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Team arrangement heuristic algorithm (TAHA): theory and application. (English) Zbl 07316764

Summary: In this research study a novel human inspired optimization algorithm namely Team Arrangement Heuristic Algorithm (TAHA) is proposed, based on the pyramidal structure of a company and also the activities of each member in the company. It is assumed that in a company three groups of members do activities, which are the CEO, directors and employees. The right arrangement of these members and also connection between them will lead the company to the best situation where, the best project will be handled by the company, with the best members and the project will be precisely finished at its dead line with a high quality. The performance of the proposed algorithm has been evaluated with popular unimodal and multimodal functions. Also CEC2005 benchmark functions are used as a challenging problems. Seven popular optimization algorithms namely, particle swarm optimization (PSO), cuckoo search (CS), fire fly algorithm (FA), flower pollination algorithm (FPA), krill herd (KH), grey wolf optimizer (GWO) and gravitation search algorithm (GSA) are used for the purpose of comparison. Two real case engineering problems, which are heat wheel optimization problem and horizontal axis tidal current turbine problem, are solved using TAHA and other mentioned algorithms. The results indicated that TAHA outperforms other algorithms in several cases and it has a great performance in solving complicated optimization problems.

MSC:

68Pxx Theory of data
90Cxx Mathematical programming
68Txx Artificial intelligence
68Wxx Algorithms in computer science

Software:

CEC 05; Krill herd; GSA; WCA; GWO; ALO; WOA; SSA; GOA
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References:

