Hybridization of Artificial Bee Colony Algorithm and its variants with Hyperbolic Spiral based Local Search
Shiv Kumar Agarwal1, Surendra Yadav2
1Shiv Kumar Agarwal*, Department of Computer Science and Engineering, Career Point University, Kota, Rajasthan, India.
2Surendra Yadav, Department of Computer Science and Engineering, Career Point University, Kota, Rajasthan, India.
Manuscript received on November 21, 2019. | Revised Manuscript received on December 15, 2019. | Manuscript published on December 30, 2019. | PP: 3547-3551 | Volume-9 Issue-2, December, 2019. | Retrieval Number: B4111129219/2019©BEIESP | DOI: 10.35940/ijeat.B4111.129219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Artificial bee colony (ABC) algorithm is grounded on intelligent swarming behavior of honey bees. It is one of the efficient algorithm for optimization. The ABC algorithm is good in exploration and sometimes fails to exploit properly. Local search strategies in addition to existing steps play important role to improve exploitation. In order to improve exploitation here a local search inspired by the nature of hyperbolic spiral introduced in ABC. The purposed variant used with ABC, Best-so-far ABC and Gbest ABC. Outcomes proved that hybrid of these algorithms with hyperbolic search gives good results with higher accuracy and reliability.
Keywords: Local Search, Optimization, Nature Inspired Algorithm, Swarm Intelligence.