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Comparative Excellence of Metaheuristic Algorithms in Stochastic TSP: Navigating Partial Visibility and Dynamic Edge Weights for Disaster Management Optimization

Students & Supervisors

Student Authors
Eaoumoon Haque
Bachelor of Science in Computer Science & Engineering, FST
Tahsinul Islam Nishat
Bachelor of Science in Computer Science & Engineering, FST
Ragib Nadim
Bachelor of Science in Computer Science & Engineering, FST
Noshin Saiyara Rahman
Bachelor of Science in Computer Science & Engineering, FST
Supervisors
Md. Faruk Abdullah Al Sohan
Lecturer, Faculty, FST

Abstract

Stochastic Traveling Salesman Problem (S-TSP) with Partial Visibility and Randomized Edge Weights is a critical challenge in disaster management where uncertainty and dynamic conditions hinder effective rescue operations. To address this, five optimization algorithms, Genetic Algorithm (GA), Ant Colony Optimization (ACO), Tabu Search (TS), Simulated Annealing (SA), and Nearest Neighbor Heuristic (NNH), are implemented using Code::Blocks IDE and evaluated through simulation. Chrono measured execution time, providing insights into algorithmic efficiency under dynamic conditions. Memory utilization and computational overhead were analyzed using gperftools, which monitored evictions, interrupts, and byte usage. Valgrind's Callgrind assessed instruction counts, offering a detailed evaluation of algorithm scalability and resource management. The comparative analysis highlighted NNH as the most effective algorithm, balancing computational efficiency with solution quality, making it highly suitable for real-time disaster response applications. The profiling tools reinforced these findings, identifying NNH’s capability to optimize memory and computation resources effectively. This study underscores the importance of using adaptable and efficient algorithms in disaster management, where rapid and accurate decision-making is crucial.

Keywords

Stochastic Traveling Salesman Problem Comparative analysis Disaster Response Metaheuristic Algorithm NP-Hard Problem.

Publication Details

  • Type of Publication: Conference 
  • Conference Name: The 8th International Conference on Engineering Research, Innovation and Education (ICERIE 2025)
  • Date of Conference: 24/04/2025 - 24/04/2025
  • Venue: Shahjalal University of Science and Technology University Ave, Kumargaon, Sylhet 3114, Bangladesh
  • Organizer: School of Applied Sciences & Technology, Shahjalal University of Science and Technology
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