scholarly journals A Modified Sparrow Search Algorithm with Application in 3d Route Planning for UAV

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1224
Author(s):  
Guiyun Liu ◽  
Cong Shu ◽  
Zhongwei Liang ◽  
Baihao Peng ◽  
Lefeng Cheng
Keyword(s):  
Optimization Problem ◽  
Search Algorithm ◽  
Route Planning ◽  
Function Optimization ◽  
Target Point ◽  
Planning Problem ◽  
Departure Point ◽  
Mutation Strategy ◽  
Whale Optimization ◽  
Low Efficiency

The unmanned aerial vehicle (UAV) route planning problem mainly centralizes on the process of calculating the best route between the departure point and target point as well as avoiding obstructions on route to avoid collisions within a given flight area. A highly efficient route planning approach is required for this complex high dimensional optimization problem. However, many algorithms are infeasible or have low efficiency, particularly in the complex three-dimensional (3d) flight environment. In this paper, a modified sparrow search algorithm named CASSA has been presented to deal with this problem. Firstly, the 3d task space model and the UAV route planning cost functions are established, and the problem of route planning is transformed into a multi-dimensional function optimization problem. Secondly, the chaotic strategy is introduced to enhance the diversity of the population of the algorithm, and an adaptive inertia weight is used to balance the convergence rate and exploration capabilities of the algorithm. Finally, the Cauchy–Gaussian mutation strategy is adopted to enhance the capability of the algorithm to get rid of stagnation. The results of simulation demonstrate that the routes generated by CASSA are preferable to the sparrow search algorithm (SSA), particle swarm optimization (PSO), artificial bee colony (ABC), and whale optimization algorithm (WOA) under the identical environment, which means that CASSA is more efficient for solving UAV route planning problem when taking all kinds of constraints into consideration.

scholarly journals Fuzzy polychromatic sets constraint model for route planning problem

2020 ◽  
Vol 17 (5) ◽  
pp. 172988142095924
Author(s):  
Cai Chao ◽  
Gong Zhi Xing ◽  
Qin Xiao Wei ◽  
Zhou Qiu Shi ◽  
Sun Xi Xia
Keyword(s):  
Mutual Coupling ◽  
Optimization Problem ◽  
Route Planning ◽  
Planning System ◽  
Planning Problem ◽  
Multiobjective Optimization Problem ◽  
Constraint Condition ◽  
Operation Rules ◽  
Aerial Vehicle ◽  
Constraint Model

Unmanned aerial vehicle route planning is a complex multiconstrained multiobjective optimization problem. Due to the complexity of various constraints and the mutual coupling between them, the expression of constraint conditions is not universal and normative. The development, maintenance, and upgrading of an existing route planning system are very difficult. In this article, by establishing the polychromatic sets of aircraft, aircraft equipment, and flight actions, creating the fuzzy relational matrix between equipment and actions and between actions and actions, this article realizes the standardized and generalized expression of the constraint condition of the route planning problem. Then the analysis and inspection of the constraint conditions are realized by the polychromatic sets operation rules.

scholarly journals Multiobjective Route Planning Model and Algorithm for Emergency Management

2015 ◽  
Vol 2015 ◽  
pp. 1-17 ◽  
Author(s):  
Wen-mei Gai ◽  
Zhong-an Jiang ◽  
Yun-feng Deng ◽  
Jing Li ◽  
Yan Du
Keyword(s):  
Multiobjective Optimization ◽  
Optimization Problem ◽  
Feasible Solution ◽  
Route Planning ◽  
Weight Coefficient ◽  
Planning Problem ◽  
Emergency Logistics ◽  
Simulation Results ◽  
Route Safety ◽  
Weight Coefficients

In order to model route planning problem for emergency logistics management taking both route timeliness and safety into account, a multiobjective mathematical model is proposed based on the theories of bounded rationality. The route safety is modeled as the product of safety through arcs included in the path. For solving this model, we convert the multiobjective optimization problem into its equivalent deterministic form. We take uncertainty of the weight coefficient for each objective function in actual multiobjective optimization into account. Finally, we develop an easy-to-implement heuristic in order to gain an efficient and feasible solution and its corresponding appropriate vector of weight coefficients quickly. Simulation results show the effectiveness and feasibility of the models and algorithms presented in this paper.

