International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Enhancing Support Vector Machine Classification Using a Metaheuristic Optimization Approach |
|---|---|
| ABSTRACT | The Flexible Job Shop Scheduling Problem (FJSP) is an NP-hard optimization problem in which each operation can be processed on one of several eligible machines, making scheduling highly complex in modern manufacturing systems. This paper presents a comparative study between a Support Vector Machine (SVM)-based scheduling approach and the Non-dominated Sorting Genetic Algorithm II (NSGA-II) for optimizing three key objectives: minimizing makespan, minimizing average waiting time, and minimizing total waiting time. The SVM-based model is used to support scheduling decisions through predictive prioritization, while NSGA-II performs population-based multi-objective search to generate Pareto-optimal schedules. Experimental analysis on benchmark-style instances indicates that NSGA-II generally provides stronger global optimization and better objective trade-offs, whereas SVM-based scheduling offers faster computational response and practical usefulness in near real-time environments. The findings suggest that both methods are valuable under different operational conditions, and that hybrid strategies can further improve performance in flexible scheduling contexts. |
| AUTHOR | VENCHIRYALA NITYA, BANDELA THIRUMALA, VEDAVATHI.K, DR.V. SUBBARAMAIAH, DR.K. RAJITHA Student, Department of Computer Science and Engineering, Mahatma Gandhi Institute of Technology, Gandipet, India Assistant Professor, Department of Computer Science and Engineering, Mahatma Gandhi Institute of Technology, Gandipet, India |
| VOLUME | 183 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1404101 |
| pdf/101_Enhancing Support Vector Machine Classification Using a Metaheuristic Optimization Approach.pdf | |
| KEYWORDS | |
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