Adaptive Chaotic Particle Swarm Optimization Research Articles

Parameter Identification and Dynamic Characteristic Research of a Fractional Viscoelastic Model for Sub-Frame Bushing

This research focused on the rubber bushings of the rear sub-frame in an electric vehicle. A dynamic model was developed to represent the bushing, incorporating an elastic element, a frictional element, and a viscoelastic element arranged in series using a fractional-order Maxwell and a Kelvin–Voigt model. To identify the parameters of the bushing model, an improved adaptive chaotic particle swarm optimization algorithm was employed, in conjunction with dynamic stiffness test data obtained at an amplitude of 0.2 mm. The test data obtained at different amplitudes (0.2 mm, 0.3 mm, 0.5 mm, and 1 mm) were fitted to the model, resulting in fitting errors of 1.13%, 4.07%, 4.42%, and 28.82%, respectively, when compared to the corresponding test data in order to enhance the accuracy of the model fitting; the Sobol sensitivity analysis method was utilized to analyze the parameter sensitivity of the model. Following the analysis, the parameters α, β, and k2, which exhibited high sensitivity, were re-identified. This re-identification process led to a reduction in the fitting error at the 1 mm amplitude to 7.45%. The improved accuracy of the model plays a crucial role in enhancing the simulation accuracy of design of experiments (DOE) analysis and verifying the vehicle’s performance under various conditions, taking into account the influence of the bushing.

Multi-index dominant grouping of rock mass discontinuities based on the combined weighting method: A case study for the Huayang tunnel

The properties of discontinuities are determined by their occurrence, trace length, aperture, roughness, and the degree of particle fill; therefore, a multi-index dominant grouping method for rock mass discontinuities based on the combined weighting method was proposed. Firstly, the combined weighting method was used to calculate the weighted value of each index participating in the similarity measurement, and a reasonable elimination of outlier discontinuities is achieved based on the principle of possibility clustering. The adaptive chaotic annealing particle swarm optimization algorithm was then used to search the global optimal initial clustering centers of the fuzzy C-means clustering algorithm. The objective function was established by minimizing the weighted sum of the distance between the discontinuities to be grouped, and the clustering centers to complete the multi-index dominant grouping of rock mass discontinuities. 230 artificial discontinuities with outliers were grouped and compared with other methods, which indicated that this method has higher grouping accuracy. The proposed method was applied to group the measured discontinuities of the Huayang tunnel in Chongqing City, China. Based on the grouping results, a discrete element model for tunnel excavation was established, and the construction was guided according to the calculated results from the model. The engineering applicability of the method was further verified.

WACPN: A Neural Network for Pneumonia Diagnosis.

Community-acquired pneumonia (CAP) is considered a sort of pneumonia developed outside hospitals and clinics. To diagnose community-acquired pneumonia (CAP) more efficiently, we proposed a novel neural network model. We introduce the 2-dimensional wavelet entropy (2d-WE) layer and an adaptive chaotic particle swarm optimization (ACP) algorithm to train the feed-forward neural network. The ACP uses adaptive inertia weight factor (AIWF) and Rossler attractor (RA) to improve the performance of standard particle swarm optimization. The final combined model is named WE-layer ACP-based network (WACPN), which attains a sensitivity of 91.87±1.37%, a specificity of 90.70±1.19%, a precision of 91.01±1.12%, an accuracy of 91.29±1.09%, F1 score of 91.43±1.09%, an MCC of 82.59±2.19%, and an FMI of 91.44±1.09%. The AUC of this WACPN model is 0.9577. We find that the maximum deposition level chosen as four can obtain the best result. Experiments demonstrate the effectiveness of both AIWF and RA. Finally, this proposed WACPN is efficient in diagnosing CAP and superior to six state-of-the-art models. Our model will be distributed to the cloud computing environment.

