Evolutionary optimization versus particle swarm optimization: Philosophy and performance differences

1998 ◽  
pp. 601-610 ◽  
Author(s):  
Peter J. Angeline
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Evolutionary Optimization ◽  
Swarm Optimization ◽  
Performance Differences ◽  
And Performance ◽  
Versus Particle

GENETIC ALGORITHM VERSUS PARTICLE SWARM OPTIMIZATION IN N-QUEEN PROBLEM

2007 ◽  
Vol 10 (2) ◽  
pp. 172-177
Author(s):  
Azhar W. Hammad ◽  
◽  
Dr. Ban N. Thannoon ◽  
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Versus Particle

scholarly journals Proportional + integral + derivative control of nonlinear full-car electrohydraulic suspensions using global and evolutionary optimization techniques

2019 ◽  
Vol 39 (2) ◽  
pp. 393-415
Author(s):  
Olurotimi A Dahunsi ◽  
Muhammed Dangor ◽  
Jimoh O Pedro ◽  
M Montaz Ali
Keyword(s):  
Particle Swarm Optimization ◽  
Random Search ◽  
Particle Swarm ◽  
Evolutionary Optimization ◽  
Optimization Techniques ◽  
Swarm Optimization ◽  
Modified Particle Swarm Optimization ◽  
Controlled Random Search ◽  
Evolutionary Optimization Techniques ◽  
Vehicle Suspension System

Resolving the trade-offs between suspension travel, ride comfort, road holding, vehicle handling and power consumption is the primary challenge in the design of active vehicle suspension system. Multi-loop proportional + integral + derivative controllers’ gains tuning with global and evolutionary optimization techniques is proposed to realize the best compromise between these conflicting criteria for a nonlinear full-car electrohydraulic active vehicle suspension system. Global and evolutionary optimization methods adopted include: controlled random search, differential evolution, particle swarm optimization, modified particle swarm optimization and modified controlled random search. The most improved performance was achieved with the differential evolution algorithm. The modified particle swarm optimization and modified controlled random search algorithms performed better than their predecessors, with modified controlled random search performing better than modified particle swarm optimization in all aspects of performance investigated both in time and frequency domain analyses.

The Assessment of Line-to-Plane Perpendicularity Error Based on Particle Swarm Optimization

2013 ◽  
Vol 655-657 ◽  
pp. 913-918
Author(s):  
Jia Ding Bao ◽  
Rui Zhao ◽  
Bo Xu ◽  
Yong Hou Sun
Keyword(s):  
Particle Swarm Optimization ◽  
Great Influence ◽  
Particle Swarm ◽  
Function Optimization ◽  
Multidimensional Space ◽  
Zone Method ◽  
Swarm Optimization ◽  
Space Function ◽  
Minimum Zone ◽  
And Performance

Perpendicularity error has great influence on the quality and performance of the geometrical products, and it is of great importance to guarantee the interchangeability. Particle swarm optimization (PSO) is an extraordinarily useful intelligent optimization algorithm with several advantages of fast convergence rate and easy realization for computer in the multidimensional space function optimization and dynamic target optimization. As a result, it is very accurate and is accordant with the requirement of minimum zone method (MZC) using PSO to associate the base plane of perpendicularity error.

Quantitative Feedback Theory control design using particle swarm optimization method

2011 ◽  
Vol 34 (4) ◽  
pp. 463-476 ◽  
Author(s):  
Hazem I Ali ◽  
Samsul Bahari B Mohd Noor ◽  
SM Bashi ◽  
Mohammad Hamiruce Marhaban
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Design Procedure ◽  
Pso Algorithm ◽  
Optimization Method ◽  
Quantitative Feedback Theory ◽  
Swarm Optimization ◽  
Servo Actuator ◽  
Actuator System ◽  
And Performance

In this paper, a particle swarm optimization (PSO) method is proposed to design Quantitative Feedback Theory (QFT) control. This method minimizes a proposed cost function subject to appropriate robust stability and performance QFT constraints. The PSO algorithm is simple and easy to implement, and can be used to automate the loop shaping procedures of the standard QFT. The proposed method is applied to the high uncertainty pneumatic servo actuator system as an example to illustrate the design procedure of the proposed algorithm. The proposed method is compared with the standard QFT control. The results show that the superiority of the proposed method in that it can achieve the same robustness requirements of standard QFT control with simple structure and low order controller.

scholarly journals Multiband Operation and Performance Enhancement of the PIFA Antenna by Using Particle Swarm Optimization and Overlapping Method

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
L. Wakrim ◽  
S. Ibnyaich ◽  
M. M. Hassani
Keyword(s):  
Particle Swarm Optimization ◽  
Performance Enhancement ◽  
Ground Plane ◽  
Particle Swarm ◽  
Wireless Devices ◽  
Swarm Optimization ◽  
Ansoft Hfss ◽  
And Performance ◽  
Overlapping Method ◽  
Pifa Antenna

Recently, the demand for wireless devices that support multiband frequency has increased. The integration of such technology in mobile communication system has led to a great demand in developing small size antenna with multiband operation, which is able to operate in the required system. In this paper, a novel type planar inverted F antenna (PIFA) with gridded ground plane structure and overlapping cells is presented. By controlling the overlapping size, we improve the characteristics of the proposed antenna. This antenna is developed to achieve multiband operation with small size and good performance. The particle swarm optimization (PSO) is employed to a PIFA antenna to get rid of the limitations of single band operation by searching the optimal localization and length of linear slots on the ground plane to give triband operation. This PIFA antenna can be integrated to operate for several mobile applications as Bluetooth/WLAN, WIMAX, and 4G (UMTS2100, LTE). The optimized antenna is simulated by both Ansoft HFSS and computer simulation technology microwave studio (CSTMWS) in terms of S-parameters. A good agreement between simulated performances by both software types is achieved. A parametric study is made to analyze the effect of different PIFA parameters on the operating frequency and the reflection coefficient in order to enhance the antenna performances. In these frequency bands, the antenna has nearly omnidirectional radiation pattern.

Improved Particle Swarm Optimization (PSO) for Performance Optimization of Electronic Filter Circuit Designs

2012 ◽  
Vol 229-231 ◽  
pp. 1643-1650
Author(s):  
Chong Woon Kien ◽  
Neoh Siew Chin
Keyword(s):  
Particle Swarm Optimization ◽  
Performance Optimization ◽  
Particle Swarm ◽  
Design Parameters ◽  
Pass Band ◽  
Swarm Optimization ◽  
Pass Filter ◽  
High Pass Filter ◽  
And Performance ◽  
High Pass

This article discusses and analyzes particle swarm optimization (PSO) approach in the design and performance optimization of a 4th-order Sallen Key high pass filter. Three types of particle swarm features are studied: basic PSO, PSO with regrouped particles (PSO-RP) and PSO with diversity embedded regrouped particles (PSO-DRP). PSO-RP and PSO-DRP are proposed to solve the stagnation problem of basic PSO. Based on the developed PSO approaches, LTspice is employed as the circuit simulator for the performance investigation of the designed filter. In this paper, 12 design parameters of the Sallen Key high pass filter are optimized to satisfy the required constraints and specifications on gain, cut-off frequency, and pass band ripples. Overall results show that PSO with diversity embedded regrouped particles improve the conventional search of basic PSO and has managed to achieve the design objectives.

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