scholarly journals Constrained Fitness Landscape Analysis of Capacitated Vehicle Routing Problems

Entropy ◽  
2021 ◽  
Vol 24 (1) ◽  
pp. 53
Author(s):  
Sebastián Muñoz-Herrera ◽  
Karol Suchan
Keyword(s):  
Vehicle Routing ◽  
Fitness Landscape ◽  
Solution Space ◽  
Search Space ◽  
Landscape Analysis ◽  
Vehicle Routing Problems ◽  
Routing Problems ◽  
Fitness Landscape Analysis ◽  
Multiple Traveling Salesman Problem ◽  
The Impact

Vehicle Routing Problems (VRP) comprise many variants obtained by adding to the original problem constraints representing diverse system characteristics. Different variants are widely studied in the literature; however, the impact that these constraints have on the structure of the search space associated with the problem is unknown, and so is their influence on the performance of search algorithms used to solve it. This article explores how assignation constraints (such as a limited vehicle capacity) impact VRP by disturbing the network structure defined by the solution space and the local operators in use. This research focuses on Fitness Landscape Analysis for the multiple Traveling Salesman Problem (m-TSP) and Capacitated VRP (CVRP). We propose a new Fitness Landscape Analysis measure that provides valuable information to characterize the fitness landscape’s structure under specific scenarios and obtain several relationships between the fitness landscape’s structure and the algorithmic performance.

scholarly journals Fitness Landscape Analysis and Edge Weighting-Based Optimization of Vehicle Routing Problems

Processes ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 1363
Author(s):  
László Kovács ◽  
Anita Agárdi ◽  
Tamás Bányai
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Fitness Landscape ◽  
Fitness Function ◽  
Search Space ◽  
Landscape Analysis ◽  
Discrete Optimization Problem ◽  
Routing Problem ◽  
Fitness Landscape Analysis ◽  
Fitness Value

Vehicle routing problem (VRP) is a highly investigated discrete optimization problem. The first paper was published in 1959, and later, many vehicle routing problem variants appeared to simulate real logistical systems. Since vehicle routing problem is an NP-difficult task, the problem can be solved by approximation algorithms. Metaheuristics give a “good” result within an “acceptable” time. When developing a new metaheuristic algorithm, researchers usually use only their intuition and test results to verify the efficiency of the algorithm, comparing it to the efficiency of other algorithms. However, it may also be necessary to analyze the search operators of the algorithms for deeper investigation. The fitness landscape is a tool for that purpose, describing the possible states of the search space, the neighborhood operator, and the fitness function. The goal of fitness landscape analysis is to measure the complexity and efficiency of the applicable operators. The paper aims to investigate the fitness landscape of a complex vehicle routing problem. The efficiency of the following operators is investigated: 2-opt, order crossover, partially matched crossover, cycle crossover. The results show that the most efficient one is the 2-opt operator. Based on the results of fitness landscape analysis, we propose a novel traveling salesman problem genetic algorithm optimization variant where the edges are the elementary units having a fitness value. The optimal route is constructed from the edges having good fitness value. The fitness value of an edge depends on the quality of the container routes. Based on the performed comparison tests, the proposed method significantly dominates many other optimization approaches.

Evolutionary Ant Colony Algorithm Using Firefly Based Transition for Solving Vehicle Routing Problems

2019 ◽  
Vol 10 (3) ◽  
pp. 46-60
Author(s):  
Rajeev Goel ◽  
Raman Maini
Keyword(s):  
Vehicle Routing ◽  
Ant Colony Algorithm ◽  
Optimization Problems ◽  
Search Space ◽  
Ant Colony ◽  
Ant Colony System ◽  
Vehicle Routing Problems ◽  
Routing Problems ◽  
Routing Problem ◽  
Firefly Optimization

Vehicle routing problems are a classical NP-hard optimization problem. In this article we propose an evolutionary optimization algorithm which adapts the advantages of ant colony optimization and firefly optimization to solve vehicle routing problem and its variants. Firefly optimization (FA) based transition rules and a novel pheromone shaking rule is proposed to escape local optima. Whereas the multi-modal nature of FA explores the search space, pheromone shaking avoids the stagnation of pheromones on the exploited paths. This is expected to improve working of an ant colony system (ACS). Performance of the proposed algorithm is compared with the performance of some of other currently available meta-heuristic approaches for solving vehicle routing problems (VRP) by applying it to certain standard benchmark datasets. Results show that the proposed approach is consistent and its convergence rate is faster. The results also demonstrate the superiority of the proposed approach over some of the other existing FA-based approaches for solving such type of discrete optimization problems.

Sign in / Sign up

Export Citation Format

Share Document