Basic Particle Swarm Optimization Algorithm Research Articles

An enhanced particle swarm optimization algorithm

<p>In this paper, an enhanced stochastic optimization algorithm based on the basic Particle Swarm Optimization (PSO) algorithm is proposed. The basic PSO algorithm is built on the activities of the social feeding of some animals. Its parameters may influence the solution considerably. Moreover, it has a couple of weaknesses, for example, convergence speed and premature convergence. As a way out of the shortcomings of the basic PSO, several enhanced methods for updating the velocity such as Exponential Decay Inertia Weight (EDIW) are proposed in this work to construct an Enhanced PSO (EPSO) algorithm. The suggested algorithm is numerically simulated established on five benchmark functions with regards to the basic PSO approaches. The performance of the EPSO algorithm is analyzed and discussed based on the test results.</p>

Simulation of Composite Energy Storage Optimization Configuration of Micro-grid Based on PSO

The micro-grid studied in this paper contains photovoltaic power generation, wind power generation and energy storage devices composed of super capacitors and storage batteries. Although this method of composite energy storage can effectively utilize new energy sources, the cost is relatively expensive. So, under the condition of normal load and overall normal operation, combined with power balance and other constraints, how to allocate capacity configuration can minimize the overall operating cost of the micro-grid, and improving the economics of micro-grid operation becomes a key issue. In this paper, for the micro-grid with island operation mode, using Matlab to build distributed power model and micro-grid model in micro-grid, aiming at the economical operation of micro-grid, using the basic particle swarm optimization algorithm to obtain the optimal solution, the grid is optimized for configuration and combined with an example for simulation analysis.

An iterative stochastic algorithm based on distributed learning automata for finding the stochastic shortest path in stochastic graphs

In this paper, we study the problem of finding the shortest path in stochastic graphs and propose an iterative algorithm for solving it. This algorithm is based on distributed learning automata (DLA), and its objective is to use a DLA for finding the shortest path from the given source node to the given destination node whose weight is minimal in expected sense. At each stage of this algorithm, DLA specifies edges needed to be sampled. We show that the given algorithm finds the shortest path with minimum expected weight in stochastic graphs with high probability which can be close to unity as much as possible. We compare the given algorithm with some distributed learning automata-based iterative algorithms, a particle swarm optimization-based algorithm, an ant colony-based algorithm, a Q-learning-based algorithm, and an actor–critic-based algorithm for finding the shortest path. Computer experiments show that the proposed algorithm requires fewer edge samples to find the shortest path than the previously introduced DLA-based algorithms.

Markov and improved particle swarm optimization-based privacy preservation algorithm for user space geographical location

ABSTRACT With the development of location-based service, the problem of personal privacy information security is becoming more and more serious. Users’ personal privacy information is used without authorization, personal privacy information security is facing various challenges, and personal property security is likely to be violated. Based on HMM model, this paper analyses the location and the location transition probability, and then predicts the location probability by AFMO algorithm. Privacy location information can be predicted by AFMO algorithm, which can improve the accuracy of location prediction. Finally, the experimental results show that the HMM-AFMO algorithm is superior to the HMM model in terms of execution time and effect. In the position prediction, the similarity advantage is obvious, which fully reflects the advantages of the algorithm proposed in this paper.

Research on time-of-use pricing method of shared bicycles

Shared bicycles have become an important mode of urban slow traffic, and the operation and management of shared bicycles have attracted more and more attention. In order to alleviate the shortage of supply during peak period and accelerate the turnover rate during flat peak period, this paper proposes a differentiated pricing strategy of shared bicycles. By establishing a bilevel programming model of shared bicycles fare and using a particle swarm optimization-based global pricing model solution algorithm, the purpose of minimizing the generalized travel cost of travelers and maximizing the economic benefits of shared bicycles enterprises are achieved. Based on the actual traffic situation in Beijing, the model parameters are calibrated to verify the validity of the model and the algorithm. The proposed model can provide a reference for the pricing strategy of shared bicycles and provide a scientific basis for the operation and management of shared bicycles enterprises.

Optimal Placement and Control of UPFC for Enhancement of Power System Performance Using Multi-objective θ-CPCE Algorithm

This paper addresses an optimal design for simultaneously locating unified power flow controller and tuning the parameters of its controllers to enhance the performance of the power system. A mixed integer nonlinear problem is acquired for the design procedure due to the characteristics of the considered objective functions. A new θ-hybrid particle swarm optimization-based co-evolutionary cultural algorithm called θ-culture-PSO-co-evolutionary (θ-CPCE) is used to solve this problem. The best Pareto-optimal solutions are also obtained by defining this problem as a multi-objective function. The simulations results on IEEE 39-bus power system show the effective performance of the proposed method as compared with other algorithms.

