Dehghani, M., Mardaneh, M., Malik, O. (2020). FOA: ‘Following’ Optimization Algorithm for solving Power engineering optimization problems. Journal of Operation and Automation in Power Engineering, 8(1), 57-64. doi: 10.22098/joape.2019.5522.1414

M. Dehghani; M. Mardaneh; O. P. Malik. "FOA: ‘Following’ Optimization Algorithm for solving Power engineering optimization problems". Journal of Operation and Automation in Power Engineering, 8, 1, 2020, 57-64. doi: 10.22098/joape.2019.5522.1414

Dehghani, M., Mardaneh, M., Malik, O. (2020). 'FOA: ‘Following’ Optimization Algorithm for solving Power engineering optimization problems', Journal of Operation and Automation in Power Engineering, 8(1), pp. 57-64. doi: 10.22098/joape.2019.5522.1414

Dehghani, M., Mardaneh, M., Malik, O. FOA: ‘Following’ Optimization Algorithm for solving Power engineering optimization problems. Journal of Operation and Automation in Power Engineering, 2020; 8(1): 57-64. doi: 10.22098/joape.2019.5522.1414

FOA: ‘Following’ Optimization Algorithm for solving Power engineering optimization problems

^{1}Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz, Iran.

^{2}Department of Electrical Engineering, University of Calgary, Calgary Alberta Canada.

Receive Date: 03 December 2018,
Revise Date: 02 April 2019,
Accept Date: 28 May 2019

Abstract

These days randomized-based population optimization algorithms are in wide use in different branches of science such as bioinformatics, chemical physics andpower engineering. An important group of these algorithms is inspired by physical processes or entities’ behavior. A new approach of applying optimization-based social relationships among the members of a community is investigated in this paper. In the proposed algorithm, search factors are indeed members of the community who try to improve the community by ‘following’ each other. FOA implemented on 23 well-known benchmark test functions. It is compared with eight optimization algorithms. The paper also considers for solving optimal placement of Distributed Generation (DG). The obtained results show that FOA is able to provide better results as compared to the other well-known optimization algorithms.

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