A Simple Butterfly Particle Swarm Optimization Algorithm with the Fitness-based Adaptive Inertia Weight and the Opposition-based Learning Average Elite Strategy

• Autorzy: Tong L. , Dong M. , Ai B. , Jing C.

Identyfikatory

DOI

10.3233/FI-2018-1738

• Języki publikacji: EN

Abstrakty

EN

Particle swarm optimization (PSO) is a population-based stochastic optimization technique that can be applied to solve optimization problems. However, there are some defects for PSO, such as easily trapping into local optimum, slow velocity of convergence. This paper presents the simple butterfly particle swarm optimization algorithm with the fitness-based adaptive inertia weight and the opposition-based learning average elite strategy (SBPSO) to accelerate convergence speed and jump out of local optimum. SBPSO has the advantages of the simple butterfly particle swarm optimizer to increase the probability of finding the global optimum in the course of searching. Moreover, SBPSO benefits from the simple particle swarm (sPSO) to accelerate convergence speed. Furthermore, SBPSO adopts the opposition-based learning average elite to enhance the diversity of the particles in order to jump out of local optimum. Additionally, SBPSO generates the fitness-based adaptive inertia weight ω to adapt to the evolution process. Eventually, SBPSO presents a approach of random mutation location to enhance the diversity of the population in case of the position out of range. Experiments have been conducted with eleven benchmark optimization functions. The results have demonstrated that SBPSO outperforms than that of the other five recent proposed PSO in obtaining the global optimum and accelerating the velocity of convergence.

Słowa kluczowe

EN

adaptive inertia weight; BF-PSO; Butterfly-PSO; opposition-based learning; particle swarm optimization (PSO); PSO

• Wydawca: IOS Press
• Czasopismo: Fundamenta Informaticae
• Rocznik: 2018
• Tom: Vol. 163, nr 2
• Strony: 205--223
• Opis fizyczny: Bibliogr. 36 poz., rys., tab., wykr.

Twórcy

autor

Tong L.

• College of Information Science and Engineering, Guilin University of Technology, 12 Jiangan Road, 541004 Guilin China
• School of Computer Science, Wuhan University, Wuhan, China

autor

Dong M.

• College of Information Science and Engineering, Guilin University of Technology, 12 Jiangan Road, 541004 Guilin China

autor

Ai B.

• College of Information Science and Engineering, Guilin University of Technology, 12 Jiangan Road, 541004 Guilin China

autor

Jing C.

• College of Information Science and Engineering, Guilin University of Technology, 12 Jiangan Road, 541004 Guilin China
• Guangxi key Laboratory of Embedded Technology and Intelligent System, Guilin University of Technology, Guilin, China