[1] A. Ahmadi-Javid, Anarchic society optimization: a human-inspired method, Evolutionary Computation (CEC), in: 2011 IEEE Congress on, New Orleans, USA, 2011.; A. Ahmadi-Javid, Anarchic society optimization: a human-inspired method, Evolutionary Computation (CEC), in: 2011 IEEE Congress on, New Orleans, USA, 2011.
[2] Bahaj, A. S.; Molland, A. F.; Chaplin, J. R.; Batten, W. M.J., Power and thrust measurements of marine current turbines under various hydrodynamic flow conditions in a cavitation tunnel and towing tank, Renew. Energy, 32, 407-426 (2007)
[3] Batten, W. M.J.; Bahaj, A. S.; Molland, A. F.; Chaplin, J. R., Hydrodynamics of marine current turbines, Renew. Energy, 31, 249-256 (2006)
[4] Cui, Z.; Cai, X.; Shi, Z., Social emotional optimization algorithm with group decision, Sci. Res. Essays, 6, 4848-4855 (2011)
[5] Czerniak, J. M.; Zarzycki, H., Artificial acari optimization as a new strategy for global optimization of multimodal functions, J. Comput. Sci., 22, 209-227 (2017)
[6] El-Abd, M., Global-best brain storm optimization algorithm, Swarm Evol. Comput., 37, 7-44 (2017)
[7] Eskandar, H.; Sadollah, A.; Bahreininejad, A.; Hamdi, M., Water cycle algorithm- a novel metaheuristic optimization method for solving constrained engineering optimization problems, Comput. Struct., 110-111, 151-166 (2012)
[8] Gandomi, A. H.; Alavi, A. H., Krill herd: a new bio-inspired optimization algorithm, Commun. Nonlinear Sci. Numer. Simul., 17, 4831-4845 (2012) · Zbl 1266.65092
[9] E.A. Gargari, C. Lucas, Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition, in: Evolutionary Computation, CEC 2007, IEEE Congress on, Singapore, 2007.; E.A. Gargari, C. Lucas, Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition, in: Evolutionary Computation, CEC 2007, IEEE Congress on, Singapore, 2007.
[10] Hansen, M. O.L., Aerodynamics of Wind Turbines (2015), Routledge, Taylor and Francis Group: Routledge, Taylor and Francis Group New York
[11] Hansen, N.; Kern, S., Evaluating the cma evolution strategy on multimodal test functions, (Eighth International Conference on Parallel Problem Solving from Nature PPSN VIII, Proceedings (2004), Springer: Springer Berlin), 282-291
[12] Hatamlou, A., Black hole: a new heuristic optimization approach for data clustering, Inform. Sci., 222, 175-184 (2013)
[13] Javidy, B.; Hatamlou, A.; Mirjalili, S., Ions motion algorithm for solving optimization problems, Appl. Soft Comput., 32, 72-79 (2015)
[14] Kashan, A. H., League championship algorithm (lca): an algorithm for global optimization inspired by sport championships, Appl. Soft Comput., 16, 171-200 (2014)
[15] Kaveh, A.; Dadras, A., A novel meta-heuristic optimization algorithm: thermal exchange optimization, Adv. Eng. Softw., 110, 69-84 (2017)
[16] Kaveh, A.; Farhoudi, N., A new optimization method: dolphin echolocation, Adv. Eng. Softw., 59, 53-70 (2013)
[17] Kaveh, A.; Ghazaan, M. I.; Bakhshpoori, T., An improved ray optimization algorithm for design of truss structures, Period. Polytech. Civ. Eng., 57, 97-112 (2013)
[18] J. Kennedy, R. Eberhart, Particle swarm optimization, in: Proceedings of the 1995 IEEE international conference on neural networks, 1995, 4, pp. 1942-1948.; J. Kennedy, R. Eberhart, Particle swarm optimization, in: Proceedings of the 1995 IEEE international conference on neural networks, 1995, 4, pp. 1942-1948.
[19] Mirjalili, S., The ant lion optimizer, Adv. Eng. Softw., 83, 80-98 (2015)
[20] Mirjalili, S., Sca: a sine cosine algorithm for solving optimization problems, Knowl.-Based Syst., 96, 120-133 (2016)
[21] Mirjalili, S.; Gandomi, A. H.; Mirjalili, S. Z.; Saremi, S.; Faris, H.; Mirjalili, S. M., Salp swarm algorithm: a bio-inspired optimizer for engineering design problems, Adv. Eng. Softw., 114, 163-191 (2017)
[22] Mirjalili, S.; Lewis, A., The whale optimization algorithm, Adv. Eng. Softw., 95, 51-67 (2016)
[23] Mirjalili, S.; Mirjalili, S. M.; Lewis, A., Grey wolf optimizer, Adv. Eng. Softw., 69, 46-61 (2014)
[24] Moghdani, R.; Salimifard, K., Volleyball premier league algorithm, Appl. Soft Comput., 64, 161-185 (2018)
[25] Qi, X.; Zhu, Y.; Zhang, H., A new meta-heuristic butterfly-inspired algorithm, J. Comput. Sci., 23, 226-239 (2017)
[26] Rashedi, E.; Nezamabadi-pour, H.; Saryazdi, S., Gsa: a gravitation search algorithm, Inform. Sci., 179, 2232-2248 (2009) · Zbl 1177.90378
[27] Sanaye, S.; Hajabdollahi, H., Multi-objective optimization of rotary regenerator using genetic algorithm, Int. J. Therm. Sci., 48, 1967-1977 (2009)
[28] Sanaye, S.; Jafari, S.; Ghaebi, H., Optimum operational conditions of a rotary regenerator using genetic algorithm, Energ Building, 40, 1637-1642 (2008)
[29] Saremi, S.; Mirjalili, S.; Lewis, A., Grasshopper optimisation algorithm: theory and application, Adv. Eng. Softw., 105, 30-47 (2017)
[30] Sergeyev, Y. D.; Kvasov, D. E.; Mukhametzhanov, M. S., Operational zones for comparing metaheuristic and deterministic one-dimensional global optimization algorithms, Math. Comput. Simulation, 141, 96-109 (2017) · Zbl 07313866
[31] Shah, R. K.; Shekulic, D. P., Fundamentals of Heat Exchanger Design (2003), John Wiley & Sons, Inc: John Wiley & Sons, Inc NewYork.
[32] Shi, Y., An optimization algorithm based on brainstorming process, Int. J. Swarm Intell. Res., 2, 4 (2011)
[33] S. Hr. Aghay Kaboli, Y.; Selvaraj, J.; Rahim, N. A., Rain-fall optimization algorithm: a population based algorithm for solving constrained optimization problems, J. Comput. Sci., 19, 31-42 (2017)
[34] Suganthan, P. N.; Hansen, N.; Liang, J. J.; Deb, K.; Chen, Y.-P.; Auger, A.; Tiwari, S., Problem definitions and evaluation criteria for the cec 2005 special session on real-parameter optimization (2005), Nanyang Technological University: Nanyang Technological University Singapore
[35] Tahani, M.; Babayan, N.; Mehrnia, S.; Shadmehri, M., A novel heuristic method for optimization of straight blade vertical axis wind turbine, Energy Convers. Manage., 127, 461-476 (2016)
[36] Tahani, M.; Babayan, N.; Pouyaei, A., Optimization of pv/wind/battery stand-alone system, using hybrid fpa/sa algorithm and cfd simulation, case study: tehran, Energy Convers. Manage., 106, 644-659 (2015)
[37] Tahani, M.; Babayan, N.; Razi Astaraei, F.; Moghadam, A., Multi objective optimization of horizontal axis tidal current turbines, using meta heuristics algorithms, Energy Convers. Manage., 103, 487-498 (2015)
[38] Tahani, M.; Maeda, T.; Babayan, N.; Mehrnia, S.; Shadmehri, M.; Li, Q.; Fahimi, R.; Masdari, M., Investigating the effect of geometrical parameters of an optimized wind turbine blade in turbulent flow, Energy Convers. Manage., 153, 71-82 (2017)
[39] M. Vijesh, S. Lyengar, S.M.V. Mahantesh, A. Ramesh, C.P. Rangan, C.E.V. Madhavan, A navigation algorithm inspired by human navigation, in: IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 2012.; M. Vijesh, S. Lyengar, S.M.V. Mahantesh, A. Ramesh, C.P. Rangan, C.E.V. Madhavan, A navigation algorithm inspired by human navigation, in: IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 2012.
[40] Yang, X. S., Firefly algorithm, stochastic test functions and design optimisation, Int. J. Bio-Inspir. Comput., 2, 78-84 (2010)
[41] Yang, X. S., Flower pollination algorithm for global optimization, (Unconventional Computation and Natural Computation (2012)), 240-249 · Zbl 1374.68527
[42] Yang, X. S.; Deb, S., Cuckoo search via levy flights, (Proc. Of World Congress on Nature & Biologically Inspired Computing (NaBIC 2009) (2009), IEEE Publications), 210-214
[43] Yazdani, M.; Jolai, F., Lion optimization algorithm (loa): a nature-inspired metaheuristic algorithm, J. Comput. Des. Eng., 3, 24-36 (2016)
[44] Yu, J. J.Q.; Li, V. O.K., A social spider algorithm for global optimization, Appl. Soft Comput., 30, 614-627 (2015)
[45] L.M. Zhang, C. Dahlmann, Y. Zhang, Human-Inspired Algorithms for continuous function optimization, in: Intelligent Computing and Intelligent Systems, ICIS 2009Shanghai, China, 2009.; L.M. Zhang, C. Dahlmann, Y. Zhang, Human-Inspired Algorithms for continuous function optimization, in: Intelligent Computing and Intelligent Systems, ICIS 2009Shanghai, China, 2009.
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