Cross Opposition Based Differential Evolution Optimization
Shweta Sharma1, Ashwani Kumar Yadav2, Deepak Sinwar3, Bhagyashri Naruka4, Vaishali5
1Shweta Sharma, Department of Computer Science & Engineering, Amity School of Engineering & Technology at Amity University Rajasthan, Jaipur, India.
2Ashwani Kumar Yadav, Department of Electronics & Communication Engineering, Amity School of Engineering & Technology at Amity University Rajasthan, Jaipur, India.
3Deepak Sinwar, Department of Computer and Communication Engineering, School of Computing & Information Technology at Manipal University Jaipur, Jaipur, Rajasthan, India.
4Bhagyashri Naruka, Department of Computer Science & Engineering, Amity School of Engineering & Technology at Amity University Rajasthan, Jaipur, India.
5Vaishali, Department of Computer and Communication Engineering, School of Computing & Information Technology at Manipal University Jaipur, Jaipur, Rajasthan, India.
Manuscript received on 14 March 2019 | Revised Manuscript received on 19 March 2019 | Manuscript published on 30 July 2019 | PP: 2887-2893 | Volume-8 Issue-2, July 2019 | Retrieval Number: B2283078219/19©BEIESP | DOI: 10.35940/ijrte.B2283.078219
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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: Differential Evolutionary (DE) Algorithms is one of the most popular metaheuristic approach. For optimization purpose DE is very useful to solve various kind of problems. In addition to that the paper offers a Cross-Opposition Based Differential Evolution (CODE). An impression of Opposition-based learning (OBL) is incorporated in population initialization phase and in step of crossover. The performance of algorithm is analysed for different mutation strategies of DE and various other existing approaches. Results demonstrated that the algorithm outperform in terms of convergence speed, versatile population and dimension size.
Index Terms: Differential Evolutionary (DE), Cross-Opposition Based Differential Evolution (CODE), Opposition-Based Learning (OBL), Metaheuristic Algorithm, Optimization
Scope of the Article: Discrete Optimization