Optimizing NP-Hard Problems: A Comparative Study of Metaheuristic Algorithms with Benchmark Performance Analysis
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Abstract
Metaheuristic algorithms play a key role in solving complex NP-hard optimization problems by offering scalable and efficient solutions. This study evaluates the performance of four popular metaheuristic algorithms: Genetic Algorithm (GA), Tabu Search (TS), Simulated Annealing (SA), and Ant Colony Optimization (ACO). These algorithms were tested on three NP-hard problems: Job Shop Scheduling Problem (JSSP), Vehicle Routing Problem (VRP), and Network Design Problem (NDP). Despite having different goals, these problems share common challenges such as resource allocation and conflict resolution. The algorithms were evaluated using profiling tools such as
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Publication Details
- DOI: http://dx.doi.org/10.1007/978-3-031-98161-6_19
- Type of Publication: Conference
- Conference Name: International Conference on Computational Intelligence in Engineering Science (ICCIES 2025)
- Date of Conference: 23/07/2025 - 23/07/2025
- Venue: Ton Duc Thang University No. 19, Nguyen Huu Tho Street, Tan Hung Ward, Ho Chi Minh City, Vietnam
- Organizer: Ton Duc Thang University