Empirically characteristic analysis of chaotic PID controlling particle swarm optimization.

Determination of voltage stability boundary values in electrical power systems by using the Chaotic Particle Swarm Optimization algorithm

Short-term cascaded hydroelectric system scheduling based on chaotic particle swarm optimization using improved logistic map

An improved Chaotic Particle Swarm Optimization (CPSO) algorithm for a jobshop scheduling problem, with minimization of makespan as the criterion, is proposed in this research. A real-valued encoding scheme based on a matrix representation is developed, which converts the continuous position value of particles in PSO to the processing order of job operation. A compound chaotic search strategy that integrates both Tent and Logistic chaotic search process is employed to the global best particle to enhance the local searching ability of PSO. In addition, a gaussian disturbance technology is embedded in the CPSO algorithm to improve the diversity of the particles in the swarm. The performance of CPSO is compared with the standard PSO algorithm on a benchmark instance of jobshop scheduling problems. The results show that the proposed CPSO algorithm has a superior performance to the PSO algorithm.

Introduction. In an electrical power system, the output of the synchronous generators varies due to disturbances or sudden load changes. These variations in output severely affect power system stability and power quality. The synchronous generator is equipped with an automatic voltage regulator to maintain its terminal voltage at rated voltage. Several control techniques utilized to improve the response of the automatic voltage regulator system, however, proportional integral derivative (PID) controller is the most frequently used controller but its parameters require optimization. Novelty. In this paper, the chaotic sequence based on the logistic map is hybridized with particle swarm optimization to find the optimal parameters of the PID for the automatic voltage regulator system. The logistic map chaotic sequence-based initialization and global best selection enable the algorithm to escape from local minima stagnation and improve its convergence rate resulting in best optimal parameters. Purpose. The main objective of the proposed approach is to improve the transient response of the automatic voltage regulator system by minimizing the maximum overshoot, settling time, rise time, and peak time values of the terminal voltage, and eliminating the steady-state error. Methods. In the process of parameter tuning, the Chaotic particle swarm optimization technique was run several times through the proposed hybrid objective function, which accommodates the advantages of the two most commonly used objective functions with a minimum number of iterations, and an optimal PID gain value was found. The proposed algorithm is compared with current metaheuristic algorithms including conventional particle swarm optimization, improved kidney algorithm, and others. Results. For performance evaluation, the characteristics of the integral of time multiplied squared error and Zwe-Lee Gaing objective functions are combined. Furthermore, the time-domain analysis, frequency-domain analysis, and robustness analysis are carried out to show the better performance of the proposed algorithm. The result shows that automatic voltage regulator tuned with the chaotic particle swarm optimization based PID yield improvement in overshoot, settling time, and function value of 14.41 %, 37.91 %, 1.73 % over recently proposed IKA, and 43.55 %, 44.5 %, 16.67 % over conventional particle swarm optimization algorithms. The improvement in transient response further improves the automatic voltage regulator system stability for electrical power systems.

This paper experimentally investigates the effect of nine chaotic maps on the performance of two Particle Swarm Optimization (PSO) variants, namely, Random Inertia Weight PSO (RIW‐PSO) and Linear Decreasing Inertia Weight PSO (LDIW‐PSO) algorithms. The applications of logistic chaotic map by researchers to these variants have led to Chaotic Random Inertia Weight PSO (CRIW‐PSO) and Chaotic Linear Decreasing Inertia Weight PSO (CDIW‐PSO) with improved optimizing capability due to better global search mobility. However, there are many other chaotic maps in literature which could perhaps enhance the performances of RIW‐PSO and LDIW‐PSO more than logistic map. Some benchmark mathematical problems well‐studied in literature were used to verify the performances of RIW‐PSO and LDIW‐PSO variants using the nine chaotic maps in comparison with logistic chaotic map. Results show that the performances of these two variants were improved more by many of the chaotic maps than by logistic map in many of the test problems. The best performance, in terms of function evaluations, was obtained by the two variants using Intermittency chaotic map. Results in this paper provide a platform for informative decision making when selecting chaotic maps to be used in the inertia weight formula of LDIW‐PSO and RIW‐PSO.

In this paper, integrated preventive maintenance planning and production scheduling for a single machine is formulated as a multi-objective mathematical optimization problem. Despite the inter-dependent relationship between them, production scheduling and preventive maintenance planning decisions are generally analyzed and executed independently in real manufacturing systems. This practice is also found in the majority of the studies found in the relevant literature. The chaotic particle swarm optimization (CPSO) algorithm is proposed to solve the integrated optimization model for production scheduling and preventive maintenance planning. The numerical results on several problem sizes indicate that the methodology's feasibility and efficiency

Prediction of particulate matter at street level using artificial neural networks coupling with chaotic particle swarm optimization algorithm

Abstract- The goal of navigating a mobile robot is to find the optimal path to direct its movement, so path planning is the best solution to find the optimal path. Therefore, the two most important problems of path planning must be solved; the first is that the path must avoid collision with obstacles, and second it must reduce the length of the path to a minimum. This paper will discuss finding the shortest path with the optimum cost function by using the Chaotic Particle Swarm Optimization (CPSO), and A*, compare the results between them and the proposed hybrid algorithm that combines A* and Chaotic Particle Swarm Optimization (ACPSO) algorithms to enhance A* algorithm to find the optimal path and velocities of the wheeled mobile robot. These algorithms are simulated by MATLAB in a fixed obstacles environment to show the effectiveness of the proposed algorithm in terms of minimum number of an evaluation function and the shortest path length as well as to obtain the optimal or near optimal wheel velocities. Index Terms—Mobile robot, Path planning, Chaotic Particle Swarm Optimization (CPSO), A* algorithm, Fixed obstacles.

