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Title: Fractional order ant colony control with genetic algorithm assisted initialisation

Authors: Ambreesh Kumar; Varun Upadhyaya; Ayush Singh; Paras Pandey; Rajneesh Sharma

Addresses: ECE Division, Mewar University, India ' Department of Instrumentation and Control Engineering, NSUT, Sector-3, Dwarka, New Delhi 110075, India ' Department of Instrumentation and Control Engineering, NSUT, Sector-3, Dwarka, New Delhi 110075, India ' Department of Instrumentation and Control Engineering, NSUT, Sector-3, Dwarka, New Delhi 110075, India ' Department of Instrumentation and Control Engineering, NSUT, Sector-3, Dwarka, New Delhi 110075, India

Abstract: The task of parameter initialisation of an ant colony optimisation (ACO) has gained much attention in recent years. For the systems using ACO-based control, the technique used was generally hit and trial. However, in order to be able to obtain better and faster response, along with better convergence, for control of fractional order (FO) systems, it became imperative to formulate some approach. In this paper, we have used genetic algorithm (GA) to initialise the ACO parameters for a systematic design of ACO-based fractional order controllers. The GA-based ACO fractional order PID controller is developed by minimisation of a multi-objective function using a nested GA technique. The effectiveness of the method used is verified using seven FO systems. The results are compared with the controllers based on ACO and GA. The proposed GA-based ACO controller yields reasonably better performance as compare to the existing techniques with a slight weakness of higher computational complexity. This limitation can be easily overcome by use of high performance machines.

Keywords: fractional order system; ant colony optimisation; ACO; genetic algorithm; GA-based ACO.

DOI: 10.1504/IJSI.2020.10033809

International Journal of Swarm Intelligence, 2021 Vol.6 No.1, pp.77 - 90

Received: 27 Aug 2020
Accepted: 28 Aug 2020

Published online: 05 May 2021 *

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