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Team Arrangement Heuristic Algorithm (TAHA): Theory and application

机译：团队安排启发式算法（TAHA）：理论与应用

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摘要

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. (C) 2019 International Association for Mathematics and Computers in Simulation (IMACS). Published by Elsevier B.V. All rights reserved.
机译：在这项研究中，基于公司的金字塔结构以及公司中每个成员的活动，提出了一种新颖的，受人启发的优化算法，即团队安排启发式算法（TAHA）。假设在一家公司中有三组成员从事活动，即首席执行官，董事和员工。这些成员的正确安排以及他们之间的联系将把公司带到最好的情况，最好的项目将由公司来处理，最好的成员将由该公司处理，并且该项目将以高品质精确完成。该算法的性能已经通过流行的单峰函数和多峰函数进行了评估。 CEC2005基准功能也被用作具有挑战性的问题。七种流行的优化算法分别是：粒子群优化（PSO），布谷鸟搜索（CS），萤火虫算法（FA），花粉授粉算法（FPA），磷虾群（KH），灰太狼优化器（GWO）和引力搜索算法（ GSA）用于比较。使用TAHA和其他提到的算法解决了两个实际案例工程问题，即热轮优化问题和水平轴潮流涡轮机问题。结果表明，TAHA在某些情况下优于其他算法，并且在解决复杂的优化问题方面具有出色的性能。 （C）2019国际模拟数学与计算机协会（IMACS）。由Elsevier B.V.发布。保留所有权利。

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