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Intelligent optimization algorithm Neural network prediction Radar communication Wireless sensor Power system
Signal processing Image processing Path planning Cellular automaton Drone
Content introduction
With the continuous development of science and technology, people’s demand for intelligence is also getting higher and higher. On campus, campus trolley food delivery has become a common service method, but how to efficiently plan the route of campus trolley food delivery has become an urgent problem to be solved. In this problem, we can use the ant colony algorithm for path planning to improve the efficiency of food delivery. This article will introduce the research algorithm process of campus car food delivery path planning problem based on ant colony algorithm.
First, let’s learn about the ant colony algorithm. Ant colony algorithm is an algorithm that simulates the foraging behavior of ants in nature. It simulates the process of ants releasing pheromones and finding the shortest path when looking for food. By simulating the movement of ants in space and the release of pheromones, the ant colony algorithm can find the optimal path. In the planning of the food delivery path of the campus car, we can regard the food delivery point as a food source and the campus car as an ant. We can find the optimal food delivery path by simulating the movement of ants in the campus and the release of pheromones.
Next, let us take a look at the research algorithm process of campus car food delivery route planning problem based on ant colony algorithm. First, we need to initialize a group of ants and randomly place them in different locations on the campus map. The ants then began to move around the campus map according to certain rules, releasing pheromones with each movement. When the ants arrive at the food delivery point, they will choose the next food delivery point based on the pheromone concentration. By constantly iterating and updating pheromones, the ants will eventually find the optimal food delivery path.
In the algorithm process, we need to consider the settings of some parameters, such as the amount of pheromone released, the volatilization rate of pheromone, the movement rules of ants, etc. The settings of these parameters will directly affect the convergence speed and final results of the algorithm. Therefore, we need to find optimal parameter settings through experiments and parameter adjustments to improve the efficiency and accuracy of the algorithm.
In addition to parameter settings, we also need to consider the characteristics of the campus map. The campus map may have some obstacles, restricted areas, etc., which will affect the operation of the ant colony algorithm. Therefore, before path planning, we need to preprocess the campus map and take these obstacles and restricted areas into consideration to ensure the accuracy and feasibility of the algorithm.
In general, the problem of campus food delivery path planning based on ant colony algorithm is a topic worthy of study. Through reasonable algorithm process design, parameter settings and campus map processing, we can improve the efficiency of campus cart meal delivery and bring a better service experience to the campus. I hope that the introduction of this article can be helpful to related research and practical work, and also hope that it can attract more people’s attention and research on intelligent campus services.
Part of the code
%Start function</code><code>function Start()</code><code> figure('Name', 'Matlab Team', 'NumberTitle', 'off\ ');</code><code> text(0.5, 0.9, 'Matlab team sets off!', 'HorizontalAlignment', 'center', 'VerticalAlignment', 'middle', 'FontSize', 20);</code><code> text(0.5, 0.2, 'Please modify the food delivery information input in the Start() function.', 'HorizontalAlignment', 'center\ ', 'VerticalAlignment', 'middle', 'FontSize', 14);</code><code> %data import</code><code> global Inmeal;</code><code> DateB();</code><code>?</code><code> %Food delivery input, each food delivery cart can have up to 30 servings, maximum 5 carts</code><code> %Chongshi Chengpuyuan</code><code> Inmeal = struct('C1', 0, 'C2', 0, 'C3',0, 'C4', 0);</code><code> num = sum(struct2array(Inmeal));</code><code> if num == 0</code><code> disp("Tangmen has not detected the need for food delivery.");</code> <code> elseif num <= 150</code><code> MA3();</code><code> else</code><code> disp("Each park can send up to 150 copies, Chongshi Park and There are not enough cars in Cheng Puyuan, so I haven’t given away one, so we are on strike!");</code><code> end</code><code>?</code><code> %Science Group</code> code><code> Inmeal = struct('L1', 0, 'L2', 0, 'L3', 0, 'L4', 0);</code><code> num = sum(struct2array(Inmeal));</code><code> if num == 0</code><code> disp("The science group has not detected the need for food delivery. ");</code><code> elseif num <= 150</code><code> MA4();</code><code> else</code><code> disp("Max. 150 copies were given away. The science group didn’t have enough cars, so we didn’t give out any copies. Strike!");</code><code> end</code><code>?</code><code> % Qizhiyuan</code><code> Inmeal = struct('Q1', 20, 'Q2', 15, 'Q3', 35, 'Q4', 5, 'Q5\ ', 16, 'Q6', 21);</code><code> num = sum(struct2array(Inmeal));</code><code> if num == 0</code><code> disp ("Qizhiyuan has not detected the need for food delivery.");</code><code> elseif num <= 150</code><code> MA1();</code><code> else</code><code> disp("Each park can give away a maximum of 150 copies. The buses in Qizhi Park are not enough, so we didn’t give out one copy. Strike!");</code><code> end</code><code>?