Two Subpopulations Cuckoo Search Algorithm Based on Mean Evaluation Method for Function Optimization Problems

2019 ◽  
Vol 34 (08) ◽  
pp. 2059027
Author(s):  
Jingsen Liu ◽  
Xiaozhen Liu ◽  
Yu Li
Keyword(s):  
Evaluation Method ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Function Optimization ◽  
Search Performance ◽  
Step Size ◽  
Fitness Evaluation ◽  
Mutation Strategy ◽  
The Individual

In order to better apply the cuckoo search (CS) algorithm in solving the problem of function extremum optimization, and further improve the phenomenon of low precision and slow convergence in the optimization process of algorithm, the two subpopulations CS algorithm based on mean value evaluation is proposed. On the one hand, the algorithm introduces dynamic inertia weight to adjust the lévy flight mechanism, thus dynamically constraining the moving step-size of each generation of population, so that the algorithm has certain self-adaptability. On the other hand, the algorithm changes the way of mutation in the preference random walk. First, the average fitness evaluation mechanism is used to divide the current population into two subpopulations: good and bad. Then, it adopts a directional mutation strategy for the better population, so that the individual can search purposefully. The worse population uses differential mutation mechanism of the disturbance items with the [Formula: see text]-distribution characteristics, and makes the individual to search in the best orientation of current, so as to enhance the local search performance and accelerate the convergence rate of the algorithm. Theoretical analysis proves the convergence and time complexity of the algorithm in this paper. The simulation results show that the improved algorithm has good applicability in solving the function optimization problem, and the optimization results and convergence speed have been significantly improved in the algorithm.

scholarly journals Solving two-stage stochastic route-planning problem in milliseconds via end-to-end deep learning

2021 ◽  
Author(s):  
Jie Zheng ◽  
Ling Wang ◽  
Shengyao Wang ◽  
Yile Liang ◽  
Jize Pan
Keyword(s):  
Deep Learning ◽  
Programming Model ◽  
Search Algorithm ◽  
Rapid Development ◽  
Optimization Technique ◽  
Route Planning ◽  
Food Delivery ◽  
Planning Problem ◽  
Two Stage ◽  
End To End

AbstractWith the rapid development of e-economy, ordering via online food delivery platforms has become prevalent in recent years. Nevertheless, the platforms are facing lots of challenges such as time-limitation and uncertainty. This paper addresses a complex stochastic online route-planning problem (SORPP) which is mathematically formulated as a two-stage stochastic programming model. To meet the immediacy requirement of online fashion, an end-to-end deep learning model is designed which is composed of an encoder and a decoder. To embed different problem-specific features, different network layers are adopted in the encoder; to extract the implicit relationship, the probability mass functions of stochastic food preparation time is processed by a convolution neural network layer; to provide global information, the location map and rider features are handled by the factorization-machine (FM) and deep FM layers, respectively; to screen out valuable information, the order features are embedded by attention layers. In the decoder, the permutation sequence is predicted by long-short term memory cells with attention and masking mechanism. To learn the policy for finding optimal permutation under complex constraints of the SORPP, the model is trained in a supervised learning way with the labels obtained by iterated greedy search algorithm. Extensive experiments are conducted based on real-world data sets. The comparative results show that the proposed model is more efficient than meta-heuristics and is able to yield higher quality solutions than heuristics. This work provides an intelligent optimization technique for complex online food delivery system.

Route planning of intelligent bridge cranes based on an improved artificial potential field method

2021 ◽  
pp. 1-8
Author(s):  
Zhengyan Chang ◽  
Zhengwei Zhang ◽  
Qiang Deng ◽  
Zheren Li
Keyword(s):  
Potential Field ◽  
Autonomous Navigation ◽  
Route Planning ◽  
Simple Algorithm ◽  
Field Method ◽  
Artificial Potential Field ◽  
Planning Problem ◽  
Operating Characteristics ◽  
Potential Field Method ◽  
Artificial Potential Field Method

The artificial potential field method is usually applied to the path planning problem of driverless cars or mobile robots. For example, it has been applied for the obstacle avoidance problem of intelligent cars and the autonomous navigation system of storage robots. However, there have been few studies on its application to intelligent bridge cranes. The artificial potential field method has the advantages of being a simple algorithm with short operation times. However, it is also prone to problems of unreachable targets and local minima. Based on the analysis of the operating characteristics of bridge cranes, a two-dimensional intelligent running environment model of a bridge crane was constructed in MATLAB. According to the basic theory of the artificial potential field method, the double-layer artificial potential field method was deduced, and the path and track fuzzy processing method was proposed. These two methods were implemented in MATLAB simulations. The results showed that the improved artificial potential field method could avoid static obstacles efficiently.

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