Performance Evaluation of WMNs Using an Hybrid Intelligent System Based on Particle Swarm Optimization and Hill Climbing Considering Different Number of Iterations

2020 ◽  
pp. 138-149
Author(s):  
Shinji Sakamoto ◽  
Seiji Ohara ◽  
Leonard Barolli ◽  
Shusuke Okamoto
Keyword(s):  
Particle Swarm Optimization ◽  
Performance Evaluation ◽  
Intelligent System ◽  
Particle Swarm ◽  
Hill Climbing ◽  
Swarm Optimization ◽  
Hybrid Intelligent System ◽  
Number Of Iterations

A Hybrid Intelligent System for Wind Shear Encountered Aircraft Landing Control

2015 ◽  
Vol 764-765 ◽  
pp. 592-596
Author(s):  
Jih Gau Juang ◽  
Shuai Ting Yu
Keyword(s):  
Particle Swarm Optimization ◽  
Wind Shear ◽  
Controller Design ◽  
Intelligent System ◽  
Sliding Mode ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Aircraft Landing ◽  
Automatic Landing ◽  
Hybrid Intelligent System

This paper presents sliding mode control (SMC) to aircraft automatic landing system (ALS), and uses genetic algorithm (GA), particle swarm optimization (PSO) and chaos particle swarm optimization (CPSO) to adjust controller parameters. When wind shear is encountered, the aircraft automatic landing system can not be used in such environment during serious wind speed changes. The proposed intelligent control scheme can help the pilots guide the aircraft to a safe landing in wind shear condition. PID control and cerebella model articulation controller (CMAC) are applied to the controller design.

scholarly journals Improved Adaptive Holonic Particle Swarm Optimization

2019 ◽  
Vol 2019 ◽  
pp. 1-22 ◽  
Author(s):  
Hao Li ◽  
Hongbin Jin ◽  
Hanzhong Wang ◽  
Yanyan Ma
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Pso Algorithm ◽  
Standard Test ◽  
Local Optimum ◽  
Swarm Optimization ◽  
State Switching ◽  
Grouping Strategy ◽  
Number Of Iterations

For the first time , the Holonic Particle Swarm Optimization (HPSO ) algorithm applies multiagent theory about the improvement in the PSO algorithm and achieved good results. In order to further improve the performance of the algorithm, this paper proposes an improved Adaptive Holonic Particle Swarm Optimization (AHPSO) algorithm. Firstly, a brief review of the HPSO algorithm is carried out, and the HPSO algorithm can be further studied in three aspects: grouping strategy, iteration number setting, and state switching discrimination. The HPSO algorithm uses an approximately uniform grouping strategy that is the simplest but does not consider the connections between particles. And if the particles with larger or smaller differences are grouped together in different search stages, the search efficiency will be improved. Therefore, this paper proposes a grouping strategy based on information entropy and system clustering and combines two grouping strategies with corresponding search methods. The performance of the HPSO algorithm depends on the setting of the number of iterations. If it is too small, it is difficult to search for the optimal and it wastes so many computing resources. Therefore, this paper constructs an adaptive termination condition that causes the particles to terminate spontaneously after convergence. The HPSO algorithm only performs a conversion from extensive search to exact search and still has the potential to fall into local optimum. This paper proposes a state switching condition to improve the probability that the algorithm jumps out of the local optimum. Finally, AHPSO and HPSO are compared by using 22 groups of standard test functions. AHPSO is faster in convergence than HPSO, and the number of iterations of AHPSO convergence is employed in HPSO. At this point, there exists a large gap between HPSO and the optimal solution, i.e., AHPSO can have better algorithm efficiency without setting the number of iterations.

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