Traditional Particle Swarm Optimization Algorithm Research Articles

Interactive adaptive particle swarm optimisation for optimal global supply chain design

This paper integrates all concerned levels of supply chain with their conflicting objectives and identifies the best solution for its design. More precisely two objectives viz. maximisation of overall quality and overall cost have been targeted. Considering both objectives, a multi-objective model has been formulated to integrate both tangible and intangible factors in the resource assignment problem of a product driven supply chain. Quality corresponding to each entity has been determined by applying a fuzzy-analytical hierarchical process approach. Minimisation of cost has been mathematically formulated with due consideration of various cost types. Proposed interactive adaptive multi-objective algorithm incorporates the decision maker's preference model to improve the accuracy of PSO in deciding the weight corresponding to each objective considered. Extensive experiments are performed on the underlying example, and computational results are reported and compared with the traditional particle swarm optimisation (PSO) algorithm and genetic algorithm to support the efficacy of the proposed algorithm.

Interactive adaptive particle swarm optimisation for optimal global supply chain design

This paper integrates all concerned levels of supply chain with their conflicting objectives and identifies the best solution for its design. More precisely two objectives viz. maximisation of overall quality and overall cost have been targeted. Considering both objectives, a multi-objective model has been formulated to integrate both tangible and intangible factors in the resource assignment problem of a product driven supply chain. Quality corresponding to each entity has been determined by applying a fuzzy-analytical hierarchical process approach. Minimisation of cost has been mathematically formulated with due consideration of various cost types. Proposed interactive adaptive multi-objective algorithm incorporates the decision maker's preference model to improve the accuracy of PSO in deciding the weight corresponding to each objective considered. Extensive experiments are performed on the underlying example, and computational results are reported and compared with the traditional particle swarm optimisation (PSO) algorithm and genetic algorithm to support the efficacy of the proposed algorithm.

Virtual machine-based task scheduling algorithm in a cloud computing environment

Virtualization technology has been widely used to virtualize single server into multiple servers, which not only creates an operating environment for a virtual machine-based cloud computing platform but also potentially improves its efficiency. Currently, most task scheduling-based algorithms used in cloud computing environments are slow to convergence or easily fall into a local optimum. This paper introduces a Greedy Particle Swarm Optimization (G&PSO) based algorithm to solve the task scheduling problem. It uses a greedy algorithm to quickly solve the initial particle value of a particle swarm optimization algorithm derived from a virtual machine-based cloud platform. The archived experimental results show that the algorithm exhibits better performance such as a faster convergence rate, stronger local and global search capabilities, and a more balanced workload on each virtual machine. Therefore, the G&PSO algorithm demonstrates improved virtual machine efficiency and resource utilization compared with the traditional particle swarm optimization algorithm.

CS-PSO: chaotic particle swarm optimization algorithm for solving combinatorial optimization problems

Combinatorial optimization problems are typically NP-hard, due to their intrinsic complexity. In this paper, we propose a novel chaotic particle swarm optimization algorithm (CS-PSO), which combines the chaos search method with the particle swarm optimization algorithm (PSO) for solving combinatorial optimization problems. In particular, in the initialization phase, the priori knowledge of the combination optimization problem is used to optimize the initial particles. According to the properties of the combination optimization problem, suitable classification algorithms are implemented to group similar items into categories, thus reducing the number of combinations. This enables a more efficient enumeration of all combination schemes and optimize the overall approach. On the other hand, in the chaos perturbing phase, a brand-new set of rules is presented to perturb the velocities and positions of particles to satisfy the ideal global search capability and adaptability, effectively avoiding the premature convergence problem found frequently in traditional PSO algorithm. In the above two stages, we control the number of selected items in each category to ensure the diversity of the final combination scheme. The fitness function of CS-PSO introduces the concept of the personalized constraints and general constrains to get a personalized interface, which is used to solve a personalized combination optimization problem. As part of our evaluation, we define a personalized dietary recommendation system, called Friend, where CS-PSO is applied to address a healthy diet combination optimization problem. Based on Friend, we implemented a series of experiments to test the performance of CS-PSO. The experimental results show that, compared with the typical HLR-PSO, CS-PSO can recommend dietary schemes more efficiently, while obtaining the global optimum with fewer iterations, and have the better global ergodicity.

