Differential Evolution Algorithm using Environment Based Operator
Shailendra Pratap Singh1, Deepak Kumar Singh2
1Shailendra Pratap Singh*, Department of Computer Science and Engineering, Bundelkhand Institute of Engineering and Technology, Jhansi, U.P., India.
2Dr. Deepak Kumar Singh, Director, Sachdeva Institute of Technology, Mathura, UP, India.
Manuscript received on January 02, 2020. | Revised Manuscript received on January 15, 2020. | Manuscript published on January 30, 2020. | PP: 128-137 | Volume-8 Issue-5, January 2020. | Retrieval Number: D9774118419/2020©BEIESP | DOI: 10.35940/ijrte.D9774.018520
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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: In this paper, new mutation strategies are proposed to improve the accuracy of the cost estimation by COCOMO’s tuning parameters using the Internal adaption based mutation operator for differential evolution algorithm (IABMO Algorithm). The proposed method provides more promising solutions to take the lead evolution and helps DE abstain the circumstance of stability. The proposed algorithm applied software cost estimation and improve the performance of the initial phase for software engineering. This approach is used for precise prediction and reduces the error rate for the initial phase of software development phase projects. The software cost estimation based IABMO algorithm has been capable of a better for effort, MRE, MMRE, and prediction.
Keywords: Evolutionary Algorithm, Software Engineering, Optimization, COCOMO Model.
Scope of the Article: Artificial Intelligence Approaches To Software Engineering.