An Investigation of Logarithm Decreasing Inertia Weight Particle Swarm Optimization in Global Optimization Problem

ABSTRACT This research investigates Logarithm Decreasing Inertia Weight (LogDIW) to improve the performance of Particle Swarm Optimization (PSO). The general problem of PSO algorithm is premature convergence when solving complex optimization problem. Some researchers try to solve the problem by modifying the PSO or proposing another PSO variants. Some PSO variants proved to have a better performance than the original PSO. The purpose of this research is to obtain some experimental facts to prove the efficiency of LogDIWPSO if the parameters are tuned correctly and to show that the LogDIWPSO performs better compared to the other PSO variants. In the early step of the experiment, a percentage value of search space boundary is obtained. This step is important to compute the velocity threshold of LogDIW based on the optimization problem. The next experiment is done to measure the performance of LogDIWPSO using six benchmark functions in optimization problems and to prove the superiority of LogDIWPSO compared to the other PSO variants. The experiment result shows that LogDIW achieves better performance than the other PSO variants

Controller design for synchronization of an array of delayed neural networks using a controllable

This is the post-print version of the Article - Copyright @ 2011 ElsevierIn this paper, a controllable probabilistic particle swarm optimization (CPPSO) algorithm is introduced based on Bernoulli stochastic variables and a competitive penalized method. The CPPSO algorithm is proposed to solve optimization problems and is then applied to design the memoryless feedback controller, which is used in the synchronization of an array of delayed neural networks (DNNs). The learning strategies occur in a random way governed by Bernoulli stochastic variables. The expectations of Bernoulli stochastic variables are automatically updated by the search environment. The proposed method not only keeps the diversity of the swarm, but also maintains the rapid convergence of the CPPSO algorithm according to the competitive penalized mechanism. In addition, the convergence rate is improved because the inertia weight of each particle is automatically computed according to the feedback of fitness value. The efficiency of the proposed CPPSO algorithm is demonstrated by comparing it with some well-known PSO algorithms on benchmark test functions with and without rotations. In the end, the proposed CPPSO algorithm is used to design the controller for the synchronization of an array of continuous-time delayed neural networks.This research was partially supported by the National Natural Science Foundation of PR China (Grant No 60874113), the Research Fund for the Doctoral Program of Higher Education (Grant No 200802550007), the Key Creative Project of Shanghai Education Community (Grant No 09ZZ66), the Key Foundation Project of Shanghai(Grant No 09JC1400700), the Engineering and Physical Sciences Research Council EPSRC of the U.K. under Grant No. GR/S27658/01, an International Joint Project sponsored by the Royal Society of the U.K., and the Alexander von Humboldt Foundation of Germany

Crow search algorithm with time varying flight length strategies for feature selection

Feature Selection (FS) is an efficient technique use to get rid of irrelevant, redundant and noisy attributes in high dimensional datasets while increasing the efficacy of machine learning classification. The CSA is a modest and efficient metaheuristic algorithm which has been used to overcome several FS issues. The flight length (fl) parameter in CSA governs crows\u27 search ability. In CSA, fl is set to a fixed value. As a result, the CSA is plagued by the problem of being hoodwinked in local minimum. This article suggests a remedy to this issue by bringing five new concepts of time dependent fl in CSA for feature selection methods including linearly decreasing flight length, sigmoid decreasing flight length, chaotic decreasing flight length, simulated annealing decreasing flight length, and logarithm decreasing flight length. The proposed approaches\u27 performance is assessed using 13 standard UCI datasets. The simulation result portrays that the suggested feature selection approaches overtake the original CSA, with the chaotic-CSA approach beating the original CSA and the other four proposed approaches for the FS task

On the Performance of Linear Decreasing Inertia Weight Particle Swarm Optimization for Global Optimization

Linear decreasing inertia weight (LDIW) strategy was introduced to improve on the performance of the original particle swarm optimization (PSO). However, linear decreasing inertia weight PSO (LDIW-PSO) algorithm is known to have the shortcoming of premature convergence in solving complex (multipeak) optimization problems due to lack of enough momentum for particles to do exploitation as the algorithm approaches its terminal point. Researchers have tried to address this shortcoming by modifying LDIW-PSO or proposing new PSO variants. Some of these variants have been claimed to outperform LDIW-PSO. The major goal of this paper is to experimentally establish the fact that LDIW-PSO is very much efficient if its parameters are properly set. First, an experiment was conducted to acquire a percentage value of the search space limits to compute the particle velocity limits in LDIW-PSO based on commonly used benchmark global optimization problems. Second, using the experimentally obtained values, five well-known benchmark optimization problems were used to show the outstanding performance of LDIW-PSO over some of its competitors which have in the past claimed superiority over it. Two other recent PSO variants with different inertia weight strategies were also compared with LDIW-PSO with the latter outperforming both in the simulation experiments conducted