Fast Convergence and Improved Particle Swarm Hybrid Optimization Algorithm

Aiming at the problem that the particle in the traditional particle swarm optimization algorithm is easy to fall into the local optimum and the convergence rate is slow, this paper proposed an improved particle swarm optimization algorithm. In particle swarm optimization algorithm, the advantages and disadvantages of the algorithm is directly decided by the performance of the particle, the paper introduced the chaos mechanism, enhance the ergodicity and particle will be quantized in the solution space, on the premise of ensuring diversity of solution, the particle get better global search ability. Meanwhile, based on the problem of slow convergence speed of the algorithm in the late, on the one hand to dynamically adjust the inertia weight of impact speed, makes the particle movement speed tend to be reasonable, on the other hand, using k-means algorithm to optimize progeny particle and get more reasonable clustering center, make the algorithm fast convergence. Experiments show that using improved Particle Swarm Optimization algorithm with high precision, strong stability and fast convergence.

An efficient image segmentation method based on a hybrid particle swarm algorithm with learning strategy

Traditional threshold image segmentation method is a time consuming problem, we aim to find an effective optimal tool for proper threshold segmentation methods (e.g. Otsu and Kapur). In this work, we present a learning strategy based particle swarm optimization algorithm with an exchange method (LPSOWE). First, for enhancing the exploration ability and maintaining the convergence rate of the traditional particle swarm optimization algorithm (PSO), new jumping operators and learning items are proposed for a favorable update equation of PSO. Second, since particles are updated as a whole item in PSOs, a random cross operator and an exchange strategy are further investigated for the particles to have more chances for exploring the search space on each dimension. The Berkeley segmentation data set is used for comparisons with other algorithm and the results show that the proposed algorithm gets better results over the Evolutionary Computation (EC) based algorithms.

Discrete Particle Swarm Optimization Routing Protocol for Wireless Sensor Networks with Multiple Mobile Sinks.

Mobile sinks can achieve load-balancing and energy-consumption balancing across the wireless sensor networks (WSNs). However, the frequent change of the paths between source nodes and the sinks caused by sink mobility introduces significant overhead in terms of energy and packet delays. To enhance network performance of WSNs with mobile sinks (MWSNs), we present an efficient routing strategy, which is formulated as an optimization problem and employs the particle swarm optimization algorithm (PSO) to build the optimal routing paths. However, the conventional PSO is insufficient to solve discrete routing optimization problems. Therefore, a novel greedy discrete particle swarm optimization with memory (GMDPSO) is put forward to address this problem. In the GMDPSO, particle’s position and velocity of traditional PSO are redefined under discrete MWSNs scenario. Particle updating rule is also reconsidered based on the subnetwork topology of MWSNs. Besides, by improving the greedy forwarding routing, a greedy search strategy is designed to drive particles to find a better position quickly. Furthermore, searching history is memorized to accelerate convergence. Simulation results demonstrate that our new protocol significantly improves the robustness and adapts to rapid topological changes with multiple mobile sinks, while efficiently reducing the communication overhead and the energy consumption.

Improved Particle Swarm Optimization with Dynamic Fractional Order Velocity and Wavelet Mutation

Particle Swarm Optimization (PSO) is one of the most powerful algorithms for optimization. Traditional PSO algorithm tends to suffer from slow convergence and trapping into local optimum. In this paper, an improved PSO algorithm is proposed by combining dynamic fractional order technology and the wavelet mutation strategy. In the proposed method, a dynamic fractional order velocity update equation is designed to control the convergence rate. Furthermore, the wavelet mutation mechanism is employed to improve the swarm diversity and escape from the local optimums. The experimental results show that the proposed algorithm can provide fast convergence speed and high convergence precision based on the ten classic test functions.