Empirical Comparative Study of Wearable Service Trust Based on User Clustering

Users of wearable services are different in age, occupation, income, education, personality, values and lifestyle, which also determine their different consumption patterns. Therefore, for the trust of wearable services, the influencing factors or strength may not be the same for different users. This article starts with the resource and motivation dimensions of VALSTM model, and the clustering model and questionnaire scale for consumers of wearable services were constructed. And then the users and potential users of wearable service are clustered by an improved clustering algorithm based on adaptive chaotic particle swarm optimization. Through clustering analysis of 535 valid questionnaires, users are grouped into three types of consumers with different lifestyles, respectively named: trend-following users, fashion-leading users and economic-rational users. Finally, this paper analyzes and compares the trust subgroup models of three clusters, and draws some conclusions.

Power supply system scheduling and clean energy application based on adaptive chaotic particle swarm optimization

The research fields in the fields of information science and computer are very extensive, and there are many research hotspots. Intelligent optimization algorithm is one of the research hotspots. One of the key issues found in research papers in this field is how to effectively improve the search quality of intelligent optimization algorithms in a complex work environment. As a multi-functional swarm algorithm, particle swarm optimization has relatively few parameters, good convergence, and relatively fast working speed. It is a popular intelligent algorithm. In the process of studying the optimal operation of the combined cooling, heating and power system, in order to better dispatch the output of each device in the combined cooling, heating and power system, a combination of chaotic search based on Tent mapping and nonlinear adaptive particle swarm optimization is proposed Optimization algorithm. Taking the operating cost, pollutant emission and energy efficiency of the cogeneration system as the goals, a multi-objective optimization model was established. The simulation results show that the proposed cooling, heating and power cogeneration system optimization method can effectively improve economic benefits, reduce pollution emissions, and improve energy utilization. The traditional model cannot accurately analyze the environmental pollution mitigation effect. In order to avoid this problem, a modeling analysis of the impact of wind power photovoltaic clean energy on the mitigation of environmental pollution is proposed, thereby building a wind power photovoltaic clean energy organization structure and maintaining the application of wind power photovoltaic clean energy the stability of the range. Set wind power photovoltaic clean energy to alleviate environmental pollution factors, and determine the degree of environmental pollution limited by natural numbers under continuous use of polynomial constraints.

Empirical Comparative Study of Wearable Service Trust Based on User Clustering

Users of wearable services are different in age, occupation, income, education, personality, values and lifestyle, which also determine their different consumption patterns. Therefore, for the trust of wearable services, the influencing factors or strength may not be the same for different users. This article starts with the resource and motivation dimensions of VALSTM model, and the clustering model and questionnaire scale for consumers of wearable services were constructed. And then the users and potential users of wearable service are clustered by an improved clustering algorithm based on adaptive chaotic particle swarm optimization. Through clustering analysis of 535 valid questionnaires, users are grouped into three types of consumers with different lifestyles, respectively named: trend-following users, fashion-leading users and economic-rational users. Finally, this paper analyzes and compares the trust subgroup models of three clusters, and draws some conclusions.

An iterative updating heuristic search strategy for groundwater contamination source identification based on an ACPSO–ELM surrogate system

The simulation-random statistics (SRS) method is one of the effective approaches to solving groundwater contamination source identification (GCSI) challenges. In this paper, we applied a heuristic search iterative process (HSIP) based on SRS to provide interval estimation and probability distribution for an unknown variable to enhance our understanding of the unknown variable. Posterior distribution has been a subject of interest in HSIP; therefore, it is imperative to update prior distributions using final iterations obtained from posterior distributions, which has been seldom considered in previous research. This paper first proposed such updates and subsequently designed an updating HSIP (UHSIP). This paper first proposed a feedback correction step and incorporated it into UHSIP to achieve feedback improvements and simultaneous updates of an unknown variable and prior distribution. In UHSIP, a surrogate system such as extreme learning machine (ELM) is usually established for a numerical simulation model to reduce computational cost. The particle swarm optimization (PSO) algorithm has been demonstrated to optimize input weights and hidden layer biases in ELM. However, the PSO algorithm is prone to premature convergence and its calculation accuracy does not satisfy precision requirements when optimizing complex functions. To enhance accuracy, this paper first proposed a new adaptive chaotic PSO (ACPSO) algorithm to establish ACPSO–ELM surrogate system. The two newly proposed approaches were tested in two case studies based on groundwater inorganic and organic contaminations. Results reveal that UHSIP can effectively enhance identification accuracy, and ACPSO–ELM surrogate system has a higher approximation accuracy to the simulation model than PSO–ELM. Overall, the two main innovations in this paper (UHSIP and ACPSO–ELM surrogate system) present promising and effective solutions to GCSI challenge.

