System Identification of Dampers Using Chaotic Accelerated Particle Swarm Optimization

2021 ◽  
Vol 01 ◽  
Author(s):  
S. Talatahari ◽  
B. Talatahari ◽  
M. Tolouei
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Linear Optimization ◽  
Particle Swarm ◽  
Model Parameters ◽  
Identification Algorithm ◽  
Swarm Optimization ◽  
Non Linear ◽  
Linear Optimization Problems ◽  
Accelerated Particle Swarm Optimization

Aims: Different chaotic APSO-based algorithms are developed to deal with high non-linear optimization problems. Then, considering the difficulty of the problem, an adaptation of these algorithms is presented to enhance the algorithm. Background: : Particle swarm optimization (PSO) is a population-based stochastic optimization technique suitable for global optimization with no need for direct evaluation of gradients. The method mimics the social behavior of flocks of birds and swarms of insects and satisfies the five axioms of swarm intelligence, namely proximity, quality, diverse response, stability, and adaptability. There are some advantages to using the PSO consisting of easy implementation and a smaller number of parameters to be adjusted; however, it is known that the original PSO had difficulties in controlling the balance between exploration and exploitation. In order to improve this character of the PSO, recently, an improved PSO algorithm, called the accelerated PSO (APSO), was proposed, and preliminary studies show that the APSO can perform superiorly. Objective: This paper presents several chaos-enhanced accelerated particle swarm optimization methods for high non-linear optimization problems. Method: Some modifications to the APSO-based algorithms are performed to enhance their performance. Then, the algorithms are employed to find the optimal parameters of the various types of hysteretic Bouc-Wen models. The problems are solved by the standard PSO, APSO, different CAPSO, and adaptive CAPSO, and the results provide the most useful method. Result: Seven different chaotic maps have been investigated to tune the main parameter of the APSO. The main advantage of the CAPSO is that there is a fewer number of parameters compared with other PSO variants. In CAPSO, there is only one parameter to be tuned using chaos theory. Conclusion: To adapt the new algorithm for susceptible parameter identification algorithm, two series of Bouc-Wen model parameters containing standard and modified Bouc-Wen models are used. Performances are assessed on the basis of the best fitness values and the statistical results of the new approaches from 20 runs with different seeds. Simulation results show that the CAPSO method with Gauss/mouse, Liebovitch, Tent, and Sinusoidal maps performs satisfactorily. Other: The sub-optimization mechanism is added to these methods to enhance the performance of the algorithm.

Minimum-Zone Circularity Evaluation Using Particle Swarm Optimization

2005 ◽  
Author(s):  
Hemant Ramaswami ◽  
Yashpal Kovvur ◽  
Sam Anand
Keyword(s):  
Particle Swarm Optimization ◽  
Evolutionary Algorithms ◽  
High Speed ◽  
Optimization Problems ◽  
Linear Optimization ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Non Linear ◽  
Minimum Zone ◽  
Accuracy And Repeatability

Robust and accurate evaluation of form tolerances is of paramount importance in today’s world of precision engineering. Present-day Coordinate Measuring Machines (CMMs) operate at high speed and have a high degree of accuracy and repeatability which are capable of meeting the stringent measurement requirements. However, the evaluation algorithms used in conjunction with them are not robust and accurate enough, because of the highly non-linear nature of the minimum-zone circularity formulation. Evolutionary algorithms have proved effective in solving constrained non-linear optimization problems. In this paper, Particle Swarm Optimization (PSO), which is one of the most recent and popular evolutionary algorithms, is employed to evaluate the minimum-zone circularity. The PSO approach imitates the social behavior of organisms such as bird flocking and fish schooling. It differs from other well-known Evolutionary Algorithms (EA) in that each particle of the population, called the swarm, adjusts its trajectory toward its own previous best position, and toward the previous best position attained by any member of its topological neighborhood. The constrained nonlinear model is rewritten as an unconstrained non-linear model using the penalty-function approach. The methodology is validated by testing on several simulated and experimental datasets and yields better results than other existing minimum-zone algorithms.

Crosshole traveltime tomography using particle swarm optimization: A near-surface field example

Geophysics ◽  
2012 ◽  
Vol 77 (1) ◽  
pp. R19-R32 ◽  
Author(s):  
Jens Tronicke ◽  
Hendrik Paasche ◽  
Urs Böniger
Keyword(s):  
Global Optimization ◽  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Velocity Fields ◽  
P Wave ◽  
Model Parameters ◽  
Optimization Approach ◽  
Swarm Optimization ◽  
Near Surface

