A Comparative Study of Genetic Algorithm and Variable Neighborhood Search for Solving the Single Depot Multiple Set Orienteering Problem

Alapfogalmak

The Genetic Algorithm (GA) and Variable Neighborhood Search (VNS) are viable methods for solving the single Depot multiple Set Orienteering Problem (sDmSOP), with VNS demonstrating superior performance in terms of both solution quality and computational time compared to GA and CPLEX.

Kivonat

This research paper presents a comparative study evaluating the performance of Genetic Algorithm (GA) and Variable Neighborhood Search (VNS) in solving the single Depot multiple Set Orienteering Problem (sDmSOP). The sDmSOP extends the traditional Set Orienteering Problem (SOP) by considering multiple travelers starting and ending at a single depot, aiming to maximize profit collection from visiting a subset of clusters within a predefined budget.

Bibliographic Information: Kant, R., Agarwal, S., Gupta, A., & Mishra, A. (2024). Exploring the Performance of Genetic Algorithm and Variable Neighborhood Search for Solving the Single Depot Multiple Set Orienteering Problem: A Comparative Study. arXiv:2411.12300v1 [math.OC].

Research Objective: This study aims to compare the effectiveness of GA and VNS metaheuristics in solving the sDmSOP and benchmark their performance against the optimal solutions obtained using Integer Linear Programming (ILP) for smaller instances.

Methodology: The researchers developed GA and VNS algorithms tailored for the sDmSOP, incorporating specific chromosome representation, genetic operators, and neighborhood search procedures. They evaluated the algorithms using benchmark instances derived from the Generalized Traveling Salesman Problem (GTSP), considering varying numbers of nodes and travelers. The ILP formulation of the sDmSOP was solved using GAMS 37.1.0 with CPLEX to obtain optimal solutions for smaller instances.

Key Findings: The computational results demonstrate that both GA and VNS provide feasible solutions for the sDmSOP. However, VNS consistently outperforms GA in terms of solution quality, achieving higher profits within the given budget constraints. Moreover, VNS exhibits significantly faster computational times compared to both GA and CPLEX, particularly for larger instances.

Main Conclusions: The study concludes that VNS is a more effective metaheuristic than GA for solving the sDmSOP, offering a better balance between solution quality and computational efficiency. The ILP formulation, while providing optimal solutions for small instances, becomes computationally expensive for larger problem sizes.

Significance: This research contributes to the field of combinatorial optimization by providing insights into the performance of different metaheuristics for solving the sDmSOP, a practically relevant problem with applications in logistics, transportation, and routing.

Limitations and Future Research: The study acknowledges the limitations of using benchmark instances derived from the GTSP and suggests exploring the performance of GA and VNS on real-world sDmSOP datasets. Further research directions include investigating the impact of different parameter settings on the algorithms' performance and exploring hybrid approaches combining the strengths of both GA and VNS.

Statisztikák

CPLEX was unable to solve 5 out of 20 small GTSP instances with less than 100 nodes optimally due to Out Of Memory (OOM) errors.
For instances with two travelers, GA demonstrated faster computational times compared to VNS.
As the number of travelers increased to three, GA's computational time increased significantly, while VNS maintained relatively consistent performance.
VNS consistently produced solutions with higher profits compared to GA for all tested instances.

Idézetek

Főbb Kivonatok

Exploring the Performance of Genetic Algorithm and Variable Neighborhood Search for Solving the Single Depot Multiple Set Orienteering Problem: A Comparative Study

by Ravi Kant, S... : arxiv.org 11-20-2024

https://arxiv.org/pdf/2411.12300.pdf

Mélyebb kérdések

How do the computational complexities of GA and VNS scale with increasing problem size and complexity in the context of the sDmSOP?

When analyzing the computational complexities of Genetic Algorithms (GA) and Variable Neighborhood Search (VNS) applied to the single Depot multiple Set Orienteering Problem (sDmSOP), it's crucial to consider how they are affected by the problem's growing size and intricacy.

Genetic Algorithms (GA): The complexity of GAs is primarily influenced by the population size and the number of generations required to reach a satisfactory solution. As the sDmSOP problem size increases (more vertices, clusters, or travelers), larger populations and more generations are typically needed to effectively explore the expanded solution space. This leads to a higher computational burden. Additionally, evaluating the fitness of each chromosome within the GA becomes more complex with larger problem instances, further contributing to the increased computational time.

Variable Neighborhood Search (VNS): VNS, on the other hand, tends to scale more favorably with problem size compared to GAs. The complexity of VNS is mainly determined by the number of neighborhood structures employed and the efficiency of the local search procedures within each neighborhood. While larger problem instances might require exploring more neighborhoods or performing more extensive local searches, VNS generally exhibits a more controlled growth in computational complexity compared to GAs. This advantage stems from VNS's focus on systematically exploring neighborhoods around a current solution rather than managing a large population of candidate solutions.
The paper's findings, particularly the results presented in Table 2, provide evidence to support these observations. As the problem size increases from Set 1 to Set 3, the computational time for both GA and VNS rises. However, the increase in computational time for GA is significantly more pronounced, especially when the number of travelers (t) is increased from 2 to 3. This suggests that GA's reliance on population-based search becomes a bottleneck as the sDmSOP becomes more complex. In contrast, VNS maintains a more manageable computational time, highlighting its suitability for larger sDmSOP instances.