Thermal error modeling of CNC milling machine tool spindle system in load machining: based on optimal specific cutting energy

The aim of this study is to reduce the influence of machine tools spindle thermal error on machining accuracy in load machining state. The temperature and thermal error measuring system and experiments of the 3-axis vertical milling machine tool spindle in idling and load machining state were established and carried out. The differences in temperature and thermal error between the idling and load machining states were analyzed. Upon combining the fuzzy clustering and gray correlation algorithm, the temperature-sensitive points were optimized. The thermal error prediction models of machine tool spindle system in load machining state with the optimal specific cutting energy were established based on the adaptive chaotic particle swarm optimization algorithm, and the model prediction effects were evaluated. The results showed that the spindle system temperature and thermal error in load machining were higher than the idle state. Two temperature-sensitive points were selected that not only reduced the redundancy of temperature measuring points but also ensured the model prediction accuracy. The thermal error models prediction accuracy was above 90%, and the root mean square error and residual error were better than PSO and regression. The experimental results showed that the thermal error prediction models have a high prediction accuracy and engineering application value.

An adaptive enhancement method for low illumination color images

In order to effectively improve the visual effect and image quality of color images under low illumination conditions, we propose an image enhancement method based on HSV and CIEL*a*b* color spaces for adaptively enhancing color image under low illumination conditions. The proposed method takes into account the characteristics of low illumination color images, and has the strategies of contrast, brightness enhancement, and color saturation correction. We utilize our proposed adaptive chaotic particle swarm optimization algorithm in this paper combined with gamma correction to improve the overall brightness of the image, and generate the best brightness adjustment effect in the proposed algorithm. In addition, our improved adaptive stretching function is used to enhance the image saturation. The experimental results show that compared with other traditional and latest color image enhancement algorithms, the proposed algorithm significantly enhances the visual effect of the low illumination color images. It can not only improve the contrast of low illumination color images and avoid color distortion, but also effectively improve the brightness of the image and provide more detail enhancement while maintaining the naturalness of the image.

Cubic Spline Interpolation-Based Robot Path Planning Using a Chaotic Adaptive Particle Swarm Optimization Algorithm

This paper proposed a cubic spline interpolation-based path planning method to maintain the smoothness of moving the robot’s path. Several path nodes were selected as control points for cubic spline interpolation. A full path was formed by interpolating on the path of the starting point, control points, and target point. In this paper, a novel chaotic adaptive particle swarm optimization (CAPSO) algorithm has been proposed to optimize the control points in cubic spline interpolation. In order to improve the global search ability of the algorithm, the position updating equation of the particle swarm optimization (PSO) is modified by the beetle foraging strategy. Then, the trigonometric function is adopted for the adaptive adjustment of the control parameters for CAPSO to weigh global and local search capabilities. At the beginning of the algorithm, particles can explore better regions in the global scope with a larger speed step to improve the searchability of the algorithm. At the later stage of the search, particles do fine search around the extremum points to accelerate the convergence speed of the algorithm. The chaotic map is also used to replace the random parameter of the PSO to improve the diversity of particle swarm and maintain the original random characteristics. Since all chaotic maps are different, the performance of six benchmark functions was tested to choose the most suitable one. The CAPSO algorithm was tested for different number of control points and various obstacles. The simulation results verified the effectiveness of the proposed algorithm compared with other algorithms. And experiments proved the feasibility of the proposed model in different dynamic environments.

