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Task scheduling is one of the most important issues in heterogeneous environments when high efficiency is required. Because task scheduling is a Nondeterministic Polynomial (NP)-hard problem, many evolutionary algorithms have been adopted to solve this problem. Since the convergence speed of solutions in population-based algorithms is low, they are integrated with local search algorithms. Thus, in this paper, to optimize the task scheduling makespan, a hybrid particle swarm optimization and hill climbing algorithm is proposed. The experimental results on random and scientific Directed Acyclic Graph (DAG) showed that the proposed algorithm performs effectively in terms of the makespan compared to the current well-known heuristic and particle swarm optimization algorithms.

In this paper, the particle swarm optimization (PSO) algorithm is adopted to design a modified ring-slot type patch rectenna with a resonance frequency of 2.45GHz. In order to accomplish minimization of dimensions and circular polarization (CP) and harmonic suppression, axial direction slits and side-cuts are added to the patch of the ring. The PSO manipulated this kind of multi-dimensional problem very well, and as a result, the designed rectenna shows a desirable performance of return loss of 21.36dB and axial ratio of 2.92dB at the frequency of 2.45GHz with compact sizing.

In this paper, a study was conducted on the methodology for learning the parameters of a neural network using an evolutionary algorithm such as a particle swarm optimization algorithm. The possibility of using the particle swarm optimization algorithm for deep learning was analyzed, and various methods were considered for practical use.

최근에 진화연산방법은 건설 분야의 여러 문제에 성공적으로 적용되어 왔으며 대표적인 방법으로는 유전자 알고리즘과 Particle Swarm Optimization이 있다. 유전자 알고리즘은 생물유전학과 자연선택이론에 바탕을 둔 병렬적인 최적화 탐색방법이며 PSO는 새나 물고리 무리의 집단적인 행동에서 영감을 얻은 진화형 통계탐색방법이다. 두 방법은 많은 유사한 점을 공유하고 있지만, 어느 한 방법이 다른 방법도 효율과 효과 면에서 항상 뛰어나다는 것을 증명할 수 없다. 이에 본 연구는 유전자 알고리즘과 PSO방법 중 어떤 방법이 공동주택 사업초기단계에서의 공사비 예측문제에서 더 뛰어난 예측성능을 보여주는 지를 조사하였다. 한국건설감리협회의 감리자 입찰공고를 통해 수집한 219개의 공동주택 사업자료를 가지고 NeuroShell Predictor 소프트웨어에서 두 방법을 이용한 예측모델을 제시하였고 모델의 예측성능을 점검하였다. 소프트웨어에서 제공하는 높은 R-squared 및 상관계수값을 통해서 두 예측모델의 성능은 우수한 것으로 판명되었으나 PSO 기반 예측모델이 유전자 알고리즘 기반 모델보다 평가시험그룹에서의 예측정확도면에서 약간 더 우수한 것으로 나타났다.

In recent years, evolutionary computation methods have been successfully applied to various problems in the construction field. Two of these popular methods are Genetic Algorithm(GA) and Particle Swarm Optimization(PSO). GA is a parallel search method for an optimal solution based on the genetics and natural selection and PSO is a population-based stochastic optimization method inspired by social behavior of bird flocking or fish schooling. While two methods share many similarities, one cannot be proven to always outperform the other in terms of efficiency and effectiveness. From this point, this paper examined whether GA or PSO is superior at a problem to predict construction cost on apartment housing projects at the early stage. With 219 apartment housing project data obtained from bidding announcements for construction supervisors in the Korea Construction Consulting Engineers Association, prediction models using two methods were suggested in the NeuroShell Predictor software and prediction performances of two models were checked. From high R-squared and correlation coefficient values provided by the software, performances of two prediction models were found to be good. However, the findings also showed that the PSO-based model is slightly better than the GA-based one in the comparison of prediction accuracy on the testing dataset.

Location problem of multi-distribution center is a kind of NP hard problem. To solve such problems, this paper proposes a chaos adaptive mutation particle swarm optimization algorithm. The algorithm uses the ergodic property of chaos to initialize the particle swarm to enhance the diversity of the population, according to the variance of population fitness to adjust the probability of mutation, and adjust the inertia weight factor to improve the global and local search capability of the whole population. In this paper, the algorithm is applied to the location problem of multi-distribution center, established the multi-factor constraints of mathematical model which aiming at timeliness, and on this basis, the corresponding algorithm is designed. It can be seen from the location instance simulation results that the optimization results and efficiency of the adaptive mutation particle swarm optimization algorithm is better than the genetic algorithm and the standard particle swarm optimization algorithm.

