Cricket chirping algorithm: an efficient meta-heuristic for numerical function optimisation Online publication date: Mon, 05-Mar-2018
by Jonti Deuri; S. Siva Sathya
International Journal of Computational Science and Engineering (IJCSE), Vol. 16, No. 2, 2018
Abstract: Nature-inspired meta-heuristic algorithms have proved to be very powerful in solving complex optimisation problems in recent times. The literature reports several inspirations from nature, exploited to solve computational problems. This paper is yet another step in the journey towards the utilisation of natural phenomena for seeking solutions to complex optimisation problems. In this paper, a new meta-heuristic algorithm based on the chirping behaviour of crickets is formulated to solve optimisation problems. It is validated against various benchmark test functions and then compared with popular state-of-the-art optimisation algorithms like genetic algorithm, particle swarm optimisation, bat algorithm, artificial bee colony algorithm and cuckoo search algorithm for performance efficiency. Simulation results show that the proposed algorithm has outperformed its counterparts in terms of speed and accuracy. The implication of the results and suggestions for further research are also discussed.
Online publication date: Mon, 05-Mar-2018
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Computational Science and Engineering (IJCSE):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email firstname.lastname@example.org