Authors: Jonti Deuri; S. Siva Sathya
Addresses: Department of Computer Science, Pondicherry University, Pondicherry, 605014, India ' Department of Computer Science, Pondicherry University, Pondicherry, 605014, India
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.
Keywords: optimisation; meta-heuristic algorithm; numerical function; cuckoo search; artificial bee colony; ABC; particle swarm optimisation; PSO; genetic algorithm; calling chirp; aggressive chirp; cricket chirping algorithm; CCA.
International Journal of Computational Science and Engineering, 2018 Vol.16 No.2, pp.162 - 172
Available online: 05 Mar 2018 *Full-text access for editors Access for subscribers Purchase this article Comment on this article