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Team Arrangement Heuristic Algorithm (TAHA): Theory and application

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  • Babayan, Narek
  • Tahani, Mojtaba

Abstract

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.

Suggested Citation

  • Babayan, Narek & Tahani, Mojtaba, 2019. "Team Arrangement Heuristic Algorithm (TAHA): Theory and application," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 166(C), pages 155-176.
    • Handle: RePEc:eee:matcom:v:166:y:2019:i:c:p:155-176
    DOI: 10.1016/j.matcom.2019.05.007
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    References listed on IDEAS

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    1. Yuhui Shi, 2011. "An Optimization Algorithm Based on Brainstorming Process," International Journal of Swarm Intelligence Research (IJSIR), IGI Global, vol. 2(4), pages 35-62, October.
    2. Batten, W.M.J. & Bahaj, A.S. & Molland, A.F. & Chaplin, J.R., 2006. "Hydrodynamics of marine current turbines," Renewable Energy, Elsevier, vol. 31(2), pages 249-256.
    3. Sergeyev, Yaroslav D. & Kvasov, Dmitri E. & Mukhametzhanov, Marat S., 2017. "Operational zones for comparing metaheuristic and deterministic one-dimensional global optimization algorithms," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 141(C), pages 96-109.
    4. Bahaj, A.S. & Molland, A.F. & Chaplin, J.R. & Batten, W.M.J., 2007. "Power and thrust measurements of marine current turbines under various hydrodynamic flow conditions in a cavitation tunnel and a towing tank," Renewable Energy, Elsevier, vol. 32(3), pages 407-426.
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