Because the accuracy of traditional sentiment orientation identification algorithm is not high under Q&A community, this paper proposes a new method based on two-level conditional random field improved by particle swarm optimization algorithm for emotion tendency recognition under Q&A community. The proposed method adopts particle swarm optimization algorithm to train two-level conditional random field model, and applies the trained conditional random field model to recognize emotion orientation of question-answer pairs in Q&A community. Experiments were performed on Yahoo! Answers data set and results show that the proposed two-level conditions random field improved by particle swarm optimization algorithm has a higher precision rate, recall rate and F1 value at the micro average and macro average aspects compared with Hidden Markov Model, Max-Entropy Markov Model, Support Vector Machine and traditional condition random domain model, which prove the proposed two-level conditions random field improved by particle swarm optimization algorithm is a more effective method to recognize emotion orientation of question-answer pairs in Q&A community.

Particle swarm optimization (PSO) algorithm is a population-based search algorithm by simulating the social behavior of birds within a flock. It is a simple and efficient optimization algorithm. But it exists the low computational speed and easy falling into local optimal solution in solving the complex problem. So the quantum theory, adaptive inertia weight, disturbance factor and diversity mutation strategy are introduced into the PSO algorithm in order to propose an improved PSO(IWDMDQPSO) algorithm in this paper. In the IWDMDQPSO algorithm, the quantum theory is used to change the updating mode of the particles for guaranteeing the simplification and effectiveness of the algorithm. The adaptive inertia weight is used to improve the premature convergence of the algorithm. The disturbance factor is used to avoid the premature of the algorithm. The diversity mutation strategy is used to improve the global searching ability and computation speed. Finally, the famous benchmark functions are selected to prove the performance and effectiveness of the proposed IWDMDQPSO algorithm. The experiment results show that the proposed IWDMDQPSO algorithm takes on better solving accuracy and higher computation speed in solving the complex function. So it has a remarkable optimization performance.

Based on particle swarm optimization (PSO) algorithm and its power system reactive power optimization method to in-depth study and research proposed a new hybrid particle swarm optimization algorithm (HPSO). Algorithm combines the differential evolution algorithm and simulated annealing algorithm and particle swarm optimization algorithm, in particle searching optimal except for tracking individual and global, and tracks produced by particle information difference of the three value. At the same time, when the particle search space of one dimension speed lower than the setting value will be re initialized the dimensional particle velocity and the particle of differential evolution mutation. For the crossover and mutation operations, new solution may be worse than the original solution to, the introduction of simulated annealing algorithm, the metropolis rule in a certain extent accept bad solutions, allows the target function in a certain degree of deterioration, practical calculation is not according to the probability to choose the poor solution, but rather the judgment target function difference is less than allows the target function deterioration range. Hybrid particle swarm optimization algorithm combines the advantages of particle swarm optimization algorithm, differential evolution algorithm and simulated annealing algorithm, to maintain the diversity of particles, has very strong practicability.

This paper aims to present a self-adaptive global particle swarm optimization (SGPSO) algorithm for solving unconstrained optimization problems. In the new algorithm, the inertia weights are generated based on Gaussian distribution, which is helpful to improve the diversity of the population. In addition, the worst particle is updated by averaging the other particles, which is beneficial to improving the quality of the population. Finally, a global disturbance is adopted to increase the convergence rate of SGPSO. In the disturbance process, a disturbance factor is utilized to control the searching ranges of the population, which can effectively keep a balance between the global exploration and local exploitation. Twenty well-known benchmark functions are considered to evaluate the performance of SGPSO, and 50 runs are implemented in each case. Numerical experiments and comparisons demonstrate that SGPSO is superior to the other three algorithms according to means, standard deviations and convergence rate.

Task scheduling and resource scheduling are the core issue in cloud computing. Pointing at the premature problem in the scheduling algorithm of particle swarm, we propose a scheduling algorithm of cloud task particle swarm based on “fission” mechanism in this paper. The particle in traditional particle swarm algorithm gets “fission” by the new algorithm in appropriate place, to get more kinds of the particles, contributing to the particle swarm diversity, avoiding premature convergence of the swarm. As the experimental result shows that, the algorithm in this paper has faster scheduling efficiency than the FIFO and the PSO, also solves the premature problem in PSO.

Image mining is used to mine knowledge from large image databases. Image segmentation, image compression, image clustering, image classification and image retrieval are significant image mining tasks. Face detection methods are used to identify the similar faces from the large collection of facial images. It has numerous computer vision applications and it has many research challenges such as rotation, scale, pose and illumination variation. Facial expression is defined as the position of the muscles beneath the skin of the face and it is a form of nonverbal communication. Facial expressions are the expression which shows the emotions and different feelings of human beings. Different facial expressions are sad, happy, fear, normal, surprise and angry. In this research work facial expressions are classified by using the optimization algorithms. PSO with LIBSVM algorithm is proposed for facial expression classification and the performance of this algorithm is compared with the existing BAT algorithm. The results of the existing and proposed algorithms are analyzed based on the two performance factors; they are classification accuracy and execution time. From the experimental results, we observed that the proposed PSO with LIBSVM algorithm has produced good results compared to existing BAT algorithm. This work is implemented in MATLAB 7.0.