Bibliografia

• [1] Bing A, Ming-Gang D, Chuan-Xian J. Simple PSO Algorithm with Opposition-based Learning Average Elite Strategy, International Journal of Hybrid Information Technology, 2016;9(6):187-196.
• [2] Bing AI, Dong M. Improved particle swarm optimization algorithm based on Gaussian disturbance and natural selection, Journal of Computer Applications, 2016, pp. 687-691. doi:10.11772/j.issn.1001-9081.2016.03.687, http://www.joca.cn/EN/abstract/article_19100.shtml.
• [3] Bohre AK, Agnihotri G, Dubey M. Hybrid butterfly based particle swarm optimization for optimization problems, 2014 First International Conference on Networks Soft Computing (ICNSC2014), Aug 2014.
• [4] Bohre AK, Agnihotri G, Dubey M. The Butterfly-Particle Swarm Optimization (Butterfly-PSO/BF-PSO) Technique and Its Variables, Int. J. Soft Comput. Math. Control, 2015;4:23-39.
• [5] Bohre AK, Agnihotri G, Dubey M, Bhadoriya JS. A novel method to find optimal solution based on modified butterfly particle swarm optimization, Int. J. Soft Comput. Math. Control, 2014;3:1-14.
• [6] Calvini M, Carpita M, Formentini A, Marchesoni M. PSO-Based Self-Commissioning of Electrical Motor Drives, IEEE Transactions on Industrial Electronics, 2015;62(2):768-776, ISSN: 0278-0046. doi:10.1109/TIE.2014.2349478.
• [7] Chang JC. A robust adaptive array beamformer using particle swarm optimization for spaceCtime code division multiple access systems, Information Sciences, 2014;278:174-186, ISSN: 0020-0255. doi:http://dx.doi.org/10.1016/j.ins.2014.03.036, http://www.sciencedirect.com/science/article/pii/S002002551400320X.
• [8] Clerc M, Kennedy J. The particle swarm - explosion, stability, and convergence in a multidimensional complex space, IEEE Transactions on Evolutionary Computation, 2002;6(1):58-73, ISSN: 1089-778X. doi:10.1109/4235.985692.
• [9] Eberhart R, Kennedy J. A new optimizer using particle swarm theory, Micro Machine and Human Science, 1995. MHS ’95., Proceedings of the Sixth International Symposium on, Oct 1995. doi:10.1109/MHS.1995.494215.
• [10] Elbeltagi E, Hegazy T, Grierson D. Comparison among five evolutionary-based optimization algorithms, Advanced Engineering Informatics, 2005;19(1):43-53, ISSN 1474-0346. doi:10.1016/j.aei.2005.01.004, http://www.sciencedirect.com/science/article/pii/S1474034605000091.
• [11] Hariya Y, Shindo T, Jin’no K. An improved rotationally invariant PSO: A modified standard PSO-2011, 2016 IEEE Congress on Evolutionary Computation (CEC), July 2016 pp. 1839-1844. doi:10.1109/CEC.2016.7744012.
• [12] Jordehi AR. Enhanced leader PSO (ELPSO): A new PSO variant for solving global optimisation problems, Applied Soft Computing, 2015;26:401-417, ISSN 1568-4946. doi:10.1109/TIE.2014.2349478.
• [13] Kennedy J, Eberhart R. Particle swarm optimization, Neural Networks, 1995. Proceedings., IEEE International Conference on, vol. 4, Nov 1995, pp. 1942-1948. doi:10.1109/ICNN.1995.488968.
• [14] Kennedy J, Eberhart RC, Shi Y, Jacob C, Koza JR Iii FHB. Andre, D., Keane, M. A.: Swarm Intelligence The Morgan Kaufmann Series in Evolutionary Computation, 2001, pp. 475-495. doi:10.1109/ICNN.1995.488968.
• [15] Koulinas G, Kotsikas L, Anagnostopoulos K. A particle swarm optimization based hyper-heuristic algorithm for the classic resource constrained project scheduling problem, Information Sciences, 2014;277:680-693, ISSN: 0020-0255. doi:http://dx.doi.org/10.1016/j.ins.2014.02.155, http://www.sciencedirect.com/science/article/pii/S0020025514002771.
• [16] Li XL, He XD. A hybrid particle swarm optimization method for structure learning of probabilistic relational models, Information Sciences, 2014;283:258-266, New Trend of Computational Intelligence in Human-Robot Interaction. ISSN: 0020-0255. doi:10.1016/j.ins.2014.04.058. http://www.sciencedirect.com/science/article/pii/S0020025514005581.
• [17] Liang JJ, Qin AK, Suganthan PN, Baskar S. Comprehensive learning particle swarm optimizer for global optimization of multimodal functions, IEEE Transactions on Evolutionary Computation, 2006;10(3):281-295, ISSN 1089-778X. doi:10.1109/TEVC.2005.857610.
• [18] Lu Q, Han QL, Liu S. A finite-time particle swarm optimization algorithm for odor source localization, Information Sciences, 2014;277:111-140, ISSN 0020-0255. doi:10.1016/j.ins.2014.02.010. http://www.sciencedirect.com/science/article/pii/S0020025514001078.