An Energy Efficient Scalable Clustering Protocol for Dynamic Wireless Sensor Networks

The issue of energy constraint has always been a challenging task in the research field of wireless sensor networks. Clustering is the most effective approach for enhancing the performance of wireless sensor networks to a great extent in terms of energy consumption, network lifetime and throughput. However, the uneven formation of clusters can lead to faster energy depletion of few nodes, and thus results in premature failure of the wireless sensor network. This paper proposes an energy-efficient scalable clustering protocol (EESCP) which considers inter-cluster and intra-cluster distances to generate balanced clusters. A novel Dragonfly algorithm based particle swarm optimization technique is proposed to optimize the selection of cluster heads. Further, extensive simulations have been carried out by varying node densities and network sizes to demonstrate the full potential of EESCP.

Improvement of Inertia Weight Declining Strategy Based on Particle Swarm Optimization Algorithm

The standard particle swarm optimization algorithm introduces the inertia weighetw, which becomes a calculation method for finding the extremum of the function effectively. It is easy to converge fast. Nowadays, the linear decreasing dynamic, inertia weighting strategy is widely used. Although this improvement is very successful. However, the search process is a nonlinear and complex process. In order to better maintain the balance between global and local search capabilities, this paper proposes a nonlinear decreasing dynamic inertia weighting strategy based on the linear decreasing strategy, using Griewank, Rastrigrin, Sphere, The simulation experiments of four standard test functions of JDSchaffer were carried out, and the inertia weights in the basic particle swarm optimization algorithm were compared with the nonlinear decrement of fixed weight w = 0.95, linear decreasing LDIW and Chen[6] exponential curve respectively. The improved nonlinear weight decrement strategy is superior to other algorithms in terms of convergence speed, convergence precision number of iterations.

Particle Swarm Optimization-Based Noise Filtering Algorithm for Photon Cloud Data in Forest Area

The Ice, Cloud, and land Elevation Satellite-2 (ICESat-2), which is equipped with the Advanced Topographic Laser Altimeter System (ATLAS), was launched successfully in 15 September 2018. The ATLAS represents a micro-pulse photon-counting laser system, which is expected to provide more comprehensive and scientific data for carbon storage. However, the ATLAS system is sensitive to the background noise, which poses a tremendous challenge to the photon cloud noise filtering. Moreover, the Density Based Spatial Clustering of Applications with Noise (DBSCAN) is a commonly used algorithm for noise removal from the photon cloud but there has not been an in-depth study on its parameter selection yet. This paper presents an automatic photon cloud filtering algorithm based on the Particle Swarm Optimization (PSO) algorithm, which can be used to optimize the two key parameters of the DBSCAN algorithm instead of using the manual parameter adjustment. The Particle Swarm Optimization Density Based Spatial Clustering of Applications with Noise (PSODBSCAN) algorithm was tested at different laser intensities and laser pointing types using the MATLAS dataset of the forests located in Virginia, East Coast, and the West Coast, USA. The results showed that the PSODBSCAN algorithm and the localized statistical algorithm were effective in identifying the background noise and preserving the signal photons in the raw MATLAS data. Namely, the PSODBSCAN achieved the mean F value of 0.9759, and the localized statistical algorithm achieved the mean F value of 0.6978. For both laser pointing types and laser intensities, the proposed algorithm achieved better results than the localized statistical algorithm. Therefore, the PSODBSCAN algorithm could support the MATLAS photon cloud data noise filtering applicably without manually selecting parameters.

FPSOEE: Fuzzy-enabled particle swarm optimization-based energy-efficient algorithm in mobile ad-hoc networks

FPSOEE: Fuzzy-enabled particle swarm optimization-based energy-efficient algorithm in mobile ad-hoc networks

Bi-level programming problem in the supply chain and its solution algorithm

Enterprise-wide supply chain planning problems naturally exhibit a multi-level decision network structure, where the upper level of a hierarchy may have his objective function and decision space partly determined by other levels. In addition, each planner’s control instruments may allow him to influence the policies at other levels and thereby to improve his own objective function. As a tool, bi-level programming is applied for modeling decentralized decisions in which two decision makers make decisions successively. In this paper, we specifically address bi-level decision-making problems with budget constraint as an attractive feature in the context of enterprise-wide supply chain. We first describe the typical bi-level linear programming problem (BLLPP) and its optimal solution to the penalty function problem, and then, a cooperative decision-making problem in supply chain is modeled as BLLPP. A particle swarm optimization-based computational algorithm is designed to solve the problem, and the numerical example is presented to illustrate the proposed framework.