Particle swarm optimization (PSO) is a relatively new global optimization approach inspired by the social behavior of bird flocking and fish schooling. Although this approach has proven to provide excellent convergence rates in different optimization problems, it has seldom been applied to inverse geophysical problems. Until today, published geophysical applications mainly focus on finding an optimum solution for simple, 1D inverse problems. We have applied PSO-based optimization strategies to reconstruct 2D P-wave velocity fields from crosshole traveltime data sets. Our inversion strategy also includes generating and analyzing a representative ensemble of acceptable models, which allows us to appraise uncertainty and nonuniqueness issues. The potential of our strategy was tested on field data collected at a well-constrained test site in Horstwalde, Germany. At this field site, the shallow subsurface mainly consists of sand- and gravel-dominated glaciofluvial sediments, which, as known from several boreholes and other geophysical experiments, exhibit some well-defined layering at the scale of our crosshole seismic data. Thus, we have implemented a flexible, layer-based model parameterization, which, compared with standard cell-based parameterizations, allows for significantly reducing the number of unknown model parameters and for efficiently implementing a priori model constraints. Comparing the 2D velocity fields resulting from our PSO strategy to independent borehole and direct-push data illustrated the benefits of choosing an efficient global optimization approach. These include a straightforward and understandable appraisal of nonuniqueness issues as well as the possibility of an improved and also more objective interpretation.

AN ACCELERATED PARTICLE SWARM OPTIMIZED BACK PROPAGATION ALGORITHM

2015 ◽  
Vol 77 (28) ◽  
Author(s):  
Nazri Mohd. Nawi ◽  
Abdullah Khan ◽  
M. Z. Rehman
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Back Propagation ◽  
Particle Swarm ◽  
Back Propagation Neural Network ◽  
Bp Algorithm ◽  
Back Propagation Algorithm ◽  
Swarm Optimization ◽  
Weight Problem ◽  
Accelerated Particle Swarm Optimization

Recently, accelerated particle swarm optimization (APSO) derived from particle swarm optimization (PSO) algorithm’s principle is becoming a very popular method in solving many hard optimization problems particularly the inherent weight problem in back propagation (BP). Therefore, this paper proposed an accelerated particle swarm optimized back propagation neural network (APSO-BP) algorithm in order to overcome the problems faced in BP algorithm. By using APSO to optimize the weights at each iterations of BP algorithm, the proposed APSO-BP is able to increase the convergence speed and avoids local minima. The simulation results demonstrates that the proposed algorithm outperforms the traditional BP method and achieves the objectives of this research, which contributes to artificial intelligence field.

A novel improved accelerated particle swarm optimization algorithm for global numerical optimization

2014 ◽  
Vol 31 (7) ◽  
pp. 1198-1220 ◽  
Author(s):  
Gai-Ge Wang ◽  
Amir Hossein Gandomi ◽  
Xin-She Yang ◽  
Amir Hossein Alavi
Keyword(s):  
Particle Swarm Optimization ◽  
Differential Evolution ◽  
Numerical Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Optimization Approach ◽  
Mutation Operator ◽  
Swarm Optimization ◽  
Content Type ◽  
Accelerated Particle Swarm Optimization

Purpose – Meta-heuristic algorithms are efficient in achieving the optimal solution for engineering problems. Hybridization of different algorithms may enhance the quality of the solutions and improve the efficiency of the algorithms. The purpose of this paper is to propose a novel, robust hybrid meta-heuristic optimization approach by adding differential evolution (DE) mutation operator to the accelerated particle swarm optimization (APSO) algorithm to solve numerical optimization problems. Design/methodology/approach – The improvement includes the addition of DE mutation operator to the APSO updating equations so as to speed up convergence. Findings – A new optimization method is proposed by introducing DE-type mutation into APSO, and the hybrid algorithm is called differential evolution accelerated particle swarm optimization (DPSO). The difference between DPSO and APSO is that the mutation operator is employed to fine-tune the newly generated solution for each particle, rather than random walks used in APSO. Originality/value – A novel hybrid method is proposed and used to optimize 51 functions. It is compared with other methods to show its effectiveness. The effect of the DPSO parameters on convergence and performance is also studied and analyzed by detailed parameter sensitivity studies.

scholarly journals Parameter Estimation in Rainfall-Runoff Modelling Using Distributed Versions of Particle Swarm Optimization Algorithm

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Michala Jakubcová ◽  
Petr Máca ◽  
Pavel Pech
Keyword(s):  
Particle Swarm Optimization ◽  
Inverse Problems ◽  
Hydrological Model ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Model Parameters ◽  
Estimation Of Parameters ◽  
Swarm Optimization

The presented paper provides the analysis of selected versions of the particle swarm optimization (PSO) algorithm. The tested versions of the PSO were combined with the shuffling mechanism, which splits the model population into complexes and performs distributed PSO optimization. One of them is a new proposed PSO modification, APartW, which enhances the global exploration and local exploitation in the parametric space during the optimization process through the new updating mechanism applied on the PSO inertia weight. The performances of four selected PSO methods were tested on 11 benchmark optimization problems, which were prepared for the special session on single-objective real-parameter optimization CEC 2005. The results confirm that the tested new APartW PSO variant is comparable with other existing distributed PSO versions, AdaptW and LinTimeVarW. The distributed PSO versions were developed for finding the solution of inverse problems related to the estimation of parameters of hydrological model Bilan. The results of the case study, made on the selected set of 30 catchments obtained from MOPEX database, show that tested distributed PSO versions provide suitable estimates of Bilan model parameters and thus can be used for solving related inverse problems during the calibration process of studied water balance hydrological model.

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