Improved particle swarm optimization combined with chaos

In this paper, a chaotic Particle Swarm Optimization (CPSO) algorithm is presented to solve the green agri-food supply chain network (GASCN). The GASCN design is critical to reduce the total transportation cost for efficient and effective supply chain management. The traditional supply chain does not adequately satisfy the expectance of all the customers, therefore new model of supply chain of great urgency to be exploited. The main contribution of this paper is to find an optimal solution for GASCN problem and propose a new solution based on CPSO to optimize the GASCN. To show the efficacy of the CPSO algorithm, the algorithm is tested on three cases. Results show better performance of the CPSO in GASCN by both optimization speed and solution quality as compared to GA and CGA, especially when the scale of problem is large.

A new hybrid model combining evolutionary artificial neural network (EANN) and mathematical models (MM) is proposed to improve the prediction precision of flow stress of 45 steel. In EANN, the optimal parameters are obtained by chaotic particle swarm optimization (CPSO) algorithm. CPSO adopts chaotic mapping and combines local search and global search, possessing high search efficiency and good performance. The results obtained from the computational study have shown that the proposed model can correctly recur to the flow stress in the sampled data and it can also predict well the non-sampled data. The efficiency and accuracy of the proposed model are demonstrated in comparison with the model combining BP networks with mathematical models (BPN-MM) used in much literature.

Considering that the global navigation satellite system (GNSS) has the influence of positioning and atmospheric signals from time to time in meteorology, errors caused by moisture, and so on in the effect of the propagation path, these factors have led to the influence of various indexes of meteorological factors. In this study, a meteorological prediction algorithm based on the CNSS and particle swarm optimization is proposed. Aiming at the phenomenon that the particle swarm optimization (PSO) algorithm is prone to slow convergence speed and low optimization accuracy and there is a local optimal but cannot achieve the global optimal, an adaptive Kent chaotic map PSO algorithm is proposed. Through the comprehensive analysis of the meteorological input indicators in the GNSS, a noncurrent weight evaluation system is proposed. Under different evaluation systems, the PSO algorithm is applied, and PCA weight can obtain the best prediction effect. Then, the GA model, PSO model, and ADPSO model are used to predict PM2.5 index in meteorology. The results show that the proposed ADPSO algorithm has a good performance in RMSE, MAE, and R2 model evaluation.

It is generally known that the balance between convergence and diversity is a key issue for solving multi-objective optimization problems. Thus, a chaotic multi-objective particle swarm optimization approach incorporating clone immunity (CICMOPSO) is proposed in this paper. First, points in a non-dominated solution set are mapped to a parallel-cell coordinate system. Then, the status of the particles is evaluated by the Pareto entropy and difference entropy. At the same time, the algorithm parameters are adjusted by feedback information. At the late stage of the algorithm, the local-search ability of the particle swarm still needs to be improved. Logistic mapping and the neighboring immune operator are used to maintain and change the external archive. Experimental test results show that the convergence and diversity of the algorithm are improved.

The traditional filter design based on Least Mean Square (LMS) algorithm which depends on step heavily, good performance cannot be achieved in some models. To solve this problem, mathematical model based on the searching for optimization coefficients of the filter is constructed, three improved algorithms based on the basic Particle Swarm Optimization (PSO) algorithm including Simulated Annealing PSO (SAPSO), Chaotic Local Search PSO (CLSPSO) and Chaotic Simulated Annealing PSO (CSAPSO) are applied to this problem. The simulation result shows that the CSAPSO algorithm can obtain optimization values efficiently and has better performance of convergence and effectiveness.

Aiming at the EEG signal radio frequency control problem of EEG signal radio control wheeled robot traversing multiple objective points, a new method of EEG signal radio frequency control that integrates the bi-objective chaotic particle swarm optimization (BCPSO) is put forward. This method transforms the selection of objective points into the EEG signal radio frequency control problem and makes optimization by using the ant colony algorithm. The EEG signal control function between the two objective points is defined, and the particle swarm optimization algorithm is applied for optimization. Given the premature phenomenon of particle swarm optimization algorithm, the inverse learning strategy is introduced into the particle swarm optimization algorithm, and the inertia weight and learning factor of the particle swarm optimization algorithm are improved. The performance test results show that the bi-objective chaotic particle swarm optimization algorithm can effectively prevent the premature particle phenomenon and improve the optimization capacity and stability of the particle swarm optimization algorithm.