Multilevel Thresholding Image Segmentation Based-Logarithm Decreasing Inertia Weight Particle Swarm Optimization

The image segmentatation technique that is often used is thresholding. Image segmentation is a process of dividing the image into different regions according to their similar characteristics. This research proposes a multilevel thresholding algorithm using modified particle swarm optimization to solve a segmentation problem. The threshold optimal values are determined by maximizing Otsu’s objective function using optimization technique namely particle swarm optimization based on the logarithmic decreasing inertia weight (LogDIWPSO). The proposed method reduces the computational time to find the optimum thresholds of multilevel thresholding which evaluated on several grayscale images. A detailed comparison analysis with other multilevel thresholding based techniques namely particle swarm optimization (PSO), iterative particle swarm optimization (IPSO), and genetic algorithms (GA), From the experiments, Modified particle swarm optimization (MoPSO) produces better performance compared to the other methods in terms of fitness value, robustness and convergence. Therefore, it can be concluded that MoPSO is a good approach in finding the optimal threshold value

Modified Particle Swarm Optimization Based PID for Movement Control of Two-Wheeled Balancing Robot

Two-wheeled balancing robot is a mobile robot that has helped various human’s jobs such as the transportations. To control stability is still be the challenges for researchers. Three equations are obtained by analyzing the dynamics of the robot with the Newton approach. To control three degrees of freedom (DOF) of the robot, PIDs is tuned automatically and optimized by multivariable Modified Particle Swarm Optimization (MPSO). Some parameters of the PSO process are modified to be a nonlinear function. The inertia weight and learning factor variable on PSO are modified to decreasing exponentially and increasing exponentially, respectively. The Integral Absolute Error (IAE) and Integral Square Error (ISE) evaluate the error values. The performances of MPSO and PSO classic are tested by several Benchmark functions. The results of the Benchmark Function show that Modified PSO proposed to produce less error and overshoot. Therefore, the MPSO purposed are implemented to the plant of balancing robot to control the angle, the position, and the heading of the robot. The result of the simulation built shows that the MPSO – PID can make the robot moves to the desired positions and maintain the stability of the angle of the robot. The input of distance and angle of the robot are coupling so MPSO needs six variable to optimize the PID parameters of balancing and distance control

Multilevel Thresholding Segmentasi Citra Warna Menggunakan Logarithmic Decreasing Inertia Weight Particle Swarm Optimization

Permasalahan utama dari segmentasi citra warna adalah tidak semua metode segmentasi citra yang ada saat ini dapat digunakan secara langsung seperti halnya pada citra gray scale. Maka dari itu diperlukan suatu teknik yang tepat untuk melakukan segmentasi warna. Teknik yang digunakan dalam penelitian ini adalah teknik segmentasi multilevel thresholding dengan menggunakan suatu bobot inersia logarithm decreasing particle swarm optimization (LogPSO). Bobot inersia Nilai threshold optimal diperoleh dengan cara memaksimalkan fungsi objektif Otsu. Teknik yang diusulkan mengurangi waktu perhitungan untuk perhitungan threshold optimum didasarkan pada multilevel thresholdingyang diujikan pada 8 citra warna standar. Suatu analisis perbandingan secara detail dengan bobot inersia lainnya yang didasarkan pada multilevel thresholding yakni constant particle swarm optimization (CPSO), menunjukkan hasil kinerja yang lebih baik pada metode yang diusulkan. Kinerja segmentasi citra warna dalam penelitian ini didasarkan pada peak signal to noise ratio (PSNR), mean, standar deviasi fitness, structural similarity index measure (SSIM), mean square of error (MSE) serta waktu perhitungannya. Algoritma LogPSO menunjukkan hasil yang lebih baik pada keseluruhan parameter tersebut kecuali pada waktu penghitungan. Algoritma LogPSO lebih lama waktu perhitungannya dibandingkan dengan CPSO

Studies in particle swarm optimization technique for global optimization.

Ph. D. University of KwaZulu-Natal, Durban 2013.Abstract available in the digital copy.Articles found within the main body of the thesis in the print version is found at the end of the thesis in the digital version

One more look on visualization of operation of a root-finding algorithm

Many algorithms that iteratively find solution of an equation require tuning. Due to the complex dependence of many algorithm’s elements, it is difficult to know their impact on the work of the algorithm. The article presents a simple root-finding algorithm with self-adaptation that requires tuning, similarly to evolutionary algorithms. Moreover, the use of various iteration processes instead of the standard Picard iteration is presented. In the algorithm’s analysis, visualizations of the dynamics were used. The conducted experiments and the discussion regarding their results allow to understand the influence of tuning on the proposed algorithm. The understanding of the tuning mechanisms can be helpful in using other evolutionary algorithms. Moreover, the presented visualizations show intriguing patterns of potential artistic applications