Parameter Identification of Chaotic Systems by a Novel Dual Particle Swarm Optimization

In this paper, we propose a dual particle swarm optimization (PSO) algorithm for parameter identification of chaotic systems. We also consider altering the search range of individual particles adaptively according to their objective function value. We consider both noiseless and noisy channels between the original system and the estimation system. Finally, we verify the effectiveness of the proposed dual PSO method by estimating the parameters of the Lorenz system using two different data acquisition schemes. Simulation results show that the proposed method always outperforms the traditional PSO algorithm.

Research on AC Asynchronous Motor Vector Control Speed Control System Based on Labview

During the process of using traditional particle swarm optimization (PSO) to identify electric parameters, the algorithm can be easily caught in locally optimal solution, thus leading to relatively large identification result error. Therefore, the article proposes a simulated annealing particle swarm optimization (SA-PSO) algorithm to integrate the advantages, namely the strong global optimization capability of simulated annealing (SA) algorithm and the fast convergence speed of PSO algorithm, in order to improve the traditional PSO algorithm, and uses simulated annealing principle to determine inertia weight of PSO algorithm. Meanwhile, DFIG with the unit capacity of 1.5MW has been taken as the research object for simulation analysis. The test system employs LabVIEW software for experiment design, and the result shows: compared with the identification result of traditional PSO algorithm, SA-PSO algorithm can rapidly and accurately identify DFIG electric parameters and the identification result has higher precision.

A fully and discriminatorily informed particle swarm optimization with different sharing strategies for superior and inferior information

What kind of information is used and in which way the particles interact with each other have direct effects on the particle swarm optimization (PSO) algorithm efficiency. In order to use the information more fully, effectively and reasonably, the paper proposes a fully and discriminatorily informed PSO (FDIPSO) algorithm. Unlike the traditional PSO algorithm, the algorithm takes the positive effects of all the superior neighbors and the negative effects of the inferior particles into account and employs different mechanisms for attractive and repulsive effects to prevent confusion of swarm evolution caused by the different effects. The superior information is fully used to build high quality equilibrium points as attractors to guide particles while the repulsive effect from inferior information is embodied by introducing an ‘escape coefficient’ to help adjust the movement of particles. Experimental studies are conducted on a set of well-known benchmark functions including unimodal, multimodal and rotated problems. Computational results show positive effect and negative effect work collaboratively in FIDPSO, verify the relative superiority of this strategy over four other information sharing strategies and indicate that the approach outperforms several other state-of-art PSO variants on the test problems.

Improved particle swarm optimization for maximum power point tracking in photovoltaic module arrays

In this paper, a maximum power point tracking (MPPT) method that incorporated shading and failure conditions in photovoltaic (PV) module arrays is developed. This MPPT method was built using improved particle swarm optimization (PSO). The PSO algorithm enables PV module arrays to perform MPPT for multi-peak power–voltage (P–V) output characteristic curves when shading or failures occur. This facilitates the tracking of actual maximum power points in PV module arrays. The HIP 2717 PV module produced by SANYO Electric Co., Ltd. was used in this study to assemble various array configurations. The characteristic curves of these array configurations when partial module shading or failure occurred were investigated. Numerous working conditions were selected for dual-peak, three-peak, and four-peak characteristics. PIC microcontrollers were then used to apply both the traditional and the proposed PSO algorithms to enable MPPT. A comparison of the measurement results showed that the proposed PSO algorithm exhibited superior tracking speed, response, and accuracy, compared with those of the traditional PSO algorithm.

