A Comparison of Particle Swarm Optimization and Genetic Algorithm Based on Multi-objective Approach for Optimal Composite Nonlinear Feedback Control of Vehicle Stability System

2016 ◽  
pp. 652-662 ◽  
Author(s):  
Liyana Ramli ◽  
Yahaya Md Sam ◽  
Zaharuddin Mohamed
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Feedback Control ◽  
Particle Swarm ◽  
Vehicle Stability ◽  
Nonlinear Feedback Control ◽  
Nonlinear Feedback ◽  
Composite Nonlinear Feedback ◽  
Swarm Optimization ◽  
Multi Objective

Composite Nonlinear Feedback Control with Multi-objective Particle Swarm Optimization for Active Front Steering System

2015 ◽  
Vol 72 (2) ◽  
Author(s):  
Liyana Ramli ◽  
Yahaya Md. Sam ◽  
Zaharuddin Mohamed ◽  
M. Khairi Aripin ◽  
M. Fahezal Ismail
Keyword(s):  
Parameter Estimation ◽  
Particle Swarm Optimization ◽  
Nonlinear Feedback ◽  
Nonlinear Gain ◽  
Composite Nonlinear Feedback ◽  
Swarm Optimization ◽  
Vehicle Handling ◽  
Multi Objective ◽  
Yaw Rate ◽  
Active Front Steering

The purpose of controlling the vehicle handling is to ensure that the vehicle is in a safe condition and following its desire path. Vehicle yaw rate is controlled in order to achieve a good vehicle handling. In this paper, the optimal Composite Nonlinear Feedback (CNF) control technique is proposed for an Active Front Steering (AFS) system for improving the vehicle yaw rate response. The model used in order to validate the performance of controller is nonlinear vehicle model with 7 degree-of-freedom (DOF) and a bicycle model is implemented for the purpose of designing the controller. In designing an optimal CNF controller, the parameter estimation of linear and nonlinear gain becomes very important to produce the best output response. An intelligent algorithm is designed to minimize the time consumed to get the best parameter. To design an optimal method, Multi Objective Particle Swarm Optimization (MOPSO) is utilized to optimize the CNF controller performance. As a result, transient performance of the yaw rate has improved with the increased speed of in tracking and searching of the best optimized parameter estimation for the linear and the nonlinear gain of CNF controller.  

Multi-objective integrated planning and scheduling model for operating rooms under uncertainty

2018 ◽  
Vol 232 (9) ◽  
pp. 930-948 ◽  
Author(s):  
Javad Ansarifar ◽  
Reza Tavakkoli-Moghaddam ◽  
Faezeh Akhavizadegan ◽  
Saman Hassanzadeh Amin
Keyword(s):  
Genetic Algorithm ◽  
Decision Making ◽  
Particle Swarm Optimization ◽  
Heuristic Algorithms ◽  
Particle Swarm ◽  
Operating Rooms ◽  
Programming Approach ◽  
Test Problems ◽  
Swarm Optimization ◽  
Multi Objective

This article formulates the operating rooms considering several constraints of the real world, such as decision-making styles, multiple stages for surgeries, time windows for resources, and specialty and complexity of surgery. Based on planning, surgeries are assigned to the working days. Then, the scheduling part determines the sequence of surgeries per day. Moreover, an integrated fuzzy possibilistic–stochastic mathematical programming approach is applied to consider some sources of uncertainty, simultaneously. Net revenues of operating rooms are maximized through the first objective function. Minimizing a decision-making style inconsistency among human resources and maximizing utilization of operating rooms are considered as the second and third objectives, respectively. Two popular multi-objective meta-heuristic algorithms including Non-dominated Sorting Genetic Algorithm and Multi-Objective Particle Swarm Optimization are utilized for solving the developed model. Moreover, different comparison metrics are applied to compare the two proposed meta-heuristics. Several test problems based on the data obtained from a public hospital located in Iran are used to display the performance of the model. According to the results, Non-dominated Sorting Genetic Algorithm-II outperforms the Multi-Objective Particle Swarm Optimization algorithm in most of the utilized metrics. Moreover, the results indicate that our proposed model is more effective and efficient to schedule and plan surgeries and assign resources than manual scheduling.