The thesis mainly studies bi-criteria no-wait flexible flow shop problem, whose optimi-zation objective is to minimize the maximum completion time and the maximum delay time. This problem is NP hard, yet enjoying important theoretical research value, thereby this thesis proposes elite particle swarm optimization (EPSO) to solve bi-criteria no-wait flex-ible flow shop problem. EPSO algorithm applies five modified heuristic algorithms and random methods to generating initial population. Moreover, for the particle personal best, this thesis puts forward elite crossover algorithm, which retains continuous fragments of the identical workpieces among excellent individuals, avoiding the destruction of good continuity between solutions of workpieces. In addition, in order to avoid algorithm into local optimum, this thesis raises double insertion disturbance algorithm to help particles jump out the local optimal state and expand the feasible search range. For the purpose of effectively evaluating algorithm quality, there is a comparison among EPSO algorithm, PSO algorithm and ICA algorithm in simulation experiment that is respectively aimed at small-scale problem and large-scale scheduling problem, the results of which show that the proposed EPSO algorithm, due to better validity and accuracy, is superior to the PSO algorithm and ICA algorithm.

Traditional flexible flow shop scheduling cannot adapt to the work processes with existence of parallel machines, and blocks or limits the processes with no-wait constraints. Firstly, according to the problem in NWBFFSSP, which minimizes the maximum time used in the flow shop, an optimal solving model has been designed to realize the flexible flow shop scheduling with multi constraints; besides, for the distribution of machinery is improved, Finally, in the solving process, a real-time release priority strategy has been proposed to determine processing machine for each work piece. Furthermore, a methodology to detect work piece conflicts has been introduced while the conflicts are then eliminated by a kind of right moving strategy based on the maximum difference. The experimental results verify the effectiveness and feasibility of the proposed algorithm.

The core issues of RBF network design are to design the minimum structure neural networks that can meet the accuracy requirements, in order to ensure the generalization ability of the network. For the purpose of simplifying the structure of RBF network, proposes a learning method of RBF network based on improved particle swarm. The method automatically constructs frugal structure of RBF network model by the combining algorithm of regularized least squares method and D- optimal experimental design; chooses three learning parameters of the combining algorithm that can affect network generalization performance by the improved particle swarm optimization algorithm. By nonlinear time series modeling, verifies the effectiveness of the method in this paper.

Mobile Internet due to the limitation of the mobile terminal power supply, transmission and calculation of the need to adopt energy saving strategy, also due to the terminal mobility, mobile Internet topology change. Therefore, mobile Internet cloud computing resources allocation need both energy efficiency and assure the time characteristics of mobile business; Aiming at this problem, this paper presents a maximum energy efficiency optimization, in the restrictive conditions at the same time, guarantee the minimum time delay the business. According to the characteristics of the optimization problems, both the distribution of the improved algorithm is proposed, the algorithm based on particle swarm optimization (pso) algorithm, build the search direction matrix of orientation, the simulation results show that the proposed allocation algorithm can effectively improve the efficiency of energy utilization, and ensure that the time delay of the business requirements.

With the growing number of alternative services in the cloud environment, users have put forward new requirements to solve the service dynamic selection problem quickly and efficiently. In this paper, an evaluation model of service process which considers concurrent requests and service association is proposed. This model evaluates the service process from three dimensions which are functional quality dimension, non-functional quality dimension and transactional dimension. To solve the service selection problem efficiently, we first design a novel coding strategy of particle, and then propose an approach based on hybrid particle swarm optimization algorithm which combines the crossover and mutation operators of genetic algorithm. The experimental results show that our proposed approach is feasible and effective.

In orthogonal frequency division multiplexing Access (OFDMA) systems resource allocation to the subcarrier is essential owing to the insufficient resources available at the base station. In OFDMA, subcarrier and power allocation are not separate; thereby this two allocation are not self-governing. This paper proposes the subcarrier allocation approach through investigating previous methods, and also the Enhanced Particle Swarm Optimization (PSO) algorithm is analyzed. The both of the algorithms cross functionality is analyzed and compared the performance of the subcarrier allocation of OFDMA systems in Heterogeneous Network (HetNet). The simulation results show that the PSO approach efficiently allocates the OFDMA subcarriers.

Considering the shortcomings of the conventional BP neural network, such as slow learning speed, weak anti-interference ability and easy to fall into local minimum, the detection accuracy of P2P traffic detection model is low and the speed is slow, the particle swarm optimization algorithm is used to optimize it here. As the conventional algorithm's optimization ability is the initial parameters, the algorithm is easy to be early, and the convergence speed is slow. Therefore, grouping, organizing, fission and mutation operation on the conventional algorithm have been carried on in order to improve the defect of conventional algorithm. Finally, the P2P traffic detection model is built by using MATLAB software, and traffic detection experiments are carried out on Bittorrent, EMule, PPlive and PPStream 4 P2P network applications. The test data show that the average recognition rate of the recognition model is 96.14%, which is 13.3% higher than that of the conventional PSO-BP model, and9.4% higher than that of the QPSO-BP recognition model for the four P2P network applications.