Abstract
The salp swarm algorithm (SSA) has shown its fast search speed in several challenging problems. Research shows that not every nature-inspired approach is suitable for all applications and functions. Additionally, it does not provide the best exploration and exploitation for each function during the search process. Therefore, there were several researches attempts to improve the exploration and exploitation of the meta-heuristics by developing the newly hybrid approaches. This inspired our current research and therefore, we developed a newly hybrid approach called hybrid salp swarm algorithm with particle swarm optimization for searching the superior quality of optimal solutions of the standard and engineering functions. The hybrid variant integrates the advantages of SSA and PSO to eliminate many disadvantages such as the trapping in local optima and the unbalanced exploitation. We have used the velocity phase of the PSO approach in salp swarm approach in order to avoid the premature convergence of the optimal solutions in the search space, escape from ignoring in local minima and improve the exploitation tendencies. The new approach has been verified on different dimensions of the given functions. Additionally, the proposed technique has been compared with a wide range of algorithms in order to confirm its efficiency in solving standard CEC 2005, CEC 2017 test suits and engineering problems. The simulation results show that the proposed hybrid approach provides competitive, often superior results as compared to other existing algorithms in the research community.
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References
Kalaiselvi K, Kumar VS, Chandrasekar K (2010) Enhanced genetic algorithm for optimal electric power flow using TCSC and TCPS. World congress on engineering, vol II, WCE 2010, June 30–July 2, 2010, London, UK
Bakirtzis AG, Biskas PN, Zoumas CE, Petridis V (2002) Optimal power flow by enhanced genetic algorithm. IEEE Power Eng Rev 22(2):60
Chung TS, Li YZA (2000) A hybrid GA approach for OPF with consideration of FACTS devices. IEEE Power Eng Rev 20(8):54–57
Cai LJ, Erlich I, Stamtsis G (2004) Optimal choice and allocation of FACTS devices in deregulated electricity market using genetic algorithms. In: IEEE PES power systems conference and exposition, 10–13 Oct 2004, New York, NY, USA
Slimani L, Optimal BT (2012) Optimal power flow solution of the algerian electrical network using differential evolution algorithm. TELKOMNIKA 10(2):199–210
Simon D (2008) Biogeography-based optimization. IEEE Trans Evol Comput 12(6):702–713
Duman S, Guvenc U, Sonmez Y, Yorukeren N (2012) Optimal power flow using gravitational search algorithm. Energy Convers Manag 59:86–95
Kennedy J (2011) Particle swarm optimization. In: Sammut C, Webb GI (eds) Encyclopedia of machine learning. Springer, Boston
Abido MA (2002) Optimal power flow using Tabu search algorithm. Electr Power Compon Syst 30:469–483
Sinsupan N, Leeton U, Kulworawanichping T (2010) Application of harmony search to optimal power flow problems. In: 2010 International conference on advances in energy engineering, 19–20 June 2010, Beijing, China
Alrashydah EI, Qudais SAA (2018) Modeling of creep compliance behavior in asphalt mixes using multiple regression and artificial neural networks. Constr Build Mater 159:635–641
Mirjalili S (2015) Dragonfly algorithm: a new meta-heuristics optimization technique for solving single-objetive, discrete, and multi-objective problems. Neural Comput Appl 27:1053–1073
Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61
Mukherjee A, Mukherjee V (2015) Solution of optimal power flow using chaotic krill herd algorithm. Chaos Solitons Fractals 78:10–21
Bouchekara HREH (2014) Optimal power flow using black-hole-based optimization approach. Appl Soft Comput 24:879–888
Tal AB, Ghaoui LEI, Nemirovski A (2009) Robust optimization. Princeton series in applied mathematics. Princeton University Press, Princeton, pp 1–576
Mirjalili S (2015) The ant lion optimizer. Adv Eng Softw 83:80–98
Singh N, Singh SB (2011) One half global best position particle swarm optimization algorithm. Int J Sci Eng Res 2(8):1–9
Roger JM, Chauchard F, Maurel VB (2003) EPOPLS external parameter orthogonalisation of PLS application to temperature-independent measurement of sugar content of intact fruits. Chemometr Intell Lab 66:191–204
Fausto F, Cuevas E, Valdivia A, González A (2017) A global optimization algorithm inspired in the behavior of selfish herds. Biosystems 160:39–55
Joshi H, Arora S (2017) Enhanced grey wolf optimization algorithm for constrained optimization problems. Int J Swarm Intell 3(2/3):126–151
Qais MH, Hasanien HM, Alghuwainem S (2018) Augmented grey wolf optimizer for grid-connected PMSG-based wind energy conversion systems. Appl Soft Comput 69:505–515
