An Improved Gradient-Based Optimization Algorithm for Solving Complex Optimization Problems

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 weigh...

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Veröffentlicht in:Processes Jg. 11; H. 2; S. 498
Hauptverfasser: Altbawi, Saleh Masoud Abdallah, Khalid, Saifulnizam Bin Abdul, Mokhtar, Ahmad Safawi Bin, Shareef, Hussain, Husain, Nusrat, Yahya, Ashraf, Haider, Syed Aqeel, Moin, Lubna, Alsisi, Rayan Hamza
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 01.02.2023
Schlagworte:
Accuracy
Algorithms
Analysis
Benchmarks
Convergence
Friedman, Milton
Intelligence
Mathematical optimization
Optimization
Optimization algorithms
Parameter modification
Statistical analysis
Germany
ISSN:2227-9717, 2227-9717
Online-Zugang:Volltext
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
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ISSN:2227-9717
2227-9717
DOI:10.3390/pr11020498