scholarly journals Intelligent Recognition Method of Vehicle Path with Time Window Based on Genetic Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Lina Huo
Keyword(s):  
Genetic Algorithm ◽  
Particle Filter ◽  
Visual Tracking ◽  
Particle Filters ◽  
Time Window ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Bayesian Probability ◽  
Vehicle Path

Based on particle filter and improved cuckoo genetic algorithm, an algorithm for intelligent vehicle path recognition with a time window is designed. Particle filter (PF) is an influential visual tracking tool; it relies on the Monte Carlo Chain framework and Bayesian probability, which are essential for intelligent monitoring systems. The algorithm first uses particle filters for visual tracking and then obtains the current operating environment of the vehicle, then performs cluster analysis on customer locations, and finally performs path recognition in each area. The algorithm not only introduces particle filters, which are advanced visual tracking, but also improves the cuckoo search algorithm; when the bird’s egg is found by the bird’s nest owner, it needs to randomly change the position of the entire bird’s nest, which speeds up the search speed of the optimal delivery route. Analyze and compare the hybrid intelligent algorithm and the cuckoo search algorithm. Finally, the international standard test set Benchmark Problems is used for testing. The experimental outcomes indicated that the new hybrid intelligent approach is an effective algorithm for handling vehicle routing tasks with time windows.

Design of model predictive force control for hydraulic servo system based on cuckoo search and genetic algorithms

2019 ◽  
Vol 234 (6) ◽  
pp. 701-714 ◽  
Author(s):  
Mohamed El-Sayed M Essa ◽  
Magdy AS Aboelela ◽  
MA Moustafa Hassan ◽  
SM Abdrabbo
Keyword(s):  
Genetic Algorithm ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Search Algorithm ◽  
Servo System ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Switching Model ◽  
Hydraulic Servo ◽  
Hydraulic Servo System

This article discusses a system identification based on a black-box state-space model for an experimental electro-hydraulic servo system. Furthermore, it presents force-tracking control for the electro-hydraulic servo system based on model predictive control. The parameters of model predictive controls have been tuned by cuckoo search algorithm as well as genetic algorithm. The realization of model predictive controls depends on using a data acquisition card (NI-6014) and Simulink/MATLAB as the core of the electro-hydraulic servo system control system. In this research, the combination of model predictive control tuned by cuckoo search algorithm and genetic algorithm has been introduced in the form of switching model predictive controls. This combination collects the advantages of two model predictive controls in one model predictive control by switching model predictive controls. The simulation and experimental results display that the suggested switching of model predictive controls introduces a good tracking performance in terms of settling time, rise time, and system overshoots as compared to the two separated model predictive controls. In addition, the experimental evaluation has shown that the proposed switching model predictive controls achieved a stable and robust control system even facing to a different reference command signals (step, multistep, and sinusoidal signals). Moreover, its behavior is more robust for system parameters perturbation and small or large perturbation of disturbances in the working environment. It also achieves the necessitated physical limits of the actuator. As a general conclusion and a deep study of electro-hydraulic servo system, one can conclude that the switching strategy between model predictive control tuned by cuckoo search algorithm and by genetic algorithm has the priority of applying it on the field of electro-hydraulic servo system. The proposed new strategy (switching of model predictive control) is promising in experimental applications.

A Hybrid Algorithm Using Genetic Algorithm and Cuckoo Search Algorithm to Solve Job Scheduling Problem in Computational Grid Systems

2016 ◽  
Vol 7 (2) ◽  
pp. 1-11 ◽  
Author(s):  
Tarun Kumar Ghosh ◽  
Sanjoy Das
Keyword(s):  
Genetic Algorithm ◽  
Hybrid Algorithm ◽  
Job Scheduling ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Distributed Resources ◽  
Computing Systems ◽  
Grid Systems ◽  
Job Scheduling Problem

Job scheduling is one of the major challenges in Grid computing systems to efficiently exploit the capabilities of dynamic, autonomous, heterogeneous and distributed resources for execution of different types of jobs. Thus optimal job scheduling is an NP-complete problem which can easily be solved by using heuristic techniques. This paper presents a hybrid algorithm for job scheduling using Genetic Algorithm (GA) and Cuckoo Search Algorithm (CSA) for efficiently allocating jobs to resources in a Grid system so that makespan and flowtime are minimized. This proposed algorithm combines the advantages of both GA and CSA. The authors' results have been compared with standard GA, CSA and Ant Colony Optimization (ACO) to show the importance of the proposed algorithm.

scholarly journals A fuzzy adaptive controller for cuckoo search algorithm in active suspension system

2018 ◽  
Vol 39 (3) ◽  
pp. 761-771
Author(s):  
Chun-Tang Chao ◽  
Ming-Tang Liu ◽  
Chi-Jo Wang ◽  
Juing-Shian Chiou
Keyword(s):  
Genetic Algorithm ◽  
Pid Controller ◽  
Optimization Problem ◽  
Controller Design ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Local Optima ◽  
Pid Controller Design ◽  
Fuzzy Adaptive

This paper presents a fuzzy adaptive cuckoo search algorithm to improve the cuckoo search algorithm, which may easily fall into a local optimum when handling multiobjective optimization problems. The Fuzzy–Proportional-Integral-Derivative (PID) controller design for an active micro-suspension system has been incorporated into the proposed fuzzy adaptive cuckoo search algorithm to improve both driving comfort and road handling. In the past research, a genetic algorithm was often applied in Fuzzy–PID controller design. However, when the dimension is high and there are numerous local optima, the traditional genetic algorithm will not only have a premature convergence, but may also be trapped in the local optima. In the proposed fuzzy adaptive cuckoo search, all parameters of the PID controller and fuzzy rules are real coded to 75 bits in the optimization problem. Moreover, a fuzzy adaptive strategy is proposed for dynamically adjusting the learning parameters in the fuzzy adaptive cuckoo search, and this indeed enables global convergence. Experimental results verify that the proposed fuzzy adaptive cuckoo search algorithm can shorten the computing time in the evolution process and increase accuracy in the multiobjective optimization problem.

Visual tracking method based on cuckoo search algorithm

2015 ◽  
Vol 54 (7) ◽  
pp. 073105 ◽  
Author(s):  
Ming-Liang Gao ◽  
Li-Ju Yin ◽  
Guo-Feng Zou ◽  
Hai-Tao Li ◽  
Wei Liu
Keyword(s):  
Visual Tracking ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Tracking Method
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