Moth-Dolphin Optimization Algorithm: A Nature Inspired Technique
Amit Kumar1, Sanjay Singla2, Meenakshi Sharma3, Shakti Kundu4
1Amit Kumar, Research Scholar, Department of Computer Science & Engineering, PTU University, Jalandhar, Punjab, India.
2Sanjay Singla, Department of Computer Science & Engineering Guru Gobind Singh College of Modern Technology, Kharar, Mohali, Punjab, India.
3Meenakshi Sharma, Department of Computer Science & Engineering, Chandigarh University, Kharar, Mohali, Punjab, India.
4Shakti Kundu, Faculty of Engineering & Computing Sciences, Teerthanker Mahaveer University, Moradabad, Uttar Pradesh, India.
Manuscript received on September 14, 2019. | Revised Manuscript received on 24 September, 2019. | Manuscript published on October 10, 2019. | PP: 2435-2443 | Volume-8 Issue-12, October 2019. | Retrieval Number: L30141081219/2019©BEIESP | DOI: 10.35940/ijitee.L3014.1081219
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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: This paper proposes a moth-dolphin based nature inspired optimization algorithm. This algorithm embeds the communication behavior of dolphin to Moth. The Moth route towards moon through the moon light in an optimized manner. But each moth doesn’t follow optimized path to travel towards the moon. The communication among the moth enables them to follow the best path towards moon. Thus, moth-dolphin algorithm uses the communication between moths to enhance the performance. The Moth–dolphin is an optimization algorithm which uses the exploration as well as exploitation search to achieve the global optima. The performance comparison of proposed algorithm with the moth-flame optimization algorithm, modified particle swarm optimization algorithm, differential evaluation and non-dominated sorting genetic algorithm over unimodal as well as multimodal functions ensures better results.
Keywords: Optimization, Moth, Moth-flame, Dolphin, Global Optima.
Scope of the Article: Discrete Optimization