Study on the planning of rural land spatial utilization by improved particle swarm optimization

The planning of rural land space utilization is a very important problem. In this paper, the objective function of rural land use planning was analyzed firstly, and then the improved particle swarm optimization (IPSO) algorithm was obtained by improving the inertia weight for solution. The results showed that the land space use in the study area was more reasonable after the planning based on the IPSO algorithm, the forest land and construction land increased, the area of grassland, cultivated land and water area reduced appropriately, the aggregation degree of all types of land improved, and the space distribution was more planned, which was more conducive to production activities. The analysis results verify the effectiveness of the IPSO method in land space use planning, which can improve the efficiency and benefit of land space use, and it can be popularized in practical application.

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Parameter Optimization of PID Controller Based on an Improved Particle Swarm Optimization for the Induction Motor

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.130-134.1938 ◽

2011 ◽

Vol 130-134 ◽

pp. 1938-1942

Author(s):

Xia Bo Shi ◽

Wei Xing Lin

Keyword(s):

Particle Swarm Optimization ◽

Induction Motor ◽

Parameter Optimization ◽

Pid Controller ◽

Dynamic Performance ◽

Particle Swarm ◽

Motor Speed ◽

Swarm Optimization ◽

Improved Particle Swarm Optimization ◽

Ipso Algorithm

This paper presents a new approach of PID parameter optimization for the induction motor speed system by using an improved particle swarm optimization (IPSO). The induction motor speed is changed by the stator voltage controlled with PID controller. The performance of PID controller based on IPSO is compared to Linearly Decreasing Inertia Weight (LIWPSO). Simulation results demonstrate that the IPSO algorithm has better dynamic performance, higher accuracy and faster convergence and good performance for the PID controller.

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An Improved Particle Swarm Optimization Algorithm Based on Centroid and Exponential Inertia Weight

Mathematical Problems in Engineering ◽

10.1155/2014/976486 ◽

2014 ◽

Vol 2014 ◽

pp. 1-14 ◽

Cited By ~ 8

Author(s):

Shouwen Chen ◽

Zhuoming Xu ◽

Yan Tang ◽

Shun Liu

Keyword(s):

Particle Swarm Optimization ◽

Optimization Algorithm ◽

Particle Swarm Optimization Algorithm ◽

Particle Swarm ◽

Pso Algorithm ◽

Swarm Optimization ◽

Local Optima ◽

Improved Particle Swarm Optimization ◽

Inertia Weight ◽

Ipso Algorithm

Particle swarm optimization algorithm (PSO) is a global stochastic tool, which has ability to search the global optima. However, PSO algorithm is easily trapped into local optima with low accuracy in convergence. In this paper, in order to overcome the shortcoming of PSO algorithm, an improved particle swarm optimization algorithm (IPSO), based on two forms of exponential inertia weight and two types of centroids, is proposed. By means of comparing the optimization ability of IPSO algorithm with BPSO, EPSO, CPSO, and ACL-PSO algorithms, experimental results show that the proposed IPSO algorithm is more efficient; it also outperforms other four baseline PSO algorithms in accuracy.

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Gravity field inversion using Improved Particle Swarm Optimization (IPSO) for estimation of sedimentary basin basement depth

Contributions to Geophysics and Geodesy ◽

10.31577/congeo.2020.50.3.2 ◽

2020 ◽

Vol 50 (3) ◽

pp. 303-323

Author(s):

Soudeh LONI ◽

Mahmoud MEHRAMUZ

Keyword(s):

Particle Swarm Optimization ◽

Sedimentary Basin ◽

Gravity Data ◽

Particle Swarm ◽

Model Parameters ◽

Swarm Optimization ◽

Improved Particle Swarm Optimization ◽

Basement Depth ◽

Optimum Solution ◽

Ipso Algorithm

In this paper, for the first time an Improved Particle Swarm Optimization (IPSO) algorithm, is developed to evaluate the 2.5-D basement of sedimentary basin and consequently to simulate its bottom, by using the density contrast that varies parabolically with depth simultaneously. The IPSO method is capable of improving the global search of particles in all of the search fields. Finding the optimum solution is adjusted by an inertia weight and acceleration coefficients. Here, we have examined the ability of the IPSO inversion by the synthetic gravity data due to a sedimentary basin, with and without noise. The calculated depth and gravity of the synthetic model do not differ too much from assumed values due to set limits for model parameters and are always within the range. Also, the mentioned method has been applied for the 2.5-D gravity inverse modelling of a sedimentary basin in Iran. We also have modelled the sedimentary basin in 2-D along seven profiles. Furthermore, using the depth values estimated by IPSO from all profiles, a 3-D model of the sedimentary basin was generated. The obtained maximum depth for this sedimentary basin is 2.62 km.

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Optimal Sensor Placement for Latticed Shell Structure Based on an Improved Particle Swarm Optimization Algorithm

Mathematical Problems in Engineering ◽

10.1155/2014/743904 ◽

2014 ◽

Vol 2014 ◽

pp. 1-12 ◽

Cited By ~ 9

Author(s):

Xun Zhang ◽

Juelong Li ◽

Jianchun Xing ◽

Ping Wang ◽

Qiliang Yang ◽

...

Keyword(s):

Particle Swarm Optimization ◽

Large Scale ◽

Sensor Placement ◽

Particle Swarm ◽

Optimal Placement ◽

Optimal Sensor Placement ◽

Swarm Optimization ◽

Improved Particle Swarm Optimization ◽

Comparison Results ◽

Ipso Algorithm

Optimal sensor placement is a key issue in the structural health monitoring of large-scale structures. However, some aspects in existing approaches require improvement, such as the empirical and unreliable selection of mode and sensor numbers and time-consuming computation. A novel improved particle swarm optimization (IPSO) algorithm is proposed to address these problems. The approach firstly employs the cumulative effective modal mass participation ratio to select mode number. Three strategies are then adopted to improve the PSO algorithm. Finally, the IPSO algorithm is utilized to determine the optimal sensors number and configurations. A case study of a latticed shell model is implemented to verify the feasibility of the proposed algorithm and four different PSO algorithms. The effective independence method is also taken as a contrast experiment. The comparison results show that the optimal placement schemes obtained by the PSO algorithms are valid, and the proposed IPSO algorithm has better enhancement in convergence speed and precision.

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