Could the performance of GA be improved by incorporating problem-specific knowledge or heuristics within its operators to compete with VNS in solving the sDmSOP?

Yes, the performance of Genetic Algorithms (GA) in solving the sDmSOP can be significantly enhanced by integrating problem-specific knowledge or heuristics into its operators. This strategic incorporation can lead to a more competitive edge against VNS, especially for larger and more complex problem instances. Here's how:

Intelligent Initialization: Instead of starting with a completely random population, GA can leverage heuristics or domain knowledge to generate an initial set of solutions that are already closer to the optimal. For instance, in the context of sDmSOP, one could use a nearest neighbor heuristic or a clustering algorithm to create initial routes for the travelers. This can significantly reduce the search space and accelerate convergence.

Specialized Crossover Operators: The standard crossover operators in GA might not always produce offspring that are feasible or effective for the sDmSOP. Designing specialized crossover operators that take into account the constraints of the problem, such as the budget constraint and the requirement of visiting at least one vertex from each selected cluster, can lead to more promising offspring and faster convergence.

Informed Mutation Strategies: Similar to crossover, mutation operators can be tailored to the sDmSOP. Instead of random mutations, one could introduce mutations that intelligently perturb the existing routes, such as swapping clusters between travelers while respecting the budget constraint, or reordering clusters within a traveler's route to minimize the total distance.

Adaptive Parameter Control: The performance of GA is sensitive to the choice of parameters like population size, crossover rate, and mutation rate. Incorporating adaptive mechanisms that dynamically adjust these parameters based on the characteristics of the search space or the progress of the algorithm can lead to more efficient exploration and exploitation of the search space.

By embedding problem-specific knowledge within these operators, GA can evolve more effective solutions that adhere to the sDmSOP's constraints. This targeted approach can significantly reduce the computational burden associated with exploring irrelevant or infeasible regions of the solution space, making GA more competitive with VNS, particularly when dealing with the complexities of larger sDmSOP instances.

What are the potential applications of the sDmSOP in emerging fields such as robotics, drone delivery systems, or autonomous vehicle routing, and how can GA and VNS be adapted to address the unique challenges posed by these domains?

The sDmSOP, with its focus on optimizing routes for multiple travelers to maximize profit within a budget, holds significant potential in various emerging fields. Let's explore some key applications:

Robotics:

Multi-Robot Exploration and Mapping: In scenarios involving a team of robots tasked with exploring an unknown environment and mapping it efficiently, the sDmSOP can be employed to plan routes for each robot. The robots, acting as travelers, can be assigned to explore different sections of the environment (clusters) while maximizing the information gathered (profit) within their limited operational time or energy (budget).
Warehouse Automation: Imagine a warehouse with multiple robots collecting and delivering items. The sDmSOP can be used to optimize the picking and delivery routes for these robots, ensuring timely order fulfillment while minimizing energy consumption and operational costs.

Drone Delivery Systems:

Last-Mile Delivery Optimization: With the rise of e-commerce, efficient last-mile delivery is crucial. sDmSOP can be applied to coordinate a fleet of delivery drones, assigning them to deliver packages to different geographical areas (clusters) while maximizing the number of successful deliveries within their battery life and flight range constraints.
Emergency Response and Disaster Relief: In time-critical situations like delivering medical supplies or essential goods during emergencies, sDmSOP can help plan efficient routes for multiple drones, ensuring the swift and effective distribution of aid within the affected region.

Autonomous Vehicle Routing:

Ride-Sharing and Ride-Hailing Services: Platforms like Uber and Lyft can leverage sDmSOP to efficiently assign passengers (clusters) to drivers (travelers), maximizing driver earnings and customer satisfaction while considering factors like pickup and drop-off locations, traffic conditions, and vehicle capacity.
Freight Transportation and Logistics: Optimizing routes for a fleet of autonomous trucks transporting goods across long distances can be achieved using sDmSOP. The algorithm can consider factors like delivery deadlines, fuel efficiency, and road conditions to minimize transportation costs and delivery times.

Adapting GA and VNS for these domains:
While GA and VNS provide a solid foundation for solving sDmSOP, they need adaptations to address the unique challenges of these emerging fields:

Dynamic Environments: Unlike static problems, real-world scenarios often involve dynamic elements like changing traffic conditions, new orders, or obstacles. Both GA and VNS need to incorporate mechanisms for real-time adaptation and re-planning to maintain solution quality in dynamic environments.
Heterogeneous Fleets: In many applications, the travelers (robots, drones, vehicles) might have different capabilities, such as varying battery life, carrying capacity, or speed. GA and VNS need to be modified to handle these heterogeneous fleets, ensuring that solutions are feasible and optimal considering the specific constraints of each traveler.
Collaboration and Communication: In multi-agent systems, effective collaboration and communication between agents are crucial. Adapting GA and VNS to incorporate communication protocols and collaborative strategies can lead to more efficient and robust solutions.
By addressing these challenges, GA and VNS can be effectively utilized to unlock the full potential of sDmSOP in these emerging fields, paving the way for more efficient, cost-effective, and robust solutions in robotics, drone delivery systems, and autonomous vehicle routing.