Network Scheduling Model of Cloud Computing based on Particle Swarm Optimization Algorithm

The paper proposed a network scheduling in cloud computing based on intelligence Particle Swarm Optimization algorithm aimed at the disadvantages of cloud computing network scheduling. Firstly, on the basis of cloud model, used intelligence Particle Swarm Optimization algorithm with strong ability of global searching to find the better solution of cloud computing network scheduling then turned the better solution into the initial pheromone of improved Particle Swarm Optimization algorithm, and found out the cloud computing network scheduling and the algorithm’s global optimal solution through improved Particle Swarm Optimization information communications and feedbacks. Finally, made comparison test of the three benchmark function on the basis of MATLAB, the results showed, compared with traditional intelligence Particle Swarm Optimization algorithms, the improved algorithm can preferably allocate the resources in cloud computing model, the effect of prediction model time is more close to actual time, can efficiently limit the possibility of falling into local convergence, the optimal solution’s time of objective function value is shorten which meet the user’s needs more.

Improved particle swarm optimization algorithm and its application in text feature selection

Text feature selection is an importance step in text classification and directly affects the classification performance. Classic feature selection methods mainly include document frequency (DF), information gain (IG), mutual information (MI), chi-square test (CHI). Theoretically, these methods are difficult to get improvement due to the deficiency of their mathematical models. In order to further improve effect of feature selection, many researches try to add intelligent optimization algorithms into feature selection method, such as improved ant colony algorithm and genetic algorithms, etc. Compared to the ant colony algorithm and genetic algorithms, particle swarm optimization algorithm (PSO) is simpler to implement and can find the optimal point quickly. Thus, this paper attempt to improve the effect of text feature selection through PSO. By analyzing current achievements of improved PSO and characteristic of classic feature selection methods, we have done many explorations in this paper. Above all, we selected the common PSO model, the two improved PSO models based respectively on functional inertia weight and constant constriction factor to optimize feature selection methods. Afterwards, according to constant constriction factor, we constructed a new functional constriction factor and added it into traditional PSO model. Finally, we proposed two improved PSO models based on both functional constriction factor and functional inertia weight, they are respectively the synchronously improved PSO model and the asynchronously improved PSO model. In our experiments, CHI was selected as the basic feature selection method. We improved CHI through using the six PSO models mentioned above. The experiment results and significance tests show that the asynchronously improved PSO model is the best one among all models both in the effect of text classification and in the stability of different dimensions.

Search-based QoS ranking prediction for web services in cloud environments

Unlike traditional quality of service (QoS) value prediction, QoS ranking prediction examines the order of services under consideration for a particular user. To address this NP-Complete problem, greedy strategy-based solutions, such as CloudRank algorithm, have been widely adopted. However, they can only produce locally approximate solutions. In this paper, we propose a search-based prediction framework to address the QoS ranking problem. The traditional particle swarm optimization (PSO) algorithm has been adapted to optimize the order of services according to their QoS records. In real situations, QoS records for a given consumer are often incomplete, so the related data from close neighbour users is often used to determine preference relations among services. In order to filter the neighbours for a specific user, we present an improved method for measuring the similarity between two users by considering the occurrence probability of service pairs. Based on the similarity computation, the top-k neighbours are selected to provide QoS information support for evaluation of the service ranking. A fitness function for an ordered service sequence is defined to guide search algorithm to find high-quality ranking results, and some additional strategies, such as initial solution selection and trap escaping, are also presented. To validate the effectiveness of our proposed solution, experimental studies have been performed on real-world QoS data, the results from which show that our PSO-based approach has a better ranking for services than that computed by the existing CloudRank algorithm, and that the improvement is statistically significant, in most cases.

Solubility prediction of supercritical carbon dioxide in 10 polymers using radial basis function artificial neural network based on chaotic self-adaptive particle swarm optimization and K-harmonic means

Excellent prediction modeling of CO2solubility in polymers using hybrid computation algorithm.

