Development of Exponentially Weighted Sine Cosine Optimization Algorithm

This paper focuses on developing an exponentially weighted sine cosine algorithm that helps achieve faster convergence and better global optimal solutions. The proposed algorithm has been achieved by incorporating exponential weight functions in the position updation equations that change during eac...

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Veröffentlicht in:2021 IEEE Madras Section Conference (MASCON) S. 1 - 6
Hauptverfasser: Mantri, Rhea, Selvaraj, Kaushik Ram, Maiti, Monalisa, Bingi, Kishore, Kulkarni, Rakshit Raghavendra
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 27.08.2021
Schlagworte:
Adaptation
benchmark functions
Benchmark testing
Conferences
Convergence
exponential weights
IEEE Sections
Optimization
sine cosine algorithm
statistical analysis
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Zusammenfassung:This paper focuses on developing an exponentially weighted sine cosine algorithm that helps achieve faster convergence and better global optimal solutions. The proposed algorithm has been achieved by incorporating exponential weight functions in the position updation equations that change during each algorithm iteration. Further, a comparison of the proposed algorithm with the traditional technique is made on various benchmark functions-selecting these functions of multiple categories such as unimodal, multimodal, and composite. The optimization results indicated that the proposed exponentially weighted algorithm performed superior to the traditional and other adaptive weighted algorithms. The best performance is proper for most functions concerning mean, best, and standard deviation values. Moreover, the conventional technique has reached the worst global minima values for most of the test functions than the proposed algorithm.
DOI:10.1109/MASCON51689.2021.9563377