Study on Chaotic Particle Swarm Optimization Algorithm in Solution of Logistics Scheduling Problem

An improved chaos particle swarm optimization (CPSO) algorithm is proposed on path planning for unmanned aerial vehicle (UAV) to overcome the inadequacy of particle swarm optimization (PSO) algorithm, which falls into local optimum easily and converges slowly in process with poor precision. Through the in-depth analysis of PSO algorithm, the chaos optimization (CO) algorithm principle is introduced into it based on the traditional update operations on the particles’ velocity and position; as a result, the diversity of particles is increased, the suboptimal search on path planning is avoided and the quickness accompanied with accuracy of convergence is improved. Combined with digital map for modeling the UAV’s flight environment, the 3-D path planning is achieved. As the simulation results demonstrated, this hybrid algorithm is superior to the traditional PSO algorithm on path searching, especially in the 3-D environment.

In order to improve the performance of Support Vector Machine (SVM) classifier for imbalanced data, this paper proposes dynamic cost-sensitive SVM classifier based on chaos particle swarm optimization (CPDC_SVM). Firstly, this paper introduces dynamic cost-sensitive thought to SVM classifier, and gives the method for structuring dynamic cost and cost-sensitive SVM model. Secondly, we propose the evaluation methodology performance for classifier, and adopts decimal base to code the particles. At last, chaos thought is introduced in particle swarm optimization algorithm, and the Algorithm of the dynamic cost-sensitive SVM classifier is given, which improves convergent speed and accuracy of particle swarm optimization, and can optimize dynamic cost-sensitive SVM well, so CPDC_SVM adds effectively the convergence speed and accuracy for the particle swarm optimization algorithm. Experimental results show CPDC_SVM has higher precision than traditional SVM classifier, and dynamic cost and chaos particle swarm optimization can improve the performance for classifier.

Prediction of gas solubility in polymers by back propagation artificial neural network based on self-adaptive particle swarm optimization algorithm and chaos theory

Fault diagnosis of sensor by chaos particle swarm optimization algorithm and support vector machine

In this paper, a novel chaos particle swarm optimization(PSO) clustering algorithm for texture image segmentation was proposed. By means of stochastic property and ergodicity of chaos search mechanism, chaos PSO algorithm can effectively avoid getting into the local convergence. The proposed method combined chaos PSO algorithm with nearest neighbor clustering theorem, which can enhance the efficiency and precision of clustering. And the proposed chaos PSO clustering algorithm was applied to texture image segmentation with gray level co-occurrence matrix. The experimental results indicate that the proposed method can get better segmentation results than PSO algorithm and effectively improve the segmentation precision of texture image.Keywordsparticle swarm optimizationchaostexture segmentationgray level co-occurrence matrix (GLCM)

An iterative convergence control method (ICCM) based on the support vector regression (SVR) is proposed to realize the position–posture control of the planar four-link underactuated manipulator with a passive second link. Firstly, the particle swarm optimization (PSO) algorithm is used to obtain the target angles of all links according to the position–posture control objective. Then, two prediction models for the coupling relationship between the first link and the passive link, and the third link and the passive link are established based on the SVR, whose optimal parameters are selected by the chaos particle swarm optimization (CPSO) algorithm. By repeatedly controlling the first link or the third link to rotate an angle which is calculated by the trained SVR model, the passive link gradually converges to its target angle after several iterations. Next, the active links are controlled to rotate to their target angles with low speeds, and the passive link does not rotate due to friction. Finally, the experimental results verify the effectiveness and feasibility of the proposed method.

The Steer-by-Wire (SbW) system is an electronically controlled steering system that is able to improve steering capability without mechanical links between the steering wheel and the front wheels. However, failure of the SbW system actuator may lead to steering performance degradation and result in instability. In this paper, a fault tolerant sliding mode predictive control (SMPC) strategy for an SbW system is proposed. The sliding mode control is applied to improve the robustness of the model predictive control (MPC) in the presence of modeling uncertainties and disturbances, while the MPC is applied to enhance the fault tolerant capability of the steering control processes. The chaos particle swarm optimization (CPSO) algorithm is introduced to optimize the MPC and a two-stage Kalman filter is introduced to simultaneously provide fault information and state estimation. The performance of the proposed approach is validated through computer simulation. The results demonstrate that the proposed SMPC-CPSO controller is more robust and provides better tracking performance in the presence of model uncertainties, disturbance, and actuator faults than SCMP-PSOs (heterogeneous comprehensive learning particle swarm optimization, evolutionary particle swarm optimizer, etc), SMPC-differential evolution, MPC, SMPC, and MPC-PSO.

Clamping force control system is essential for clamping tasks that require high precision. In this paper, Active Disturbance Rejection Controller (ADRC) is applied for clamping force control system, aiming to achieve higher control precision. Furthermore, the CPSO-ADRC system is proposed and implemented by optimizing the critical parameters of ordinary ADRC using chaos particle swarm optimization (CPSO) algorithm. To verify the effectiveness of CPSO-ADRC, Particle Swarm Optimization- (PSO-) ADRC is introduced as a comparison. The simulation results show that the CPSO-ADRC can effectively improve the control quality with faster dynamic response and better command tracking performance compared to ordinary ADRC and PSO-ADRC.

