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This paper presents optimal placement and sizing of multiple FACTS devices based on computational intelligence techniques. In this study, particle swarm optimization (PSO) and Evolutionary Programming (EP) approach is proposed to minimize the transmission loss in the power system with flexible AC transmission systems (FACTS) devices. Two types of FACTS are used: static var compensator (SVC) and thyristors controlled series compensator (TCSC) are two FACTS devices chosen for the optimal installation for compensation purposes due to reported promising performances of the devices. Experiment results on the IEEE 30 bus system with FACTS devices show that the proposed PSO approach can obtain better solutions than EP technique. [ABSTRACT FROM PUBLISHER]

Particle Swarm Optimization (PSO) has proved to be an effective global optimization in recent years. However, PSO still suffers from the prematurity to local optima. In order to solve this disadvantage, researches have carried out by combination with other optimizers. In recent years, a local optimization called Extremal Optimization (EO) has been introduced into PSO and gain improvements. Although, the combination of PSO with EO would bring severe computation overhead result in the simple way they combined. In this paper, an adaptive hybrid combined PSO (AHPSO-EO) is proposed. It can improve the computation effectively by an adaptive way. The experimental results on benchmark functions reveal that the adaptive PSO combined with EO accelerates the convergence and improves the performance of proposed algorithm. [ABSTRACT FROM PUBLISHER]

Spatial clustering is one of the important tasks of data mining, particle swarm optimization(PSO) algorithm has fast convergence and global optimization ability of the search features. In this paper, we present a Particle Swarm Optimization (PSO) clustering algorithm based on similarity, and improve the corresponding fitness function. Contrary to using Euclidean distance, this algorithm performs a more correct search in the entire solution space. Spatial data sources is from IRIS dataset. These data are clustered by clustering analysis based on PSO-similarity algorithm. Experimental results show that PSO — similarity clustering method can achieve fast and accurate spatial data clustering. [ABSTRACT FROM PUBLISHER]

This paper presents the design of a robust fractional order controller for non-minimum phase hydro power plants based on quantitative feedback theory (QFT) using Particle Swarm Optimization (PSO). In this work, a fractional-order compensator, with a flexible controller structure, is introduced into the QFT design to give a better approximation of optimum open loop in Nichols. It has been demonstrated that the fractional order controller can provide smaller high frequency gain than the integer order controller due to its extra tunable parameters. [ABSTRACT FROM PUBLISHER]

Localization is one the key technologies of wireless sensor networks, and the problem of localization is always formulate as an optimization problem. Particle swarm optimization (PSO) is easy to implement and requires moderate computing resources, which is feasible for localization of sensor network. To improve the efficiency and precision of PSO-based localization methods, this paper proposes a distributed PSO-based method. Based on the probabilistic distribution of ranging error, it presents a new objective function to evaluate the fitness of particles. Moreover, it tries to localize as many unknown nodes as possible in a more accurate search space. Simulation results show that the proposed method outperforms previous proposed algorithms. [ABSTRACT FROM PUBLISHER]

This paper investigates a new approach of computation using particle swarm in order to resolve economic environmental dispatch problem. This approach is called accelerated multiobjective particle swarm optimization (AMOPSO) which incorporates vector function as objective function and uses matrix computation and updates solutions set, in each iteration, for developing the Pareto front unlike the existing multi-objective algorithms which use an external archive. We apply this approach to resolve the problem which treats fuel cost, NOx emissions and active power losses as competing objectives. [ABSTRACT FROM PUBLISHER]

Economic usage of energy is a critical issue in wireless sensor network. Network clustering is an efficient technique for minimizing node energy consumption and maximizing network lifetime. One of the major issues of a clustering protocol is selecting an optimal group of sensor nodes as the cluster heads to divide the network. But optimum clustering is an NP-Hard problem and solving it involves searches through vast spaces of possible solutions. Evolutionary algorithms have been applied successfully to a variety of such issue. In this paper, we explore an evolutionary algorithm to optimize the energy consumption, which is particle swarm optimization to find the optimal clusters based on residual energy and transmission distance. The simulation results demonstrate that our protocol considerably increases the network's lifespan, compared with existing clustering protocols. [ABSTRACT FROM PUBLISHER]