A New Improved Simplified Particle Swarm Optimization Algorithm

A new simplified particle swarm optimization algorithm is proposed for the question that the basic particle swarm optimization algorithm is easy to fall into the local optima. The algorithm introduces new parameters on the basis of simplifying particle swarm, adjusts parameters by adaptive method, and coordinates the relationship between various parameters, which increases the use of particle information and ensures the difference between particles. Through the test function of eight, the improved algorithm can effectively avoid the premature convergence and greatly improve the convergence speed and convergence precision.

Genetic Algorithm-Based Particle Swarm Optimization Approach to Reschedule High-Speed Railway Timetables: A Case Study in China

In this study, a mixed integer programming model is proposed to address timetable rescheduling problem under primary delays. The model considers timetable rescheduling strategies such as retiming, reordering, and adjusting stop pattern. A genetic algorithm-based particle swarm optimization algorithm is developed where position vector and genetic evolution operators are reconstructed based on departure and arrival time of each train at stations. Finally, a numerical experiment of Beijing-Shanghai high-speed railway corridor is implemented to test the proposed model and algorithm. The results show that the objective value of proposed method is decreased by 15.6%, 48.8%, and 25.7% compared with the first-come-first-service strategy, the first-schedule-first-service strategy, and the particle swarm optimization, respectively. The gap between the best solution obtained by the proposed method and the optimum solution computed by CPLEX solver is around 19.6%. All delay cases are addressed within acceptable time (within 1.5 min). Moreover, the case study gives insight into the correlation between delay propagation and headway. The primary delays occur in high-density period (scheduled headway closes to the minimum headway), which results in a great delay propagation.

A dynamic adaptive particle swarm optimization and genetic algorithm for different constrained engineering design optimization problems

A dynamic adaptive particle swarm optimization and genetic algorithm is presented to solve constrained engineering optimization problems. A dynamic adaptive inertia factor is introduced in the basic particle swarm optimization algorithm to balance the convergence rate and global optima search ability by adaptively adjusting searching velocity during search process. Genetic algorithm–related operators including a selection operator with time-varying selection probability, crossover operator, and n-point random mutation operator are incorporated in the particle swarm optimization algorithm to further exploit optimal solutions generated by the particle swarm optimization algorithm. These operators are used to diversify the swarm and prevent premature convergence. Tests on nine constrained mechanical engineering design optimization problems with different kinds of objective functions, constraints, and design variables in nature demonstrate the superiority of the dynamic adaptive particle swarm optimization and genetic algorithm against several other meta-heuristic algorithms in terms of solution quality, robustness, and convergence rate in most cases.

On Optimal Resource Allocation in Multi-RAT Wireless Networks With Receiver Characteristic Awareness

Next-generation wireless systems are expected to use multiple coexisting radio access technologies with different receive and transmit characteristics. This will make the radio frequency front-ends susceptible to unprecedented adjacent-channel interference (ACI), which can jeopardize communication performance. In this paper, we propose a novel ACI-aware joint channel and power allocation framework that takes into account the receiver imperfections arising due to: 1) imperfect image frequency rejection and 2) analog-to-digital converter aliasing. The objective is to minimize the number of allocated channels and the aggregate power transmitted while satisfying the rate demands of different links in a multi-radio access technology environment. As the overall problem is NP-hard, we develop an efficient algorithm to solve it through decomposing the problem into two subproblems: channel allocation and power assignment. Then, it solves the decomposed problem by iteration using a two-phase structure. In Phase I, a particle swarm optimization-based algorithm is tailored to obtain good solutions for the channel allocation subproblem. Phase II solves the power allocation subproblem optimally given the channel allocation solution obtained in Phase I. The results demonstrate: 1) the criticality of receiver-characteristic awareness when designing resource allocation schemes and 2) the superiority of the proposed algorithm in solving the overall problem efficiently.

Particle swarm optimization-based algorithms for solving inverse problems of designing thermal cloaking and shielding devices

Inverse problems associated with designing cylindrical thermal layered shielding and cloaking shells are studied. Using the optimization method these inverse problems are reduced to corresponding control problems. Thermal conductivities of the shell layers play the role of passive controls. A numerical algorithm based on the particle swarm optimization is proposed and the results of numerical experiments are discussed. Important properties of optimal solutions are established. Based on these properties simple design rules are formulated allowing to design cloaking and shielding shells which possess the best cloaking or shielding performance in addition to easy manufacturability.