Mathematical Model and IDPSO Algorithm Researchon Dynamic Fleet Planning

A mathematical model for dynamic fleet planning with multi routes and multi ship, which took the maximum total operation profits as its objective function, was built up according to the characteristics of the shipping company. The model had the following characteristics: Firstly in the model, the long-term fleet planning and short-term scheduling were combined. Secondly, the economic and technology indexes were changed with ship's age, which fully reflect the dynamic fleet planning. At the same time, because of being at a large scale, discrete, multi-dimensional and multi-stage optimiza- tion problem has become very difficult to solve. An improved discrete particle swarm optimal (IDPSO) algorithm was used to solute the model. According to the characteristics of the traditional discrete particle swarm optimization algorithm, some methods including coding strategy, iterative formula, initialization, dimension mutation operator to avoid precocious etc. for discrete particle swarm algorithm, were used to improve the method, which can be more suitable for fleet plan- ning's mathematical model and to avoid premature phenomenon. Finally, an example was given to show that the im- proved method was effective.

Characterising the searchability of continuous optimisation problems for PSO

The focus of research in swarm intelligence has been largely on the algorithmic side with relatively little attention being paid to the study of problems and the behaviour of algorithms in relation to problems. When a new algorithm or variation on an existing algorithm is proposed in the literature, there is seldom any discussion or analysis of algorithm weaknesses and on what kinds of problems the algorithm is expected to fail. Fitness landscape analysis is an approach that can be used to analyse optimisation problems. By characterising problems in terms of fitness landscape features, the link between problem types and algorithm performance can be studied. This article investigates a number of measures for analysing the ability of a search process to improve fitness on a particular problem (called evolvability in literature but referred to as searchability in this study to broaden the scope to non-evolutionary-based search techniques). A number of existing fitness landscape analysis techniques originally proposed for discrete problems are adapted to work in continuous search spaces. For a range of benchmark problems, the proposed searchability measures are viewed alongside performance measures for a traditional global best particle swarm optimisation (PSO) algorithm. Empirical results show that no single measure can be used as a predictor of PSO performance, but that multiple measures of different fitness landscape features can be used together to predict PSO failure.

Prediction for Gas Emission Quantity of the Working Face Based on LSSVM Optimized by Improved Particle Swarm Optimization

Gas emission quantity may forecast the quantity of gas inside the coal, which has important significance for predicting the outburst of gas, but the problem always has not been well solved. Traditional Particle swarm optimization (PSO) algorithm lacks the ability to track the optimal solution while the fitness function changes. An improved algorithm named Time Variant PSO (TVPSO) was proposed to track the optimal solution online. Then it was used to choose the parameters of Least Square Support Vector Machine (LSSVM), which could avoid the man-made blindness and enhance the efficiency of online forecasting. The TVPSO-LSSVM method is based on the minimum structure risk of SVM and the globally optimizing ability of TVPSO to forecast continuously the gas emission quantity of the working face. The method was applied to solve the problem of nonlinear chaos time series prediction. Result shows that the method satisfies the need of online forecasting.

Containing Small Hydropower and Wind Power System Optimal Spinning Reserve

When larger scale of small hydropower and wind power exist in power system, the traditional reserve capacity algorithm is no longer applicable at the same time of reducing the carbon emission. Aiming at the volatility and non-regulatory of small hydropower and wind power, considering the forecast error of small hydropower, wind power and load,the unit outage rate and other factors, combined with the actual operation parameters of one regional power system in Southwest of China, the wind-hydro – thermal power coordinated operation of multiple objective function optimization calculation model of reserve capacity is established. Using the weighted coefficient method of unified objective method, it transforms the multi-objective optimization problem into a single objective optimization one. Applying the exterior penalty function method, the constrained optimization problem is changed into a non-constrained optimization one. The improved particle swarm optimization algorithm is got by introducing the particle concentration cognition into traditional particle swarm optimization algorithm. The simulation results verify the validity of the model. To Compare them under different strategies, this method can get less fuel expenses of thermal power units in the case of lower loss of load probability (LOLP).With the small hydropower and wind power output size to optimize the spinning reserve capacity. The model and the algorithm are helpful for drawing up optimization strategies of the reserve capacity in those regions in which larger scale small hydropower and wind power exists.