TY - JOUR
T1 - An Improved Gradient-Based Optimization Algorithm for Solving Complex Optimization Problems
AU - Altbawi, Saleh Masoud Abdallah
AU - Khalid, Saifulnizam Bin Abdul
AU - Mokhtar, Ahmad Safawi Bin
AU - Shareef, Hussain
AU - Husain, Nusrat
AU - Yahya, Ashraf
AU - Haider, Syed Aqeel
AU - Moin, Lubna
AU - Alsisi, Rayan Hamza
N1 - Funding Information:
The first author thanks the Libyan government for supporting this research with a scholarship provided by the Ministry of Higher Education and Scientific Research. The authors would like to acknowledge the facilities provided by Universiti Teknologi Malaysia for the accomplishment of this work. The authors would like to thank the editor and anonymous reviewers for improving this paper with their valuable comments and suggestions.
Publisher Copyright:
© 2023 by the authors.
PY - 2023/2
Y1 - 2023/2
N2 - In this paper, an improved gradient-based optimizer (IGBO) is proposed with the target of improving the performance and accuracy of the algorithm for solving complex optimization and engineering problems. The proposed IGBO has the added features of adjusting the best solution by adding inertia weight, fast convergence rate with modified parameters, as well as avoiding the local optima using a novel functional operator (G). These features make it feasible for solving the majority of the nonlinear optimization problems which is quite hard to achieve with the original version of GBO. The effectiveness and scalability of IGBO are evaluated using well-known benchmark functions. Moreover, the performance of the proposed algorithm is statistically analyzed using ANOVA analysis, and Holm–Bonferroni test. In addition, IGBO was assessed by solving well-known real-world problems. The results of benchmark functions show that the IGBO is very competitive, and superior compared to its competitors in finding the optimal solutions with high convergence and coverage. The results of the studied real optimization problems prove the superiority of the proposed algorithm in solving real optimization problems with difficult and indefinite search domains.
AB - In this paper, an improved gradient-based optimizer (IGBO) is proposed with the target of improving the performance and accuracy of the algorithm for solving complex optimization and engineering problems. The proposed IGBO has the added features of adjusting the best solution by adding inertia weight, fast convergence rate with modified parameters, as well as avoiding the local optima using a novel functional operator (G). These features make it feasible for solving the majority of the nonlinear optimization problems which is quite hard to achieve with the original version of GBO. The effectiveness and scalability of IGBO are evaluated using well-known benchmark functions. Moreover, the performance of the proposed algorithm is statistically analyzed using ANOVA analysis, and Holm–Bonferroni test. In addition, IGBO was assessed by solving well-known real-world problems. The results of benchmark functions show that the IGBO is very competitive, and superior compared to its competitors in finding the optimal solutions with high convergence and coverage. The results of the studied real optimization problems prove the superiority of the proposed algorithm in solving real optimization problems with difficult and indefinite search domains.
KW - engineering optimization problems
KW - gradient-based optimizer
KW - improve gradient-based optimizer
KW - inertia
KW - metaheuristic
KW - operator
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U2 - 10.3390/pr11020498
DO - 10.3390/pr11020498
M3 - Article
AN - SCOPUS:85149152200
SN - 2227-9717
VL - 11
JO - Processes
JF - Processes
IS - 2
M1 - 498
ER -