This paper presents the use of a bio-inspired method in robotics research. We discuss the Particle swarm optimization (PSO) for two ground robots: an omnidirectional rolling robot and a biped walker robot. For the wheeled robot, we studied the navigation in a flat environment with eventual obstacles. Thus, for the biped robot, we applied on the gesture of the straight walk. The PSO algorithm shows that is very tempting for contribution in the evolutionary robotics researches. [ABSTRACT FROM PUBLISHER]

This paper proposes a swarm intelligence framework for distributed population-based metaheuristics that uses stigmergy and similarity measures as basic modeling rules with a local range of action for structuring the neighborhood. The system — which can be described as a cellular automaton with short-term memory — displays complex and emergent behavior whose most visible trait is the self-organization of a population of particles into dynamic clusters. These clusters tend to gather similar particles (similarity here is measured as the algebraic difference between randomly assigned fitness values). During the execution of the algorithm, the particles move through a grid of nodes leaving a mark with the fitness value of the particle in each node they visit. When deciding where to move, the particles take into account the marks in the neighborhood and tend to travel to nodes with marks that minimize the difference between the particle's fitness and the mark's fitness. A kind of hierarchical behavior is also modeled by forcing the particles to move toward nodes with better fitness values. We show that these simple rules conduct the system to a critical state in which clusters are constantly created and broken, while maintaining a typical pattern of clusters and paths. In addition, we demonstrate that the system's variables display noise, which is one of the signatures of Self-Organized Criticality (SOC). Since it does not require the tuning of control parameters to precise values, we hypothesize that the proposed system converges to SOC. [ABSTRACT FROM PUBLISHER]

Particle swarm optimization (PSO) and genetic algorithms (GAs) are two kinds of widely used evolutionary compution techniques. In this paper, a particle swarm optimizer is implemented and compared to a genetic algorithm for the object localization problem. The problem of object localization can be formulated into an integer nonlinear optimization problem (INOP). We respectively expand the basic PSO and GA to solve the formulated INOP. Experiments were made on a set of 42 test images with complex backgrounds. The results show that although GA and PSO share many common features, PSO is more suitable for the problem than GA. [ABSTRACT FROM PUBLISHER]

The Octagonal Steiner Minimal Tree (OSMT) problem is an NP-hard problem, which is one of the key problems in non-Manhattan routing. Particle Swarm Optimization (PSO) has been proved to be an efficient intelligent algorithm for optimization designs. This paper presents an OSMT algorithm based on discrete PSO (DPSO), namely OSMT_DPSO, to optimize the wire length. In order to solve the problem of the slow convergence rate of PSO used for a high-dimensional space optimization, a self-adapting strategy that can adjust the learning factors, and combine with the crossover and mutation operators of Genetic Algorithm (GA) is proposed. The experimental results show that the proposed algorithm can efficiently provide the solution of OSMT problem with good quality. Moreover, the algorithm can obtain several topologies of OSMTs which is beneficial for optimizing congestion in VLSI global routing stage. [ABSTRACT FROM PUBLISHER]

With limited budget and inflation rate, the materials acquisition in multi-unit libraries has been a challenging issue all over the world. The materials acquisition for multi-unit libraries can be regarded as a generalized version of the knapsack problem, which was known to be NP-hard, with much more constraints. Thus, it can be computationally expensive to solve the problem. In this paper, the materials acquisition problem in multi-unit libraries is formulated as an integer programming model, and two different constraint-handling mechanisms applied in discrete particle swarm optimization algorithm for obtaining the near optimal solution are presented. It is evident from our computational results that one constraint-handling mechanism can solve the problem effectively and efficiently, while the other one takes more time. [ABSTRACT FROM PUBLISHER]

Multi-objective particle swarm optimization has two different points from single objective one. The first point is guide position selection methods for personal best and global best. The second one is the usage of an archive to preserve good positions for Pareto optimal set. In this paper, we consider a guide selection problem in multiobjective particle swarm optimization. A selection method for the personal best that depends on one objective function among plural objective functions is presented. Then, a selection method for the global best that selects among the archived position due to one objective function is presented. The performances of the proposed methods are evaluated by the benchmark problems for the evolutionary multiobjective optimization algorithms. [ABSTRACT FROM PUBLISHER]

This paper introduces a variant of particle swarm optimization algorithm called the drift particle swarm optimization (DPSO), which is inspired by the free electron model in an external electric field at finite temperature. As the compression-expansion coefficient in DPSO is an important parameter which can greatly influence the performance of the algorithm, three types of control strategies are proposed to control this parameter. The performance of these strategies on the DPSO is comprehensively evaluated on eight benchmark functions. From the experimental results and statistical tests, guidelines about selecting the control method for the compression-expansion coefficient are given. [ABSTRACT FROM PUBLISHER]

