Abstract
Recently metaheuristic algorithms have become popular in solving DG placement problems due to its advantages of simple implementation and ability to find the near optimal solution for complex and large-scale optimization problems. Symbiotic organism search (SOS) is one of the latest metaheuristic algorithms introduced to solve DG placement problems. Unlike many other metaheuristic algorithms, SOS is simple to implement and does not use any control parameters which lead to enhancing performance stability. However, like other optimization algorithms SOS suffers with local optimal and stagnations which affects its accuracy and convergence especially in solving real world problems like DG placement problem. This work attempts to enhance performance of SOS by combining with cloud-based model. The proposed algorithm is named cloud based model symbiotic organism search (CMSOS). In CMSOS, Cloud-based theories have been used to generate random number operator in mutualism phase of the original SOS. To assess the performance of CMSOS in solving optimization problems 26 benchmark functions with different dimensions have been used. The performance of the proposed algorithm has been tested for real world DG placement problems. The performed analysis such as statistical, convergence and complexity measures show superiority of the proposed algorithm compare to the studied algorithms.
Similar content being viewed by others
References
Karimyan P, Gharehpetian GB, Abedi M, Gavili A (2014) Long term scheduling for optimal allocation and sizing of dg unit considering load variations and dg type. Int J Electr Power Energy Syst 54:277–287
Viral R, Khatod D (2012) Optimal planning of distributed generation systems in distribution system: A review. Renew Sustain Energy Rev 16(7):5146–5165
Naik SNG, Khatod DK, Sharma MP (2014) Analytical approach for optimal siting and sizing of distributed generation in radial distribution networks. IET Gener Transmission Distrib 9(3):209–220
Ehsan A, Yang Q (2018) Optimal integration and planning of renewable distributed generation in the power distribution networks: A review of analytical techniques. Appl Energy 210:44–59
Vc VR et al (2018) Optimal renewable resources placement in distribution networks by combined power loss index and whale optimization algorithms. J Electr Syst Inf Technol 5(2):175–191
Sultana U, Khairuddin AB, Mokhtar A, Zareen N, Sultana B (2016) Grey wolf optimizer based placement and sizing of multiple distributed generation in the distribution system. Energy 111:525–536
Ullah Z, Wang S, Radosavljević J (2019) A novel method based on ppso for optimal placement and sizing of distributed generation. IEEJ Trans Electr Electr Eng 14(12):1754–1763
Haesens E, Espinoza M, Pluymers B, Goethals I, Thong V, Driesen J, Belmanss R, Moor B d (2005) Optimal placement and sizing of distributed generator units using genetic optimization algorithms. Electr Power Qual Utilisat J 11(1):97–104
García JAM, Mena AJG (2013) Optimal distributed generation location and size using a modified teaching-learning based optimization algorithm. Int J Electr Power Energy Syst 50:65–75
Das B, Mukherjee V, Das D (2016) Dg placement in radial distribution network by symbiotic organisms search algorithm for real power loss minimization. Appl Soft Comput 49:920–936
Cheng M-Y, Prayogo D (2014) Symbiotic organisms search: a new metaheuristic optimization algorithm. Comput Struct 139:98–112
Ezugwu AE, Adeleke OJ, Akinyelu AA, Viriri S (2020) A conceptual comparison of several metaheuristic algorithms on continuous optimisation problems. Neural Comput Appl 32(10):6207–6251
Ezugwu AE, Prayogo D (2019) Symbiotic organisms search algorithm: theory, recent advances and applications. Expert Syst Appl 119:184–209
Anbarasan P, Jayabarathi T (2017) Optimal reactive power dispatch problem solved by symbiotic organism search algorithm. In 2017 innovations in power and advanced computing technologies (i-PACT). IEEE, pp 1–8
Prasad D, Mukherjee V (2018) Solution of optimal reactive power dispatch by symbiotic organism search algorithm incorporating facts devices. IETE J Res 64(1):149–160
Yalçın E, Taplamacıoğlu MC, Çam E (2019) The adaptive chaotic symbiotic organisms search algorithm proposal for optimal reactive power dispatch problem in power systems. Electrica 19(1):37–47
Xiong G, Zhang J, Yuan X, Shi D, He Y (2018) Application of symbiotic organisms search algorithm for parameter extraction of solar cell models. Appl Sci 8(11):2155
Sulaiman M, Ahmad A, Khan A, Muhammad S (2018) Hybridized symbiotic organism search algorithm for the optimal operation of directional overcurrent relays. Complexity. https://doi.org/10.1155/2018/4605769
Dosoglu MK, Guvenc U, Duman S, Sonmez Y, Kahraman HT (2018) Symbiotic organisms search optimization algorithm for economic/emission dispatch problem in power systems. Neural Comput Appl 29(3):721–737
