Abstract
Although different modified versions of particle swarm optimization (PSO) were proposed in past decades to solve global optimization problems, the appropriate mechanism used to attain proper balancing of algorithm’s exploration and exploitation searches remains as an open-ended challenges. A modified PSO with unique self-cognitive learning (MPSO-USCL) is proposed in this paper to address this issue. For each particle, a unique exemplar can be generated by the proposed USCL module to replace the self-cognitive component of each particle and guide its search process towards the promising regions of search space with different levels of exploration and exploitation strengths. Extensive simulation studies are performed to compare the optimization performances of MPSO-USCL with six existing PSO variants using 12 benchmark functions. The proposed MPSO-USCL is reported to outperform its peer algorithms for all benchmark functions.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ang CK, Tang SH, Mashohor S, Arrifin MKAM (2014) Solving continuous trajectory and forward kinematics simultaneously based on ANN. Int J Comput Commun Control
Abdullah Al-Sanabani DG, Solihin MI, Liew PP, Astuti W, Ang CK, Lim WH (2019) Development of non-destructive mango assessment using handheld spectroscopy and machine learning regression. J Phys Conf Ser 1367:012030
Alrifaey M, Sai Hong T, Supeni EE, As’arry A, Ang CK (2019) Identification and prioritization of risk factors in an electrical generator based on the hybrid FMEA framework. Energies 12, 649
Yao L, Shen J, Lim WH (2016) Real-Time energy management optimization for smart household. In: 2016 IEEE international conference on internet of things (iThings) and IEEE green computing and communications (GreenCom) and IEEE cyber, physical and social computing (CPSCom) and IEEE smart data (SmartData), Chengdu, China, pp 20–26
Yao L, Damiran Z, Lim WH (2017) Energy management optimization scheme for smart home considering different types of appliances. In: 2017 IEEE International conference on environment and electrical engineering and 2017 IEEE industrial and commercial power systems Europe (EEEIC/ I&CPS Europe), Milan, Italy, pp 1–6
Yao L, Lim WH (2018) Optimal purchase strategy for demand bidding. IEEE Trans Power Syst 33:2754–2762
Yao L, Lim WH, Tiang SS, Tan TH, Wong CH, Pang JY (2018) Demand bidding optimization for an aggregator with a genetic algorithm. Energies 11:2498
Yao L, Yao L, Lim WH (2018) A soft curtailment of wide-area central air conditioning load. Energies 11:492
Yao L, Chen Y, Lim WH (2015) Internet of things for electric vehicle: an improved decentralized charging scheme. In: 2015 IEEE international conference on data science and data intensive systems, Sydney, NSW, Australia, pp. 651–658
Natarajan E, Kaviarasan V, Lim WH, Tiang SS, Tan TH (2018) Enhanced multi-objective teaching-learning-based optimization for machining of delrin. IEEE Access 6:51528–51546
Natarajan E, Ang CT, Lim WH, Kosalishkwaran G, Ang CK, Parasuraman S (2019) Design topology optimization and kinematics of a multi-modal quadcopter and quadruped. In: 2019 IEEE student conference on research and development (SCOReD), pp. 214–218. Bandar Ser Iskandar, Malaysia
Natarajan E, Kaviarasan V, Lim WH, Tiang SS, Parasuraman S, Elango S (2019) Non-dominated sorting modified teaching–learning-based optimization for multi-objective machining of polytetrafluoroethylene (PTFE). J Intell Manuf
Tarawneh MA, Yu, LJ, Tarawni MA, Ahmad SHJ, Al-Banawi O, Bathiha MA (2015) High performance thermoplastic elastomer (TPE) nanocomposite based on graphene nanoplates (GNPs). World J Eng 12:437–442
Yu LJ, Sahrim AH, Kong I, Mouad AT (2012) Microwave absorbing properties of nickel-zinc ferrite/multiwalled nanotube thermoplastic natural rubber composites. Adv Mater Res 501:24–28
Yu LJ, Ahmad SH, Kong I, Tarawneh MA, Flaifel MH (2013) Preparation and characterisation of NiZn ferrite/multiwalled nanotubes thermoplastic natural rubber composite. Int J Mater Eng Innov 4:214–224
Yu LJ, Ahmad SH, Kong I, Appadu S, Flaifel MH (2012) Magnetic properties, microsturcture and mophology of thermoplastics natural rubber composite reinforced with NiZn ferrite/Mwnt. Sains Malaysiana 41:453–458
Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of ICNN’95—international conference on neural networks, Perth, WA, Australia, pp 1942–1948, vol 1944
Lim WH, Isa NAM (2015) Particle swarm optimization with dual-level task allocation. Eng Appl Artif Intell 38:88–110
Lim WH, Isa NAM, Tiang SS, Tan TH, Natarajan E, Wong CH, Tang JR (2018) A self-adaptive topologically connected-based particle swarm optimization. IEEE Access 6:65347–65366
Ang KM, Lim WH, Isa NAM, Tiang SS, Wong CH (2020) A constrained multi-swarm particle swarm optimization without velocity for constrained optimization problems. Expert Syst Appl 140:112882
Bonyadi MR, Michalewicz Z (2017) Particle swarm optimization for single objective continuous space problems: a review. Evol Comput 25:1–54
Liang JJ, Qin AK, Suganthan PN, Baskar S (2006) Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans Evol Comput 10:281–295
Tang Y, Wang Z, Fang J-A (2011) Feedback learning particle swarm optimization. Appl Soft Comput 11:4713–4725
Zhan Z, Zhang J, Li Y, Shi Y (2011) Orthogonal learning particle Swarm optimization. IEEE Trans Evol Comput 15:832–847
De Oca, MAM, Stutzle T, Birattari M, Dorigo M (2009) Frankenstein’s PSO: a composite particle swarm optimization algorithm. IEEE Trans Evol Comput 13:1120–1132
Zhan Z, Zhang J, Li Y, Chung HS (2009) Adaptive particle swarm optimization. IEEE Trans Syst Man Cybern Part B (Cybern) 39:1362–1381
Kathrada M (2009) The flexi-PSO: towards a more flexible particle swarm optimizer. OPSEARCH 46:52–68
Van den Bergh F, Engelbrecht AP (2004) A cooperative approach to particle swarm optimization. IEEE Trans Evol Comput 8:225–239
Derrac J, GarcÃa S, Molina D, Herrera F (2011) A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evol Comput 1:3–18
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Ang, K.M. et al. (2022). Modified Particle Swarm Optimization with Unique Self-cognitive Learning for Global Optimization Problems. In: Ab. Nasir, A.F., Ibrahim, A.N., Ishak, I., Mat Yahya, N., Zakaria, M.A., P. P. Abdul Majeed, A. (eds) Recent Trends in Mechatronics Towards Industry 4.0. Lecture Notes in Electrical Engineering, vol 730. Springer, Singapore. https://doi.org/10.1007/978-981-33-4597-3_25
Download citation
DOI: https://doi.org/10.1007/978-981-33-4597-3_25
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-33-4596-6
Online ISBN: 978-981-33-4597-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)