved cooperation search algorithm for the multi-degree reduction in Ball Bézier surfaces. Soft Comput 27, 11687–11714 (2023). https://doi.org/10.1007/s00500-023-07847-0
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00500-023-07847-0