Duman S (2017) Symbiotic organisms search algorithm for optimal power flow problem based on valve-point effect and prohibited zones. Neural Comput Appl 28(11):3571–3585
Gharehchopogh FS, Shayanfar H, Gholizadeh H (2019) A comprehensive survey on symbiotic organisms search algorithms. Artif Intell Rev. https://doi.org/10.1007/s10462-019-09733-4
Saha S, Mukherjee V (2020) A novel multi-objective modified symbiotic organisms search algorithm for optimal allocation of distributed generation in radial distribution system. Neural Comput Appl. https://doi.org/10.1007/s00521-020-05080-6
Lalitha MP, Babu PS, Adivesh B (2016) Optimal distributed generation and capacitor placement for loss minimization and voltage profile improvement using symbiotic organisms search algorithm. Int J Electr Eng 9(3):249–261
Igel C (2014) No free lunch theorems: limitations and perspectives of metaheuristics. Theory and principled methods for the design of metaheuristics. Springer, pp 1–23
Abdullahi M, Ngadi MA, Dishing SI, Usman MJ et al (2020) A survey of symbiotic organisms search algorithms and applications. Neural Comput Appl 32:547–566
Nama S, Saha A, Ghosh S (2016) Improved symbiotic organisms search algorithm for solving unconstrained function optimization. Decis Sci Lett 5(3):361–380
Tejani GG, Savsani VJ, Patel VK (2016) Adaptive symbiotic organisms search (sos) algorithm for structural design optimization. J Comput Des Eng 3(3):226–249
Secui DC (2016) A modified symbiotic organisms search algorithm for large scale economic dispatch problem with valve-point effects. Energy 113:366–384
Saha S, Mukherjee V (2018) A novel chaos-integrated symbiotic organisms search algorithm for global optimization. Soft Comput 22(11):3797–3816
Zang W, Ren L, Zhang W, Liu X (2018) A cloud model based dna genetic algorithm for numerical optimization problems. Future Gener Comput Syst 81:465–477
Torabzadeh E, Zandieh M (2010) Cloud theory-based simulated annealing approach for scheduling in the two-stage assembly flowshop. Adv Eng Softw 41(10–11):1238–1243
Wu L, Zuo C, Zhang H (2015) A cloud model based fruit fly optimization algorithm. Knowl-Based Syst 89:603–617
Ma Y, Xu J (2015) A cloud theory-based particle swarm optimization for multiple decision maker vehicle routing problems with fuzzy random time windows. Eng Optim 47(6):825–842
Cheng J, Wang L, Jiang Q, Cao Z, Xiong Y (2018) Cuckoo search algorithm with dynamic feedback information. Future Gener Comput Syst 89:317–334
Cheng J, Duan Z (2019) Cloud model based sine cosine algorithm for solving optimization problems. Evol Intel 12(4):503–514
Teng J-H (2003) A direct approach for distribution system load flow solutions. IEEE Trans Power Deliv 18(3):882–887
Quadri IA, Bhowmick S, Joshi D (2018) A comprehensive technique for optimal allocation of distributed energy resources in radial distribution systems. Appl Energy 211:1245–1260
Truong KH, Nallagownden P, Elamvazuthi I, Vo DN (2020) A quasi-oppositional-chaotic symbiotic organisms search algorithm for optimal allocation of dg in radial distribution networks. Appl Soft Comput 88:106067
Wang Z (2011) Application of cloud theory in association rules. Inf Technol Comput Sci 3:36–42
Yang Y, Liu R, Chen Y, Li T, Tang Y (2018) Normal cloud model-based algorithm for multi-attribute trusted cloud service selection. IEEE Access 6:37644–37652
Engelbrecht AP (2014) Fitness function evaluations: a fair stopping condition. In 2014 IEEE symposium on swarm intelligence. IEEE, pp 1–8
Askarzadeh A (2016) A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm. Comput Struct 169:1–12
Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67
Harifi S, Mohammadzadeh J, Khalilian M, Ebrahimnejad S (2020) Giza pyramids construction: an ancient-inspired metaheuristic algorithm for optimization. Evolut Intell. https://doi.org/10.1007/s12065-020-00451-3
Mandal B, Roy PK (2014) Multi-objective optimal power flow using quasi-oppositional teaching learning based optimization. Appl Soft Comput 21:590–606
Baran ME, Wu FF (1989) Network reconfiguration in distribution systems for loss reduction and load balancing. IEEE Power Eng Rev 9(4):101–102
Kashem M, Ganapathy V, Jasmon G, Buhari M (2000) A novel method for loss minimization in distribution networks. In: Proceedings DRPT2000. International conference on electric utility deregulation and restructuring and power technologies (Cat. No. 00EX382). IEEE, pp 251–256
Mahmoud K, Yorino N, Ahmed A (2015) Optimal distributed generation allocation in distribution systems for loss minimization. IEEE Trans Power Syst 31(2):960–969
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Kawambwa, S., Hamisi, N., Mafole, P. et al. A cloud model based symbiotic organism search algorithm for DG allocation in radial distribution network. Evol. Intel. 15, 545–562 (2022). https://doi.org/10.1007/s12065-020-00529-y
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12065-020-00529-y