Mathematics / Statistics

Baskar A This email address is being protected from spambots. You need JavaScript enabled to view it.1

1Panimalar Institute of Technology, Poonamallee, Tamil Nadu 600123, India

 

Received: December 11, 2022
Accepted: February 2, 2022
Publication Date: March 1, 2022

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.


Download Citation: ||https://doi.org/10.6180/jase.202212_25(6).0020  


ABSTRACT

Heuristics play a key role in solving optimization problems with complex functions. They are popular as efficient heuristics are capable of providing results quickly with acceptable solution quality. Population-based heuristics are stochastic and hence, several iterations and trials are needed to achieve the expected accuracy and convergence to the global optimum. This article proposes two new, simple; population-based trigonometric algorithms, Sine (AB) and Cosine (AB). The algorithms are validated using forty well-known benchmark test functions available in the literature. The results are compared with a similar popular Sine Cosine Algorithm and the computational results show that the performance of Sine (AB) and Cosine (AB) are better than Sine Cosine Algorithm. Wilcoxon Signed-Rank and Friedman tests are carried out for statistical analyses. In addition to unconstrained functions, three real-world, constrained problems are solved to have a more intensive analysis of the proposed algorithms.


Keywords: Optimization, Population-based heuristic, Un-constrained optimization, Sine Cosine Algorithm, Trigonometric Algorithm


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