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All Journal Jambura Journal of Mathematics IJISCS (International Journal of Information System and Computer Science)
Ali, Ayad
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Mathematics

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A NOVEL APPROACH: THREE-GROUP EXPLORATION STRATEGY ALGORITHM FOR SOLVING OPTIMIZATION PROBLEMS Ali, Ayad
IJISCS (International Journal of Information System and Computer Science) Vol 9, No 2 (2025): IJISCS (International Journal of Information System and Computer Science)
Publisher : Bakti Nusantara Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56327/ijiscs.v9i2.1774

Abstract

In this study, we present a novel optimization technique, known as the Three-Group Exploration Strategy (TGES) algorithm, specifically inspired by collaborative group dynamics often seen in problem-solving. We showed wide testing on 26 widely-recognized benchmark functions, providing a severe comparison between TGES and several well-established optimization algorithms. These results highlight TGES’s effectiveness in finding optimal solutions with high reliability and accuracy. Furthermore, the practical applications of TGES are demonstrated by successfully solving six interesting, real-world engineering problems, showcasing its adaptability and robustness. The experimental results indicate that TGES not only exhibits superior optimization performance, but it also achieves faster convergence and higher solution quality compared to several leading algorithms. This finds TGES algorithm as a strong and adaptable tool for solving a variety of engineering optimization problems.
A Hybrid Grey Wolf Optimizer–Zebra Optimization Algorithm for Solving Optimization Problems Ali, Ayad
Jambura Journal of Mathematics Vol 8, No 1: February 2026
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjom.v8i1.34499

Abstract

Metaheuristic algorithms are widely applied to complex optimization problems, yet many suffer from premature convergence or slow search efficiency. To report these limitations, this paper proposes a new hybrid algorithm, Grey Wolf Optimizer–Zebra Optimization Algorithm (GWO–ZOA). The algorithm integrates the exploitation ability of the Grey Wolf Optimizer with the exploration capability of the Zebra Optimization Algorithm in a sequential framework, thereby enhancing both convergence accuracy and global search ability. The performance of GWO–ZOA is first evaluated on 23 standard benchmark functions, where it demonstrates competitive results in both unimodal and multimodal landscapes. Further validation is carried out on the CEC2017 and CEC2020 benchmark suites, confirming the hybrid’s robustness across higher-dimensional and more challenging composite problems. In all three benchmark categories, the Friedman statistical test ranks GWO–ZOA first among the compared algorithms, highlighting its superior overall performance. Finally, the algorithm is applied to two real-world engineering design problems, where it consistently achieves high-quality feasible solutions and demonstrates practical effectiveness. These results confirm that the proposed GWO–ZOA algorithm is both robust and reliable for solving diverse and complex optimization tasks.
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