Study on the Evaluation Method of Harmonic Loss Based on Data Analysis in Distribution Network

Because of the lack of unified and scientific method for the evaluation of harmonic loss in distribution network, based on the big data analysis method of the power, this paper established the harmonic loss assessment system of distribution network including data, index and application, constructed the evaluation index system of harmonic loss. Combined with radial basis (RBF) neural network, improved conventional particle swarm optimization (PSO) algorithm, and made the harmonic loss assessment system with adaptive chaotic particle swarm optimization RBF (CAPSO-RBF) neural network. Refer to the harmonic core temperature rise model of electric cable, got the insulation aging life evaluation model of electric cable under the influence of harmonic. The case analysis proved the feasibility of the method and verifies the reasonableness of the designed evaluation system.

An optimized IS-APCPSO algorithm for large scale complex traffic network

Chaotic particle swarm optimization algorithm is improved by incorporating antibody concentration, adaptive propagation, optimization mechanism of the multi-population evolution strategy, elite particles chaotic traversal mechanism and constraint processing mechanism. In this paper, an improved adaptive propagation chaotic particle swarm optimization algorithm based on immune selection (IS-APCPSO algorithm for short) is proposed. The performance of several algorithms has been compared by multimodal function, functions with high dimensional and complex constraints, bi-level programming function and a classic example of traffic network optimization. The experimental results prove that the proposed algorithm in accelerating convergence rate, increasing the diversity of particles, and preventing premature phenomenon is effective. The novel algorithm is expected to be used in the model solution of large-scale complex traffic network optimization problem.

Probabilistic interactive fuzzy satisfying generation and transmission expansion planning using fuzzy adaptive chaotic binary PSO algorithm

In this paper, an interactive fuzzy satisfying method based on fuzzy adaptive chaotic binary particle swarm optimization (FACBPSO) algorithm is proposed to investigate the multi-objective Generation and Transmission Expansion Planning (G-TEP). The objective functions of the G-TEP problem, which are modeled by fuzzy sets, present the total invest- ment/operation cost and the total pollutant emission. In modern power systems, the necessity of considering the wind/solar energy resources in Generation Expansion Planning (GEP) studies is important. In addition, HVDC links, transmitting renewable resource power from remote sites, should be considered in TEP problem. The use of the wind/solar energy due to the uncertainty of their generation and the integration of HVDC links would consequently impose more complexity to solution of the G-TEP problem. In this paper, the proposed algorithm is tested on an IEEE test system based on economic and environmental considerations to generate an optimal expansion plan.

Aerodynamic Parameter Estimation of a Symmetric Projectile Using Adaptive Chaotic Mutation Particle Swarm Optimization

This article details a new optimizing algorithm called Adaptive Chaotic Mutation Particle Swarm Optimization (ACM-PSO). The new algorithm is used to perform aerodynamic parameter estimation on a spinning symmetric projectile. The main creative ideas of this new algorithm are as follows. First, a self-adaptive weight function is used so that the inertial weight can be adjusted dynamically by itself. Second, the initialized particle is generated by chaos theory. Last, a method that can be used to judge whether the algorithm has fallen into a local optimum is established. The common testing function is used to test the new algorithm, and the result shows that, compared with the basic particle swarm optimization (PSO) algorithm, it is more likely to have a quick convergence and high accuracy and precision, leading to extensive application. Simulated ballistic data are used as testing data, and the data are subjected to the new algorithm to identify the aerodynamic parameters of a spinning symmetric projectile. The result shows that the algorithm proposed in this paper can effectively identify the aerodynamic parameters with high precision and a quick convergence velocity and is therefore suitable for use in actual engineering.

A multi-objective bi-level location planning problem for stone industrial parks

This paper focuses on a stone industrial park location problem with a hierarchical structure consisting of a local government and several stone enterprises under a random environment. In contrast to previous studies, conflicts between the local authority and the stone enterprises are considered. The local government, being the leader in the hierarchy, aims to minimize both total pollution emissions and total development and operating costs. The stone enterprises, as the followers in the hierarchy, only aim to minimize total costs. In addition, unit production cost and unit transportation cost are considered random variables. This complicated multi-objective bi-level optimization problem poses several challenges, including randomness, two-level decision making, conflicting objectives, and difficulty in searching for the optimal solutions. Various approaches are employed to tackle these challenges. In order to make the model trackable, expected value operator is used to deal with the random variables in the objective functions and a chance constraint-checking method is employed to deal with such variables in the constraints. The problem is solved using a bi-level interactive method based on a satisfactory solution and Adaptive Chaotic Particle Swarm Optimization (ACPSO). Finally, a case study is conducted to demonstrate the practicality and efficiency of the proposed model and solution algorithm. The performance of the proposed bi-level model and ACPSO algorithm was highlighted by comparing to a single-level model and basic PSO and GA algorithms.