Operation results
References
[1] Yang Fuxing, Wang Fei. Research on the distribution path planning problem of emergency supplies after emergencies based on improved ant colony algorithm [J]. Logistics Engineering and Management, 2016, 38(11):3.DOI:10.3969/j. issn.1674-4993.2016.11.033.
[2] Li Jianjun. Research on vehicle path planning problem based on ant colony algorithm [D]. Xi’an University of Electronic Science and Technology, 2015. DOI: 10.7666/d.D01066551.
[3] Wei Ziqiu, Sun Mingzhe. Research on solving VRPTW path planning problem based on ant colony algorithm [J]. Logistics Technology, 2022, 45(3):5.
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1 Improvements and applications of various intelligent optimization algorithms
Production scheduling, economic scheduling, assembly line scheduling, charging optimization, workshop scheduling, departure optimization, reservoir scheduling, three-dimensional packing, logistics location selection, cargo space optimization, bus scheduling optimization, charging pile layout optimization, workshop layout optimization, Container ship stowage optimization, water pump combination optimization, medical resource allocation optimization, facility layout optimization, visible area base station and drone site selection optimization
2 Machine learning and deep learning
Convolutional neural network (CNN), LSTM, support vector machine (SVM), least squares support vector machine (LSSVM), extreme learning machine (ELM), kernel extreme learning machine (KELM), BP, RBF, width Learning, DBN, RF, RBF, DELM, XGBOOST, TCN realize wind power prediction, photovoltaic prediction, battery life prediction, radiation source identification, traffic flow prediction, load prediction, stock price prediction, PM2.5 concentration prediction, battery health status prediction, water body Optical parameter inversion, NLOS signal identification, accurate subway parking prediction, transformer fault diagnosis
2. Image processing
Image recognition, image segmentation, image detection, image hiding, image registration, image splicing, image fusion, image enhancement, image compressed sensing
3 Path planning
Traveling salesman problem (TSP), vehicle routing problem (VRP, MVRP, CVRP, VRPTW, etc.), UAV three-dimensional path planning, UAV collaboration, UAV formation, robot path planning, raster map path planning , multimodal transportation problems, vehicle collaborative UAV path planning, antenna linear array distribution optimization, workshop layout optimization
4 UAV applications
UAV path planning, UAV control, UAV formation, UAV collaboration, UAV task allocation, and online optimization of UAV safe communication trajectories
5 Wireless sensor positioning and layout
Sensor deployment optimization, communication protocol optimization, routing optimization, target positioning optimization, Dv-Hop positioning optimization, Leach protocol optimization, WSN coverage optimization, multicast optimization, RSSI positioning optimization
6 Signal processing
Signal recognition, signal encryption, signal denoising, signal enhancement, radar signal processing, signal watermark embedding and extraction, EMG signal, EEG signal, signal timing optimization
7 Power system aspects
Microgrid optimization, reactive power optimization, distribution network reconstruction, energy storage configuration
8 Cellular Automata
Traffic flow, crowd evacuation, virus spread, crystal growth
9 Radar aspect
Kalman filter tracking, track correlation, track fusion
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