• [19] Lynn N, Suganthan PN. Heterogeneous comprehensive learning particle swarm optimization with enhanced exploration and exploitation, Swarm and Evolutionary Computation, 2015;24:11-24, ISSN 2210-6502. doi:10.1016/j.swevo.2015.05.002. http://www.sciencedirect.com/science/article/pii/S2210650215000401.
• [20] Mahanipour A, Nezamabadi-pour H. Improved PSO-based feature construction algorithm using Feature Selection Methods, 2017 2nd Conference on Swarm Intelligence and Evolutionary Computation (CSIEC), March 2017. doi:10.1109/CSIEC.2017.7940173.
• [21] Nasir M, Das S, Maity D, Sengupta S, Halder U, Suganthan P. A dynamic neighborhood learning based particle swarm optimizer for global numerical optimization, Information Sciences, 2012;209:16-36, ISSN 0020-0255. doi:10.1016/j.ins.2012.04.028. http://www.sciencedirect.com/science/article/pii/S0020025512002927.
• [22] Qu B, Liang J, Suganthan P. Niching particle swarm optimization with local search for multi-modal optimization, Information Sciences, 2012;197:131-143, ISSN 0020-0255. doi:10.1016/j.ins.2012.02.011. http://www.sciencedirect.com/science/article/pii/S0020025512001144.
• [23] Ranjani M, Murugesan P. Optimal fuzzy controller parameters using PSO for speed control of Quasi-Z Source DC/DC converter fed drive, Applied Soft Computing, 2015;27:332-356, ISSN 1568-4946. doi:10.1016/j.asoc.2014.11.007. http://www.sciencedirect.com/science/article/pii/S1568494614005596.
• [24] Ratnaweera A, Halgamuge SK, Watson HC. Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients, IEEE Transactions on Evolutionary Computation, 2004;8(3):240-255, ISSN:1089-778X. doi:10.1109/TEVC.2004.826071.
• [25] Shen M, Zhan ZH, Chen WN, Gong YJ, Zhang J, Li Y. Bi-Velocity Discrete Particle Swarm Optimization and Its Application to Multicast Routing Problem in Communication Networks, IEEE Transactions on Industrial Electronics, 2014;61(12):7141-7151. ISSN 0278-0046. doi:10.1109/TIE.2014.2314075.
• [26] Shi Y, Eberhart R. A modified particle swarm optimizer, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat.No.98TH8360), May 1998 pp. 69-73. doi:10.1109/ICEC.1998.699146.
• [27] Shi Y, Eberhart RC. Fuzzy adaptive particle swarm optimization, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546), 2001;1:101-106. doi:10.1109/CEC.2001.934377.
• [28] Tizhoosh HR. Opposition-Based Learning: A New Scheme for Machine Intelligence, International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC’06), Nov 2005;1:695-701. doi:10.1109/CIMCA.2005.1631345.
• [29] Tran B, Xue B, Zhang M. A New Representation in PSO for Discretization-Based Feature Selection, IEEE Transactions on Cybernetics, 2017;PP(99):1-14, ISSN 2168-2267. doi:10.1109/TCYB.2017.2714145.
• [30] Wang H, Li H, Liu Y, Li C, Zeng S. Opposition-based particle swarm algorithm with cauchy mutation, 2007 IEEE Congress on Evolutionary Computation, Sept 2007 pp. 4750-4756, ISSN 1089-778X. doi:10.1109/CEC.2007.4425095.
• [31] Wang H, Wu Z, Rahnamayan S, Liu Y, Ventresca M. Enhancing particle swarm optimization using generalized opposition-based learning, Information Sciences, 2011;181(20):4699-4714, ISSN 0020-0255, Special Issue on Interpretable Fuzzy Systems. doi:10.1016/j.ins.2011.03.016. http://www.sciencedirect.com/science/article/pii/S0020025511001459.
• [32] Wang HU, Zhi-Shu LI. A Simpler and More Effective Particle Swarm Optimization Algorithm, Journal of Software, 2007;18(4):861-868.
• [33] Wang ZJ, Zhan ZH, Zhang J. An Improved Method for Comprehensive Learning Particle Swarm Optimization, 2015 IEEE Symposium Series on Computational Intelligence, Dec 2015 pp. 218-225. doi:10.1109/SSCI.2015.41.
• [34] Zhan ZH, Li JJ, Zhang J. Adaptive particle swarm optimization with variable relocation for dynamic optimization problems, 2014 IEEE Congress on Evolutionary Computation (CEC), July 2014 pp. 1565-1570, ISSN:1089-778X. doi:10.1109/CEC.2014.6900454.
• [35] Zhao X, Liu H, Liu D, Ai W, Zuo X. New modified bare-bones particle swarm optimization, 2016 IEEE Congress on Evolutionary Computation (CEC), July 2016 pp. 416-422. doi:10.1109/CEC.2016.7743824.
• [36] van Zyl ET, Engelbrecht AP. Group-based stochastic scaling for PSO velocities, 2016 IEEE Congress on Evolutionary Computation (CEC), July 2016 pp. 1862-1868. doi=10.1109/CEC.2016.7744015.