scholarly journals A Novel Multi-Objective and Multi-Constraint Route Recommendation Method Based on Crowd Sensing

2021 ◽  
Vol 11 (21) ◽  
pp. 10497
Author(s):  
Xiaoyao Zheng ◽  
Yonglong Luo ◽  
Liping Sun ◽  
Qingying Yu ◽  
Ji Zhang ◽  
...  
Keyword(s):  
Ant Colony Algorithm ◽  
Search Algorithm ◽  
Route Planning ◽  
Neighborhood Search ◽  
Scoring Method ◽  
Multi Objective ◽  
Crowd Sensing ◽  
Planning Algorithm ◽  
Route Recommendation ◽  
Public Datasets

Nowadays, people choose to travel in their leisure time more frequently, but fixed predetermined tour routes can barely meet people’s personalized preferences. The needs of tourists are diverse, largely personal, and possibly have multiple constraints. The traditional single-objective route planning algorithm struggles to effectively deal with such problems. In this paper, a novel multi-objective and multi-constraint tour route recommendation method is proposed. Firstly, ArcMap was used to model the actual road network. Then, we created a new interest label matching method and a utility function scoring method based on crowd sensing, and constructed a personalized multi-constraint interest model. We present a variable neighborhood search algorithm and a hybrid particle swarm genetic optimization algorithm for recommending Top-K routes. Finally, we conducted extensive experiments on public datasets. Compared with the ATP route recommendation method based on an improved ant colony algorithm, our proposed method is superior in route score, interest abundance, number of POIs, and running time.

scholarly journals Three-Dimensional Unmanned Aerial Vehicle Route Planning Using Hybrid Differential Evolution

2020 ◽  
Vol 24 (7) ◽  
pp. 820-828
Author(s):  
Hao Zhang ◽  
Lihua Dou ◽  
Chunxiao Cai ◽  
Bin Xin ◽  
◽  
...  
Keyword(s):  
Differential Evolution ◽  
Search Algorithm ◽  
Dynamic Properties ◽  
Three Dimensional ◽  
Route Planning ◽  
Performance Comparison ◽  
Solution Quality ◽  
De Algorithm ◽  
Planning Algorithm ◽  
Aerial Vehicle

Unmanned aerial vehicles (UAVs) have been investigated proactively owing to their promising applications. A route planner is key to UAV autonomous task execution. Herein, a hybrid differential evolution (HDE) algorithm is proposed to generate a high-quality and feasible route for fixed-wing UAVs in complex three-dimensional environments. A multiobjective function is designed, and both the route length and risk are optimized. Multiple constraints based on actual situations are considered, including UAV mobility, terrain, forbidden flying areas, and interference area constraints. Inspired by the wolf pack search algorithm, the proposed HDE algorithm combines differential evolution (DE) with an approaching strategy to improve the search capability. Moreover, considering the dynamic properties of fixed-wing UAVs, the quadratic B-spline curve is used for route smoothing. The HDE algorithm is compared with a state-of-the-art UAV route planning algorithm, i.e., the modified wolf pack search algorithm, and the traditional DE algorithm. Several numerical experiments are performed, and the performance comparison of algorithms shows that the HDE algorithm demonstrates better performances in terms of solution quality and constraint-handling ability in complex three-dimensional environments.

Multi-Helicopter Search and Rescue Route Planning Based on Strategy Optimization Algorithm

2018 ◽  
Vol 33 (01) ◽  
pp. 1950002 ◽  
Author(s):  
Quan Shao ◽  
Chenchen Xu ◽  
Yan Zhu
Keyword(s):  
Ant Colony Algorithm ◽  
Route Planning ◽  
Behavioral Model ◽  
Search And Rescue ◽  
Planning Model ◽  
Cooperative Search ◽  
Rescue Operation ◽  
Algorithm Efficiency ◽  
Planning Algorithm ◽  
Efficient Route

This paper attempts to develop an efficient route planning algorithm to guide the operations of the multi-helicopter search and rescue in emergency. Route planning model of multi-helicopter cooperative search and rescue activity was established first, based on preference ordering of search and rescue objectives, as well as behavioral model of rescue helicopter and on-board detector. Given the route planning model, a multi-helicopter search and rescue route planning general algorithm was developed. The operation mechanism of ant colony algorithm was improved by introducing cooperative modes and the pheromone updating mechanism into existing methods. Furthermore, two cooperative search and rescue modes were studied: one is Overall Cooperative Search and Rescue Mode (OCSARM), in which many ants search and rescue the same region all together; the other is Blocking Cooperative Search and Rescue Mode (BCSARM), which partitions the region into small blocks and appoints helicopter with corresponding performance capabilities. Simulated experiments were developed to test the operability of proposed multi-helicopter search and rescue route planning algorithm. The comparison with existing algorithm shows that the algorithm proposed in this paper reduces computational complexity and evidently enhances algorithm efficiency. Results also indicate that this algorithm not only has the capability of comparing efficiency of two search and rescue modes in different mission requirements but also helps select search and rescue modes before rescue operation.

scholarly journals Multi-Objective Neighborhood Search Algorithm Based on Decomposition for Multi-Objective Minimum Weighted Vertex Cover Problem

2019 ◽  
Vol 11 (13) ◽  
pp. 3634
Author(s):  
Shuli Hu ◽  
Xiaoli Wu ◽  
Huan Liu ◽  
Yiyuan Wang ◽  
Ruizhi Li ◽  
...  
Keyword(s):  
Search Algorithm ◽  
Vertex Cover ◽  
Score Function ◽  
Search Space ◽  
Single Type ◽  
Neighborhood Search ◽  
Multi Objective ◽  
Vertex Cover Problem ◽  
Weighted Vertex ◽  
Cover Problem

The multi-objective minimum weighted vertex cover problem aims to minimize the sum of different single type weights simultaneously. In this paper, we focus on the bi-objective minimum weighted vertex cover and propose a multi-objective algorithm integrating iterated neighborhood search with decomposition technique to solve this problem. Initially, we adopt the decomposition method to divide the multi-objective problem into several scalar optimization sub-problems. Meanwhile, to find more possible optimal solutions, we design a mixed score function according to the problem feature, which is applied in initializing procedure and neighborhood search. During the neighborhood search, three operators ( A d d , D e l e t e , S w a p ) explore the search space effectively. We performed numerical experiments on many instances, and the results show the effectiveness of our new algorithm (combining decomposition and neighborhood search with mixed score) on several experimental metrics. We compared our experimental results with the classical multi-objective algorithm non-dominated sorting genetic algorithm II. It was obviously shown that our algorithm can provide much better results than the comparative algorithm considering the different metrics.

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