Many evolutionary computation (EC) methods have been used to solve feature selection problems and they perform well on most small-scale feature selection problems. However, as the dimensionality of feature selection problems increases, the solution space increases exponentially. Meanwhile, there are more irrelevant features than relevant features in datasets, which leads to many local optima in the huge solution space. Therefore, the existing EC methods still suffer from the problem of stagnation in local optima on large-scale feature selection problems. Furthermore, large-scale feature selection problems with different datasets may have different properties. Thus, it may be of low performance to solve different large-scale feature selection problems with an existing EC method that has only one candidate solution generation strategy (CSGS). In addition, it is time-consuming to find a suitable EC method and corresponding suitable parameter values for a given large-scale feature selection problem if we want to solve it effectively and efficiently. In this article, we propose a self-adaptive particle swarm optimization (SaPSO) algorithm for feature selection, particularly for large-scale feature selection. First, an encoding scheme for the feature selection problem is employed in the SaPSO. Second, three important issues related to self-adaptive algorithms are investigated. After that, the SaPSO algorithm with a typical self-adaptive mechanism is proposed. The experimental results on 12 datasets show that the solution size obtained by the SaPSO algorithm is smaller than its EC counterparts on all datasets. The SaPSO algorithm performs better than its non-EC and EC counterparts in terms of classification accuracy not only on most training sets but also on most test sets. Furthermore, as the dimensionality of the feature selection problem increases, the advantages of SaPSO become more prominent. This highlights that the SaPSO algorithm is suitable for solving feature selection problems, particularly large-scale feature selection problems.

Particle swarm optimization (PSO) algorithm has shown promising performances on various benchmark functions and engineering optimization problems. However, it is still difficult to achieve a satisfying trade-off between exploration and exploitation for all the optimization problems and different evolving stages. Furthermore, control parameters of some related mechanisms need pre-experience by the requirement of trial-and-error scheme. This paper presents a novel PSO algorithm, which adaptively adopts various search strategies, called Self-adapting Hybrid Strategy PSO (SaHSPS). Unlike some other peer PSO variants, this method dynamically changes the probabilities of different strategies according to their previous successful searching memories, without any additional control parameters. The probabilities of different strategies would be re-initialized according to a proposed dynamic probabilistic model to diverse the population. Besides, particles are updated by probabilistically selected strategies after niching PSO with Ring topology. Moreover, a dynamic updating mechanism by niching PSO is proposed to guarantee the parallel searching capability during the whole evolution process. Thus, this proposed algorithm might be problem-independent and search-stage-independent, yielding more satisfying solutions on various optimization problems. A comprehensive experimental study is conducted on 28 benchmark functions of CEC 2013 special session on real-parameter optimization, including shifted, rotated, multi-modal, high conditioned, expanded and composition problems, compared with several state-of-the-art variants of PSO and differential evolution (DE) algorithms. Comparison results show that SaHSPS obtains outstanding performances on the majority of the test problems. Moreover, a practical engineering problem, real power loss minimization of IEEE 30-bus power system, is used to further evaluate SaHSPS. The numerical results, compared with other stochastic search algorithms, show that SaHSPS could find high-quality solutions with higher probability.

Collaborative beamforming in wireless sensor networks using a novel particle swarm optimization algorithm variant

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

The performance of the Particle Swarm Optimization (PSO) algorithm can be greatly improved if the parameters are appropriately tuned. However, tuning the control parameters for PSO algorithms has traditionally been a time-consuming, empirical process. Furthermore, ideal parameters may be time-dependent. To address the issue of parameter tuning, self-adaptive PSO (SAPSO) algorithms adapt the PSO control parameters over time. While many such SAPSO techniques have been proposed, their behaviour is not well understood as no in-depth critical analysis of their adaptation mechanisms has been performed. This study examines the convergence behaviour of eight SAPSO algorithms both analytically and empirically. Evidence clearly indicates that the field of self-adaptive PSO algorithms is in a sad state, given that many techniques either demonstrate divergent behaviour coupled with excessive invalid particles, and thus infeasible solutions, or have prohibitively low particle step sizes caused by rapid convergence.

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.

The particle swarm optimization (PSO) algorithm presents a new way for finding optimal solutions of complex optimization problems. In this paper a modified particle swarm optimization algorithm is presented. We modify the PSO algorithm in some aspects. Firstly, a contractive factor is introduced to the position update equation, and the particles are limited in search region. A new strategy for updating velocity is then adopted, in which the velocity is weakened linearly. Thirdly, using an idea of intersecting two modified PSO algorithms. Finally, adding an item of integral control in the modified algorithm can improve its global search ability. Based on these strategies, we proposed a new PSO algorithm named crossed PSO algorithm. Simulation results show that the crossed PSO is superior to the original PSO algorithm and can get overall promising performance over a wide range of problems.