Research on an Optimization Model of Marine Industry Strategy Adjustment under Complex Ocean Conditions Based on the Ant Colony Algorithm

2019 ◽  
Vol 98 (sp1) ◽  
pp. 251
Author(s):  
Ming Chen ◽  
Jinglei Su ◽  
Peng Cui ◽  
Yuening Liu
Keyword(s):  
Optimization Model ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Marine Industry ◽  
Ocean Conditions

A metaheuristic optimization model for spectral allocation in cognitive networks based on ant colony algorithm (M-ACO)

2020 ◽  
Vol 24 (20) ◽  
pp. 15551-15560
Author(s):  
B. Padmanaban ◽  
S. Sathiyamoorthy
Keyword(s):  
Optimization Model ◽  
Ant Colony Algorithm ◽  
Cognitive Networks ◽  
Ant Colony ◽  
Metaheuristic Optimization

Optimization Design of Self-Balacing Torsion Bar Diameter

2011 ◽  
Vol 308-310 ◽  
pp. 2486-2489
Author(s):  
Zhi Qi Huang
Keyword(s):  
Optimization Model ◽  
Optimization Design ◽  
Ant Colony Algorithm ◽  
The Self ◽  
Ant Colony ◽  
New Method ◽  
C Language ◽  
Torsion Bar ◽  
Bar Diameter ◽  
Theoretical Results

The thesis builds the optimization model for the self-balacing torsion bar, On the basis of the Ant Colony Algorithm, designs the Ant Colony Algorithm procedure using C Language and optimizes torsion bar diameter. Results show the Ant Colony Algorithm is feasible and provides a new method choosing torsion bar diameter. The max difference value is 1.12% between optimizing results and theoretical results.

scholarly journals Optimizing Route for Hazardous Materials Logistics Based on Hybrid Ant Colony Algorithm

2013 ◽  
Vol 2013 ◽  
pp. 1-6 ◽  
Author(s):  
Haixing Wang ◽  
Guiping Xiao ◽  
Zhen Wei
Keyword(s):  
Optimization Model ◽  
Network Flow ◽  
Road Network ◽  
Ant Colony Algorithm ◽  
Hazardous Materials ◽  
Construction Rule ◽  
Ant Colony ◽  
Route Optimization ◽  
Total Cost ◽  
Risk Limits

Optimizing Route for Hazardous Materials Logistics (ORHML) belongs to a class of problems referred to as NP-Hard, and a strict constraint of it makes it harder to solve. In order to dealing with ORHML, an improved hybrid ant colony algorithm (HACA) was devised. To achieve the purpose of balancing risk and cost for route based on the principle of ACA that used to solve TSP, the improved HACA was designed. Considering the capacity of road network and the maximum expected risk limits, a route optimization model to minimize the total cost is established based on network flow theory. Improvement on route construction rule and pheromone updating rule was adopted on the basis of the former algorithm. An example was analyzed to demonstrate the correctness of the application. It is proved that improved HACA is efficient and feasible in solving ORHML.

scholarly journals Optimization of Gantry Cranes’ Operation Path for Transshipment Based on Improved TSP

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Qi Zhang ◽  
Hongjin Dong ◽  
Mingjun Ling ◽  
Leyi Duan ◽  
Yuguang Wei
Keyword(s):  
Optimization Model ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Gantry Crane ◽  
Railway Network ◽  
Basic Model ◽  
Loading And Unloading ◽  
Container Freight ◽  
Gantry Cranes

In order to improve the transshipment efficiency of transit containers in the port or the port-type railway network container freight station (PRNCS) with the condition that each transit container matches a railway flat-car, this paper studied the optimization of operation path of the rail mounted gantry crane (RMG) in the loading and unloading track for containers transshipped directly from highway to railway. Based on the basic model of TSP, the paper constructed the optimization model for the operation path of RMG, and designed the Ant Colony Algorithm (ACA) to solve it, and then obtained the operation scheme of RMG having the highest efficiency. Finally, the validity and correctness of the model and algorithm were verified by a case.

Optimization of Exploration Prospects Based on Ant Colony Algorithm and XGBoost Combined Optimization Model

2021 ◽  
Author(s):  
Mengxin Song ◽  
Bingxin Xu ◽  
Mei Feng ◽  
Xinxi Fu
Keyword(s):  
Optimization Model ◽  
Ant Colony Algorithm ◽  
Trap Depth ◽  
Ant Colony ◽  
Petroleum Exploration ◽  
Nonlinear Relationship ◽  
Geological Factors ◽  
Model Training ◽  
Reservoir Coefficient ◽  
Artificial Neural Network Ann

Abstract Traditional exploration prospect optimization is uncertain due to human factor, the primary reason of that problem is the complex nonlinear relationship between trap quality and related geological factors. Some researchers proposed use artificial neural network (ANN) to solve the problem of the comprehensive geological evaluation of traps, because ANN can describe the nonlinear relationship of multiple geological factors. Considering ANN has some drawbacks, such as it is need lots of parameters for training, and the learning process can not be observed. In this paper we proposed a combined optimization model to accomplish optimization of exploration prospects, and express the affinity order between the prospects and its related geological factors, also can provide the data support for exploration. Based on trap data of an oilfield in Africa, there are 12 geological factors related to trap quality, including trap coefficient, trap depth, trap scale, trap area, Reservoir coefficient, Preservation coefficient, hydrocarbon source coefficient, resources etc.. The ant colony algorithm is used for feature selection, and irrelevant and redundant features are eliminated through multiple iterations, making it suitable for model processing and improving training speed. Based on ant colony algorithm, we get the key parameters for XGBoost model training, namely trap area, reservoir coefficient, preservation coefficient, resource, and the key features are used in XGBoost model for training and prediction. Finally, we compared our prediction results with expert prediction, the error is 0. In this paper, we proposed a combined optimization model based on ant colony algorithm and XGBoost for exploration prospect optimization. We recognized the key geological factors and different characteristic rules for exploration prospect optimization, in the process of optimization, ant colony discards the bad features that interfere with classification and recognition, and retains the features that contribute greatly to classification. In comprehensive geological evaluate of trap, the proposed combined optimization model is suitable for complicated nonlinear geological relationship, and express the affinity order between the prospects, the proposed method can work as an auxiliary way in petroleum exploration, also the proposed method can provide decision support for exploration prospect optimization, and finally can fulfill cost decreasing and benefit increasing.

scholarly journals A Schedule Optimization Model on Multirunway Based on Ant Colony Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Yu Jiang ◽  
Zhaolong Xu ◽  
Xinxing Xu ◽  
Zhihua Liao ◽  
Yuxiao Luo
Keyword(s):  
Optimization Model ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Objective Functions ◽  
Total Delay ◽  
Schedule Optimization ◽  
Delay Cost ◽  
Flight Delay

In order to make full use of the slot of runway, reduce flight delay, and ensure fairness among airlines, a schedule optimization model for arrival-departure flights is established in the paper. The total delay cost and fairness among airlines are two objective functions. The ant colony algorithm is adopted to solve this problem and the result is more efficient and reasonable when compared with FCFS (first come first served) strategy. Optimization results show that the flight delay and fair deviation are decreased by 42.22% and 38.64%, respectively. Therefore, the optimization model makes great significance in reducing flight delay and improving the fairness among all airlines.

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