Asymmetric Bouc–Wen hysteresis modeling and inverse compensation for piezoelectric actuator via a genetic algorithm–based particle swarm optimization identification algorithm

The hysteresis characteristics, which commonly existed in smart materials–based actuators, play a significant role in precision control technology. In this article, a modified Bouc–Wen model which can describe the asymmetric hysteresis characteristics of piezoelectric ceramic actuators is investigated. The corresponding parameters of the modified Bouc–Wen hysteresis model are identified through a genetic algorithm–based particle swarm optimization algorithm. Compared with independent particle swarm optimization method which is easily trapped in the local extremum, the proposed genetic algorithm–based particle swarm optimization features the strong searching ability both in early global search period and the later local search period. The experimental results show that the asymmetric Bouc–Wen model identified via genetic algorithm–based particle swarm optimization algorithm are more accurate than that identified through independent particle swarm optimization or genetic algorithm approach, and the maximum displacement error and the maximum relative error between the genetic algorithm–based particle swarm optimization model and the experimental value are 0.20 µm and 14.28%, respectively, which are much smaller than that of particle swarm optimization method with 0.67 µm and 47.85% and genetic algorithm method with 0.35 µm and 25%. In order to further verify the accuracy of the identified model, the hysteresis compensation of piezoelectric ceramic actuator was realized using the feedforward controller based on the inverse Bouc–Wen model.

Discrimination of polycyclic aromatic hydrocarbons based on fluorescence spectrometry coupled with CS-SVM

A new approach for discrimination of polycyclic aromatic hydrocarbons (PAHs) in environment was proposed based on fluorescence coupled with CS-SVM. Two groups of experiments were carried out on PAHs with similar spectra. The first one was PAHs any two among benzo[k]fluoranthene (BkF), benzo[b]fluoranthene (BbF) and benzo[a]pyrene (BaP). The second one was naphthalene (NAP) and fluorene (FLU) with similar spectra. The BaP-BkF mixture, BaP-BbF mixture and BbF-BkF mixture with similar fluorescence properties, cuckoo search algorithm (CS) optimizing support vector machine (SVM) was used to discriminate these three mixtures. By comparison with the basic grid search algorithm (GS), genetic algorithm (GA) and particle swarm optimization algorithm (PSO) optimizing SVM, it was found that fitting degree of CS was the best and the convergence speed was also the fastest. The test sample classification accuracy of CS-SVM can reach 100%, which was higher than that of GS-SVM, GA-SVM and PSO-SVM. In order to verify the validity of the proposed approach, all the above methods were applied to discriminate NAP and FLU with extremely similar spectra. The test sample classification accuracy can reach 100%. The satisfying results indicated that the proposed approach had potential to be an alternative approach for discriminating PAHs in environment.

Stereo vision-based vehicle localization in point cloud maps using multiswarm particle swarm optimization

We propose a vision-based localization algorithm with multiswarm particle swarm optimization for driving an autonomous vehicle. With stereo vision, the vehicle can be localized within a 3D point cloud map using the particle swarm optimization. For vehicle localization, the GPS (global positioning system)-based algorithms are often affected by the certain conditions resulting in intermittent missing signal. We address this issue in vehicle localization by using stereo vision in addition to the GPS information. The depth-based localization is formulated as an optimization-based tracking problem. Virtual depth images generated from the point cloud are matched with the online stereo depth images using the particle swarm optimization. The virtual depth images are generated from the point cloud using a series of coordinate transforms. We propose a novel computationally efficient tracker, i.e., a multiswarm particle swarm optimization-based algorithm. The tracker is initialized with GPS information and employs a Kalman filter in the bootstrapping phase. The Kalman filter stabilizes the GPS information in this phase and, subsequently, initializes the online tracker. The proposed localization algorithm is validated with acquired datasets from driving tests. A detailed comparative and parametric analysis is conducted in the experiments. The experimental results demonstrate the effectiveness and robustness of the proposed algorithm for vehicle localization, which advances the state of the art for autonomous driving.

Particle swarm optimization-based algorithm of a symplectic method for robotic dynamics and control

Multibody system dynamics provides a strong tool for the estimation of dynamic performances and the optimization of multisystem robot design. It can be described with differential algebraic equations (DAEs). In this paper, a particle swarm optimization (PSO) method is introduced to solve and control a symplectic multibody system for the first time. It is first combined with the symplectic method to solve problems in uncontrolled and controlled robotic arm systems. It is shown that the results conserve the energy and keep the constraints of the chaotic motion, which demonstrates the efficiency, accuracy, and time-saving ability of the method. To make the system move along the pre-planned path, which is a functional extremum problem, a double-PSO-based instantaneous optimal control is introduced. Examples are performed to test the effectiveness of the double-PSO-based instantaneous optimal control. The results show that the method has high accuracy, a fast convergence speed, and a wide range of applications. All the above verify the immense potential applications of the PSO method in multibody system dynamics.