Singh N, Singh SB (2017) Hybrid algorithm of particle swarm optimization and grey wolf optimizer for improving convergence performance. J Appl Math 2017:1–15
Soares J, Sousa T, Vale ZA, Morais H, Faria P (2011) Ant colony search algorithm for the optimal power flow problem. In: 2011 IEEE power and energy society general meeting, 24–28 July 2011, Detroit, MI, USA
Saremi S, Mirjalili S, Lewis A (2017) Grasshopper optimisation algorithm: theory and application. Adv Eng Softw 105:30–47
Mirjalili S (2016) SCA: a sine cosine algorithm for solving optimization problems. Knowl Based Syst 96:120–133
Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67
Singh S, Singh SB (2017) A novel hybrid GWO-SCA approach for optimization problems. Eng Sci Technol Int J 20(6):1586–1601
Mirjalili S (2015) Moth-flame optimization algorithm: a novel nature-inspire heuristic paradigm. Knowl Syst 89:228–249
Daryani N, Hagh MT, Teimourzadeh S (2016) Adaptive group search optimization algorithm for multi-objective optimal power flow problem. Appl Soft Comput 38:1012–1024
Singh N, Singh S, Singh SB (2017) A new hybrid MGBPSO-GSA variant for improving function optimization solution in search space. Evolut Bioinform 13:1–13
Mirjalili S, Mirjalili SM, Hatamlou A (2016) Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Comput Appl 27:495–513
Singh N, Singh S, Singh SB, Arora S (2012) Half mean particle swarm optimization algorithm. Int J Sci Eng Res 3(80):1–10
Rao RM, Babu AVN (2013) Optimal power flow using cuckoo optimization algorithm. Int J Adv Res Electr Electron Instrum Eng 2(9):1–6
Singh N, Singh SB (2012) Personal best position particle swarm optimization. J Appl Comput Sci Math Suceava 12(6):69–76
Singh N, Hachimi H (2018) A new hybrid whale optimizer algorithm with mean strategy of grey wolf optimizer for global optimization. Math Comput Appl 23(1):1–32
Yu S, Wu Z, Wang H, Chen Z (2010) A hybrid particle swarm optimization algorithm based on space transformation search and a modified velocity model. In: Schaeffer J (ed) High performance computing and applications. Springer, Berlin, pp 522–527
Liang RH, Tsai SR, Chen YT, Tseng WT (2011) Optimal power flow by a fuzzy based hybrid particle swarm optimization approach. Electr Power Syst Res 81(7):1466–1474
Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of the ICNN’95—international conference on neural networks. IEEE, pp 1942–1948. https://doi.org/10.1109/icnn.1995.488968
Kennedy J, Eberhart RC, Shi Y (2001) Swarm intelligence. Morgan Kaufmann Publishers, Burlington
Liu W, Wang K, Sun B, Shao K (2006) A hybrid particle swarm optimization algorithm for predicting the chaotic time series. In: 2006 international conference on mechatronics and automation, 25–28 June 2006, Luoyang, Henan, China
Marinke R, Araujo E, Coelho LS, Matiko L (2005) Particle swarm optimization (PSO) applied to fuzzy modeling in a thermal-vacuum system. In: Fifth international conference on hybrid intelligent system (HIS’05), 6–9 Nov 2005, Rio de Janeiro, Brazil
Angeline PJ (1998) Evolutionary optimization versus particle swarm optimization: philosophy and performance differences. In: Porto VW, Saravanan N, Waagen D, Eiben AE (eds) Evolutionary programming VII. EP 1998. Lecture notes in computer science, vol 1447. Springer, Berlin
Juang CF (2004) A hybrid of genetic algorithm and particle swarm optimization for recurrent network design. IEEE Trans Syst Man Cybern B (Cybern) 34(2):997–1006
Zhang C, Shao H, Li Yu (2000) Particle swarm optimisation for evolving artificial neural network. In: SMC 2000 conference proceedings. 2000 IEEE international conference on systems, man and cybernetics. ‘cybernetics evolving to systems, humans, organizations, and their complex interactions’, 8–11 Oct 2000, Nashville, TN, USA
Esmin AAA, Torres GL, Souza ACZD (2005) A hybrid particle swarm optimization applied to loss power minimization. IEEE Trans Power Syst 20(2):859–866
Esmin AAA, Torres GL, Alvarenga GB (2006) Hybrid evolutionary algorithm based on PSO and GA mutation. In: 2006 sixth international conference on hybrid intelligent systems (HIS’06’), 13–15 Dec 2006, Rio de Janeiro, Brazil
Zhao B, Guo CX, Cao YJ (2005) A multiagent-based particle swarm optimization approach for optimal reactive power dispatch. IEEE Trans Power Syst 20:1070–1078
Vlachogiannis JG, Leet KY (2006) A comparative study on particle swarm optimization for optimal steady-state performance of power systems. IEEE Trans Power Syst 21(4):1718–1728
Huang CM, Huang CJ, Wang ML (2005) A particle swarm optimization to identifying the ARMAX model for short-term load forecasting. IEEE Trans Power Syst 20(2):1126–1133
Esmin AAA, Torres GL (2012) Application of particle swarm optimization to optimal power systems. Int J Innov Comput Inf Control 8(3):1705–1716
Esmin AAA, Torres GL (2006) Fitting fuzzy membership functions using hybrid particle swarm optimization. In: 2006 IEEE international conference on fuzzy systems, 16–21 July 2006
