Flight Parameter Model Based Route Planning Method of UAV Using Stepped-Adaptive Improved Particle Swarm Optimization

With the continuous prosperity and development of the shipping industry, it is necessary and meaningful to plan a safe, green, and efficient route for ships sailing far away. In this study, a hybrid multicriteria ship route planning method based on improved particle swarm optimization–genetic algorithm is presented, which aims to optimize the meteorological risk, fuel consumption, and navigation time associated with a ship. The proposed algorithm not only has the fast convergence of the particle swarm algorithm but also improves the diversity of solutions by applying the crossover operation, selection operation, and multigroup elite selection operation of the genetic algorithm and improving the Pareto optimal frontier distribution. Based on the Pareto optimal solution set obtained by the algorithm, the minimum-navigation-time route, the minimum-fuel-consumption route, the minimum-navigation-risk route, and the recommended route can be obtained. Herein, a simulation experiment is conducted with respect to a container ship, and the optimization route is compared and analyzed. Experimental results show that the proposed algorithm can plan a series of feasible ship routes to ensure safety, greenness, and economy and that it provides route selection references for captains and shipping companies.

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Image Segmentation to HSI Model Based on Improved Particle Swarm Optimization

Emerging Intelligent Computing Technology and Applications. With Aspects of Artificial Intelligence - Lecture Notes in Computer Science ◽

10.1007/978-3-642-04020-7_81 ◽

2009 ◽

pp. 757-765 ◽

Cited By ~ 1

Author(s):

Bo Zhao ◽

Yajun Chen ◽

Wenhua Mao ◽

Xiaochao Zhang

Keyword(s):

Particle Swarm Optimization ◽

Image Segmentation ◽

Particle Swarm ◽

Swarm Optimization ◽

Improved Particle Swarm Optimization ◽

Model Based

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Application of a Hybrid Model Based on Echo State Network and Improved Particle Swarm Optimization in PM2.5 Concentration Forecasting: A Case Study of Beijing, China

Sustainability ◽

10.3390/su11113096 ◽

2019 ◽

Vol 11 (11) ◽

pp. 3096 ◽

Cited By ~ 7

Author(s):

Xinghan Xu ◽

Weijie Ren

Keyword(s):

Air Pollution ◽

Particle Swarm Optimization ◽

Air Quality ◽

Hybrid Model ◽

Particle Swarm ◽

Echo State Network ◽

Swarm Optimization ◽

Improved Particle Swarm Optimization ◽

Model Based ◽

Pm2.5 Concentration

With the acceleration of urbanization, there is an increasing trend of heavy pollution. PM2.5, also known as fine particulate matter, refers to particles in the atmosphere with a diameter of less than or equal to 2.5 microns. PM2.5 has a serious impact on human life, a sustainable city, national economic development, and so on. How to forecast the PM2.5 concentration accurately, and then formulate a scientific air pollution prevention and monitoring program is of great significance. This paper proposes a hybrid model based on echo state network (ESN) and an improved particle swarm optimization (IPSO) algorithm for the Beijing air pollution problem, and provides a method for PM2.5 concentration forecasting. Firstly, the PSO algorithm is improved to speed up the search performance. Secondly, the optimal subset of the original data is selected by the convergence cross-mapping (CCM) method. Thirdly, the phase space reconstruction (PSR) process is combined with the forecasting model, and some parameters are optimized by the IPSO. Finally, the optimal variable subset is used to predict PM2.5 concentration. The 11-dimensional air quality data in Beijing from January 1 to December 31, 2016 are analyzed by the proposed method. The experimental results show that the hybrid method is superior to other comparative models in several evaluation indicators, both in one-step and multi-step forecasting of PM2.5 time series. The hybrid model has good application prospects in air quality forecasting and monitoring.

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