A Multi-Objective Hybrid Algorithm for Optimization of Grid Structures

2018 ◽  
Vol 10 (01) ◽  
pp. 1850009 ◽  
Author(s):  
Zhe Xiong ◽  
Xiao-Hui Li ◽  
Jing-Chang Liang ◽  
Li-Juan Li
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Convergence Rate ◽  
Pareto Optimality ◽  
Hybrid Algorithm ◽  
Particle Swarm ◽  
Mathematical Functions ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Structural Problems

In this study, a novel multi-objective hybrid algorithm (MHGH, multi-objective HPSO-GA hybrid algorithm) is developed by crossing the heuristic particle swarm optimization (HPSO) algorithm with a genetic algorithm (GA) based on the concept of Pareto optimality. To demonstrate the effectiveness of the MHGH, the optimizations of four unconstrained mathematical functions and four constrained truss structural problems are tested and compared to the results using several other classic algorithms. The results show that the MHGH improves the convergence rate and precision of the particle swarm optimization (PSO) and increases its robustness.

Multi-Objective Particle Swarm Optimization for Control Laws Design

2013 ◽  
Vol 333-335 ◽  
pp. 1361-1365
Author(s):  
Xiao Xiong Liu ◽  
Heng Xu ◽  
Yan Wu ◽  
Peng Hui Li
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Tracking Error ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Control Laws ◽  
Reference Models ◽  
Multi Objective ◽  
Simulation Results

In order to overcome the difficult of large amount of calculation and to satisfy multiple design indicators in the design of control laws, an improved multi-objective particle swarm optimization (PSO) algorithm was used to design control laws of aircraft. Firstly, the hybrid concepts of genetic algorithm were introduced to particle swarm optimization (PSO) algorithm to improve the algorithm. Then based on aircraft flying quality the reference models were built, and then the tracking error, settling time and overshoot were used as the optimization goal of the control laws design. Based on this multi-objective optimize problem the attitude hold control laws were designed. The simulation results show the effectiveness of the algorithm.

Four-Variable Simultaneous Optimization of the Cooling and Acoustic Power with Particle Swarm Optimization

2020 ◽  
Vol 28 (02) ◽  
pp. 2050012
Author(s):  
It Sing Chan ◽  
Normah Mohd Ghazali ◽  
Nor Atiqah Zolpakar ◽  
Maziah Mohamad
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Acoustic Power ◽  
Simultaneous Optimization ◽  
Cooling Power ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Conflicting Objectives ◽  
Thermoacoustic Refrigerator

The low performance of the thermoacoustic refrigerator has made it uncompetitive to currently available refrigeration systems and hence its path towards commercialization is being restricted. Recently, evolutionary algorithm such as genetic algorithm has become popular among researchers in optimizing the performance of the thermoacoustic refrigerator due to its capability to provide a solution with a global maximum or minimum through simultaneous optimization of several objectives. The purpose of this study was to maximize the performance of the thermoacoustic refrigerator using the Multi-Objective Particle Swarm Optimization (MOPSO), an evolutionary optimization tool that has not been tried in this field before. By optimizing the two conflicting objectives which are maximizing the cooling power and minimizing the acoustic power required, simultaneous optimization of inter-dependent controlling parameters has been performed for two, three and four parameters. Comparing with the results of past studies, MOPSO has improved the stack COP by 6.92% compared to the parametric optimization approach and 2.96% higher than the maximum COP achieved by multi-objective genetic algorithm (MOGA) with an optimum COP of 1.39. Also, a maximum cooling power of 10.8 W was obtained. This study has highlighted the potential of MOPSO in providing optimized conditions for conflicting objectives desired for a thermoacoustic system.

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