Ali AA, Tawhid MA (2017) A hybrid particle swarm optimization and genetic algorithm with population partitioning for large scale optimization problems. Ain Shams Eng J 8(2):191–206
Mao B, Xie Z, Wang Y, Handroos H, Wu H, Shi S (2017) A hybrid differential evolution and particle swarm optimization algorithm for numerical kinematics solution of remote maintenance manipulators. Fusion Eng Des 124:587–590
Hadji B, Mahdad B, Srairi K, Mancer N (2015) Multi-objective PSO-TVAC for environmental/economic dispatch problem. Energy Procedia 74:102–111
Zhang Y, Gong DW, Cheng J (2017) Multi-objective particle swarm optimization approach for cost-based feature selection in classification. IEEE/ACM Trans Comput Biol Bioinf 14(1):64–75
Song XF, Zhang Y, Guo YN, Sun XY, Wang YL (2020) Variable-size cooperative co-evolutionary particle swarm optimization for feature selection on high-dimensional data. IEEE Trans Evolut Comput. https://doi.org/10.1109/TEVC.2020.2968743
Zhang Y, Gong DW, Geng N, Sun XY (2014) Hybrid bare-bones PSO for dynamic economic dispatch with value-point effects. Appl Soft Comput 18:248–260
Zhang Y, Gong DW, Zhang J (2013) Robot path planning in uncertain environment using multi-objective particle swarm optimization. Neurocomputing 103:172–185
Singh N, Singh SB (2017) A modified mean gray wolf optimization approach for benchmark and biomedical problems. Evol Bioinform 13:1–28
Mirjalil 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
Awad N, Ali M, Liang J, Qu B, Suganthan P (2017) Problem definitions and evaluation criteria for the CEC 2017 special session and competition on single objective real-parameter numerical optimization. Technical report
Derrac J, García 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(1):3–18
David DCN, Stephen CEA (2018) Cost Minimization of Welded Beam Design Problem using Non-traditional optimization through Matlab and validation through analyses simulation. Int J Mech Eng Technol IJMET 9(8):180–192
Mosavi A, Vaezipour A (2012) Reactive search optimization: application to multi-objective optimization problems. Appl Math 3:1572–1582
Li HS, Au SK (2010) Solving constrained optimization problems via subset simulation. In: 4th international workshop on reliable engineering computing, pp 439–453
Coello CAC (2000) Use of a self-adaptive penalty approach for engineering optimization problems. Comput Ind 41:113–127
Akay B, Karaboga D (2012) Artificial bee colony algorithm for large-scale problems and engineering design optimization. J Intell Manuf 23(4):1001–1014
He Q, Wang L (2007) An effective co-evolutionary particle swarm optimization for constrained engineering design problems. Eng Appl Artif Intell 20(1):89–99
Huang FZ, Wang L (2007) An effective co-evolutionary differential evolution for constrained optimization. Appl Math Comput 186(1):340–356
Mahdavi M, Fesangharg M, Damangir E (2007) An improved harmony search algorithm for solving optimization problems. Appl Math Comput 188(2):1567–1579
Baykasoğlu A, Ozsoydan FB (2015) Adaptive firefly algorithm with chaos for mechanical design optimization problems. Appl Soft Comput 36:152–164
Kaveh A, Talatahari S (2010) A novel heuristic optimization method: charged system search. Acta Mech 213:267–289
Lu Y, Zhou Y, Wu X (2017) A hybrid lightning search algorithm-simplex method for global optimization. Discrete Dyn Nat Soc 8342694:1–23
Khalilpourazari S, Khalilpourazary S (2019) An efficient hybrid algorithm based on water cycle and moth-flame optimization algorithms for solving numerical and constrained engineering optimization problems. Soft Comput 23:1699–1722. https://doi.org/10.1007/s00500-017-2894-y
Kumar A (2016) Ball bearing design through Jaya algorithm. Int J Adv Res Sci Eng 5(12):458–467
Chakraborty I, Kumar V, Nair SB (2003) Rolling element bearing design through genetic algoirthms. Eng Optim 35(6):1–26
Dhiman G (2019) ESA: a hybrid bio-inspired metaheuristic optimization approach for engineering problems. Eng Comput. https://doi.org/10.1007/s00366-019-00826-w
Yuan X, Dai X, Zhao J, He Q (2014) On a novel multi-swarm fruit fly optimization algorithm and its application. Appl Math Comput 233:260–271
Mosavi A, Vaezipour A (2012) Reactive search optimization; application to multiobjective optimization problems. Appl Math 3(10):1572–1582
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The authors are very grateful to the referees for their valuable suggestions, which helped to improve the quality of the paper significantly.
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Singh, N., Singh, S.B. & Houssein, E.H. Hybridizing salp swarm algorithm with particle swarm optimization algorithm for recent optimization functions. Evol. Intel. 15, 23–56 (2022). https://doi.org/10.1007/s12065-020-00486-6
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DOI: https://doi.org/10.1007/s12065-020-00486-6