WSN Coverage Enhancement Algorithm Based on Particle Swarm Optimization

For the optimization of WSN coverage, this paper proposes a coverage optimization approach: Adaptive Disturbance Chaotic Particle Swarm Optimization, referred as ADCPSO. Based on the effective coverage of the network as the optimization goal, the method first conducts adaptive operation on the particles. Introducing perturbations factor and making particles trapped into local optimum quickly jump out, this method then use the randomness and the ergodicity of chaotic motion, to make local fine search. This effectively avoids particles’ “being premature” and improves the accuracy of the algorithm. The simulation results show that the ADCPSO algorithm can get better coverage.

Multi-Objective Reactive Power Optimization Based on Refined Chaos Particle Swarm Optimization Algorithm

In order to reduce the active network loss, increase the power quality and voltage static stability of power system, an index function of multi-objective reactive power optimization is established. Then, an improved adaptive chaotic particle swarm optimization algorithm is proposed to solve the problem. Through the using of cubic chaotic mapping, the particle population is initialized to enhance the diversity of its value; In the optimization process, poor fitness particles are updated with chaos disturbance, and their inertia weight are adjusted dynamically with particles fitness value so as to avoid local convergence. Simulation of IEEE 30 bus system shows that the proposed algorithm for reactive power optimization can avoid premature convergence effectively, and converge to optimal solution rapidly.

Real-time Path Planning Strategy for UAV Based on Improved Particle Swarm Optimization

Unmanned Aerial Vehicle (UAV) path planning is divided into off-line static path planning and real-time dynamic path planning. The former one is applied to the ideal situation that the terrain has been clear, and there is no unexpected situation in flight. Actually, however, the flight situation is very complex, we have to adopt real-time path planning based on off-line static path planning. To meet the demand of real-time dynamic path planning, this paper proposes a dynamic path planning strategy of adaptive chaotic particle swarm optimization (PSO) algorithm, which owns both good global and local search ability. The simulation shows that the path planning strategy this paper proposes, basically, meets the needs of real-time path planning. Moreover, it has better performance than other algorithm.

UCAV Path Planning by Fitness-Scaling Adaptive Chaotic Particle Swarm Optimization

Path planning plays an extremely important role in the design of UCAVs to accomplish the air combat task fleetly and reliably. The planned path should ensure that UCAVs reach the destination along the optimal path with minimum probability of being found and minimal consumed fuel. Traditional methods tend to find local best solutions due to the large search space. In this paper, a Fitness-scaling Adaptive Chaotic Particle Swarm Optimization (FAC-PSO) approach was proposed as a fast and robust approach for the task of path planning of UCAVs. The FAC-PSO employed the fitness-scaling method, the adaptive parameter mechanism, and the chaotic theory. Experiments show that the FAC-PSO is more robust and costs less time than elite genetic algorithm with migration, simulated annealing, and chaotic artificial bee colony. Moreover, the FAC-PSO performs well on the application of dynamic path planning when the threats cruise randomly and on the application of 3D path planning.

Improved Oustaloup approximation of fractional-order operators using adaptive chaotic particle swarm optimization

A rational approximation method of the fractional-order derivative and integral operators is proposed. The turning frequency points are fixed in each frequency interval in the standard Oustaloup approximation. In the improved Oustaloup method, the turning frequency points are determined by the adaptive chaotic particle swarm optimization (PSO). The average velocity is proposed to reduce the iterations of the PSO. The chaotic search scheme is combined to reduce the opportunity of the premature phenomenon. Two fitness functions are given to minimize the zero-pole and amplitude-phase frequency errors for the underlying optimization problems. Some numerical examples are compared to demonstrate the effectiveness and accuracy of this proposed rational approximation method.