Applying Particle Swarm Optimization to Transfer Function Specification for Direct Volume Rendering

A novel Cooperative Motion Estimation Algorithm based on Particle Swarm Optimization and its multicore implementation

Particle swarm optimization, as an evolutionary computing technique, has succeeded in many continuous problems, but research on discrete problems especially combinatorial optimization problem has been done little according to Kennedy and Eberhart (1997) and Mohan and Al-kazemi (2001). In this paper, a modified particle swarm optimization (PSO) algorithm was proposed to solve a typical combinatorial optimization problem: traveling salesman problem (TSP), which is a well-known NP-hard problem. Fuzzy matrices were used to represent the position and velocity of the particles in PSO and the operators in the original PSO formulas were redefined. Then the algorithm was tested with concrete examples in TSPLIB, experiment shows that the algorithm can achieve good results.

High-performance beamforming incorporating multiple objectives for large-scale antenna arrays becomes increasingly important to improve the capacity and efficiency of wireless communication systems. The speed of synthesizing a desired beam pattern is critical in wireless communications systems to adapt to highly dynamic wireless channels. A modified particle swarm optimization (PSO) algorithm for synthesizing array beam patterns is proposed in this study. The initial positions of particles in PSO are designated following a Taylor distribution instead of being given uniformly distributed random values as in the classical PSO algorithm. The fitness functions are defined to include multiple objectives represented by producing multiple main lobes with customized deep and broadened nulls. Several scenarios have been established to examine the feasibility of the proposed algorithm. Moreover, the performance of the proposed algorithm is compared with those of the ones based on the classical PSO. A significant performance improvement for obtaining beamforming coefficients has been achieved. The robustness of the proposed algorithm is demonstrated further by applying it to a finite array on a curved surface for beamforming.

In this paper, a novel hybrid natural computing approach, called PGQ, combining Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and quantum computation, is introduced to solve the Vehicle Routing Problem (VRP). We propose a quantum approach, called QUP, to update the particles in PSO. And, we add GA operators to improve population quality. The simulation results show that the PGQ algorithm is very effective, and is better than simple PSO and GA, as well as PSO and GA mixed algorithm.KeywordsVRPPSOGAQuantum computation

Cloud computing provides the computational machines as a support of the clients utilizing cloud organize. In cloud computing, the user inputs are executed with required machines to convey the administrations. Numerous task scheduling methods are utilized to plan the client tasks to the machines. In this paper, another successful hybrid task scheduling is proposed to minimize the total execution time using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithms. In hybrid Genetic Algorithm - Particle Swarm Optimization (GA-PSO) algorithm, PSO helped GA to obtain better results compare to a standard genetic algorithm, Min-Min, and Max-Min algorithms results.

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

The Cloud computing is a most recent computing paradigm where IT services are provided and delivered over the Internet on demand. The Scheduling problem for cloud computing environment has a lot of awareness as the applications tasks could be mapped to the available resources to achieve better results. One of the main existed algorithms of task scheduling on the available resources on the cloud environment is based on the Particle Swarm Optimization (PSO). According to this PSO algorithm, the applications tasks are allocated to the available resources to minimize the computation cost only. In this paper, a modified PSO algorithm has been introduced and implemented for solving task scheduling problem in the cloud. The main idea of the modified PSO is that the tasks are allocated on the available resources to minimize the execution time in addition to the computation cost. This modified PSO algorithm is called Modified Particle Swarm Optimization (MPOS).The MPOS evaluations have been illustrated using different time, and cost parameters and their effects in the performance measures such as utilization, speedup, and efficiency. According to the implementation results, it is found that the modified MPOS algorithm outperforms the existed PSO.

The optimal power flow (OPF) problem was introduced by Carpentier in 1962 as a network constrained economic dispatch problem. Since then, it has been intensively studied and widely used in power system operation and planning. In the past few decades, many stochastic optimization methods such as Genetic Algorithm (GA), Evolutionary Programming (EP), and Particle Swarm Optimization (PSO) have been applied to solve the OPF problem. In particular, PSO is a newly proposed population based stochastic optimization algorithm. The main idea behind it is based on the food-searching behavior of birds and fish. Compared with other stochastic optimization methods, PSO has comparable or even superior search performance for some hard optimization problems in real power systems. Nowadays, some modifications such as breeding and selection operators are considered to make the PSO superior and robust. In this paper, we propose the Modified PSO (MPSO), in which the mutation operator of GA is incorporated into the conventional PSO to improve the search performance. To verify the optimal solution searching ability, the proposed approach has been evaluated on an IEEE 30-bus test system. The results showed that performance of the proposed approach is better than that of the standard PSO.

