Using Modified Conjugate Gradient Method to Improve SCA
Ayad Hamad Khalaf, Ban Ahmed Mitras · Journal of Physics Conference Series · 2020
Abstract This research is to improve Sine - Cosine Algorithm (SCA) that is like any other intelligent techniques that encounter some problem such as slow convergence and the dropping in local solution. To overcome these problems. SCA has been developed and improved through three directions, First: Hybrid of SCA with Modified conjugate gradient method (MCG) that has improved through that derivation of parameter of new conjugate factor (βnew) and attest its characteristic such as descent and global to construct improve algorithm called SCA-MCG. The second direction was a hybrid of SCA with classic optimization methods such as conjugate gradient (CG) algorithm to construct SCA-CG, and parallel Tangent (PT) algorithm to construct SCA-PT. Third combining both previous methods, using the Hybrid value with SCA to construct SCA-CG-PT Algorithm of high quality accounts in all directions mentioned above. To improve the initial population which randomly generated by using excellent characteristics of MCG-CG-PT as well as using this improvement as initial population for SCA. Numerical results have proved the efficiency of improved Algorithm and the results was excellent if we compared with SCA. In addition, we got optimum global values for most functions by achieving functions minimum.