Gene Clustering is one of the most popular application in the field of Bioinformatics. It is a method of grouping gene into clusters, such that each cluster must have similar gene expression levels. The two most popular population based globalized search algorithms Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) are used for clustering gene expression data but the main drawback of these two algorithm's is that they got trapped in local optima while exploring across the problem space. So the PSO and GA has been hybridized with several other methods, to overcome the local optima problem. This paper presents a literature survey of both GA and PSO's application in Gene Clustering. PSO and GA variants and their hybridization with other metaheuristics are also described in this paper. An attempt is made to provide a guide for the researchers who are working in the area of population based gene clustering. Bioinformatics field deals with the analysis of the biological data, which stored information in the form of DNA, protein sequence in various biological databases. The Bioinformatics field involves various problem such as Gene Clustering Problem, Molecular Docking Problem, Multiple Sequence Alignment Problems, Phylogenetic tree construction , RNA Secondary Structure Prediction, Protein Secondary Structure Prediction, Fragment Assembly Problem. In Gene Clustering the gene is defined as a sequence of nucleotide bases, which carry the required information for the purpose of protein formation, which helps in the construction of the structural components. Clustering is a process of combining input patterns into groups based on the similar functionality. So, Gene clustering is a process of grouping genes in the same clusters based on similar gene expression levels. Gene expression refers to a process through which the coded information from a gene is converted into structures operating in the cell. In order to cluster the Gene expression data , various population based algorithms were used by a number of researchers. The two most popular algorithms among these are PSO (Particle Swarm Optimization) and

To solve the strong randomicity and slow convergence of the Particle Swarm Optimization(PSO) algorithms, two new particle’s position renewal formulas were analyzed on the basis of extrapolation in mathematics. A new modified PSO algorithms (called Leading PSO algorithms) was put forward. The Direct Torque Control(DTC) System was built in the environment of Matlab(Simulink). The weight and threshold values of BP Neural Network were trained using the modified PSO algorithms. Some disadvantages such as slow convergence speed and easily plunging into the local solution were avoided effectively. The simulation result shows that the system works well, and the rotor speed identifier has great static and dynamic performance.

ABSTRACTThis article introduces a method of mistuned parameter identification which consists of static frequency testing of blades, dichotomy and finite element analysis. A lumped parameter model of an engine bladed-disc system is then set up. A bladed arrangement optimization method, namely the genetic particle swarm optimization algorithm, is presented. It consists of a discrete particle swarm optimization and a genetic algorithm. From this, the local and global search ability is introduced. CUDA-based co-evolution particle swarm optimization, using a graphics processing unit, is presented and its performance is analysed. The results show that using optimization results can reduce the amplitude and localization of the forced vibration response of a bladed-disc system, while optimization based on the CUDA framework can improve the computing speed. This method could provide support for engineering applications in terms of effectiveness and efficiency.

The complexity of embedded system design increase as the technology keeps evolving from day to day. Hardware software partitioning has been a promising approach to solve this design problem of complexity in the embedded systems, by providing a solution that automatically decides the partitioning. A lot of research has been done to automate the partitioning which focusing on exact and heuristic algorithm. Then due to the slow performance of the exact algorithms, the study focus shift to heuristic algorithms such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). In this research the performance of both PSO algorithm and GA are analyzed in the application of the partitioning. In order to get the best among these two algorithms, hybrid combination across the two algorithms is designed. The best cost and their average time to achieve it are compared among PSO, GA and hybrid design. As a result, the graph obtained from the hybrid GA-GA-PSO required a smaller number of iterations to reach best cost. Compared to previous work, GA-GA-PSO obtained a smooth as the successive PSO graph. In conclusion, a new idea of hybrid across PSO and GA has been introduced and it results into a better solution for Hardware Software Partitioning.

Particle Swarm Optimization (PSO) is a biologically inspired computational search and optimization method developed in 1995 by Eberhart and Kennedy based on the social behaviors of birds flocking or fish schooling.A number of basic variations have been developed due to improve speed of convergence and quality of solution found by the PSO.On the other hand, basic PSO is more appropriate to process static, simple optimization problem.Modification PSO is developed for solving the basic PSO problem.The observation and review 46 related studies in the period between 2002 and 2010 focusing on function of PSO, advantages and disadvantages of PSO, the basic variant of PSO, Modification of PSO and applications that have implemented using PSO.The application can show which one the modified or variant PSO that haven't been made and which one the modified or variant PSO that will be developed.

The objective of the research work is to examine energy-efficient routing in wireless sensor networks for moving nodes by contrasting new evolutionary algorithms with the Particle Swarm Optimization (PSO) Algorithm. Comparative analysis of energy efficient routing is performed by a novel genetic algorithm where there are several samples (N = 10) and a PSO algorithm where there are several samples (N = 10) techniques with pre-test power of 80% using NS2. When routing wireless sensor networks with moving nodes, Various metrics, including delay, energy use, and packet delivery ratio, are used to compare PSO and genetic algorithms. The evolutionary algorithm outperforms the PSO algorithm in these ways in terms of packet delivery ratio, energy usage, and delay. (p < 0.05) indicates a statistically significant difference. The research's findings comprised the three metrics of delay, energy consumption and packet delivery ratio. The method shows that the novel Genetic algorithm outperforms the PSO algorithm for wireless sensor network energy-efficient routing for moving nodes by identifying a set of routes that can satisfy the delay restrictions.KeywordsNovel genetic algorithmParticle Swarm Optimization (PSO)Wireless sensor networkDelayEnergy consumptionPacket delivery ratio

Particle swarm optimization using dimension selection methods