The cooperation behaviors existing in the animal and human being societies, have been modeled for the numerical optimization, but the local cooperation has not been modeled separately in optimization problems. In this paper the local cooperation is newly modeled as Neighborhood Field Model (NFM). Based on NFM, a new optimization technique called Neighborhood Field Optimization algorithm (NFO) is firstly proposed to deliver global optimization. In NFO, each individual is attracted by its superior neighbor and repulsed by its inferior neighbor to search a better solution. In this paper, NFO is compared with certain algorithms under twelve different benchmark functions. The results show that NFO can outperform them on multimodal functions in the respect of accuracy, effectiveness and robustness. It also can be noted that the cooperation behavior can play a dominant role in the optimization algorithm separately. [ABSTRACT FROM PUBLISHER]

This paper presents a general cooperative coevolution model inspired by the concept and main ideas of the coevolution of symbiotic species in natural ecosystems. A novel approach called “multi-hive artificial bee colony” for constrained multi-objective optimization (MHABC-CMO) is proposed based on this model. A novel information transfer strategy among multiple swarms and division operator are proposed in MHABC-CMO to tie it closer to natural evolution, as well as improve the robustness of the algorithm. Simulation experiment of MHABC-CMO on a set of benchmark test functions are compared with other nature inspired techniques which includes multi-objective artificial bee colony (MOABC), nondominated sorting genetic algorithm II (NSGA II) and multi-objective particle swarm optimization (MOPSO). The numerical results demonstrate MHABC-CMO approach is a powerful search and optimization technique for constrained multi-objective optimization. [ABSTRACT FROM PUBLISHER]

Premature convergence happens in Particle Swarm Optimization (PSO) partially due to improper search information propagation. Fast propagation of search information will lead particles get clustered together quickly. Determining a proper search information propagation mechanism is important in optimization algorithms to balance between exploration and exploitation. In this paper, we attempt to figure out the relationship between search information propagation and the population diversity change. Firstly, we analyze the different characteristics of search information propagation in PSO with four kinds of topologies: star, ring, four clusters, and Von Neumann. Secondly, population diversities of PSO, which include position diversity, velocity diversity, and cognitive diversity, are utilized to monitor particles' search during optimization process. Position diversity, velocity diversity, and cognitive diversity, represent distributions of current solutions, particles' “moving potential”, and particles' “moving target”, respectively. From the observation of population diversities, the effect of search information propagation on PSO's optimization performance is discussed at last. [ABSTRACT FROM PUBLISHER]

Quantum behaved particle swarm optimization (QPSO) is a recently proposed metaheuristic, which describes bird flocking trajectories by a quantum behavior. It uses only one tunable parameter and suggests a new and interesting philosophy for moving in the search space. It has been successfully applied to several problems. In this paper, we investigate the possibility of extending QPSO to handle multiple objectives. More specifically, we address the way global best solutions are recorded within an archive and used to compute the local attractor point of each particle. For this purpose, a two level selection strategy that uses sigma values and crowding distance information has been defined in order to select the suitable guide for each particle. The rational is to help convergence of each particle using sigma values while favoring less crowded regions in the objective space to attain a uniformly spread out Pareto front. The proposed approach has been assessed on test problems for function optimization from convergence and diversity points of view. Very competitive results have been achieved compared to some state of the art algorithms. [ABSTRACT FROM PUBLISHER]

Real-world problems can present search spaces with hundreds of dimensions and swarm intelligence algorithms have been developed to solve this type of problems. Particle Swarm Optimization (PSO) presents a fast convergence in continuous problems, but it can not maintain diversity along the search process. On the other hand, Artificial Bee Colony (ABC) presents the capability to generate diversity when the guide bees are in the exploration mode. We propose in this paper to introduce a mechanism based on the ABC to generate diversity in an adaptive PSO approach and analyze its performance in high dimensional search spaces. The swarm switches its behavior depending on the dispersion of the swarm. We evaluated our proposal in a well known set of 20 benchmark functions recently proposed in 2010 and our proposal achieved better performance than PSO, APSO and ABC in most of the cases. [ABSTRACT FROM PUBLISHER]

This paper presents a discrete artificial bee colony algorithm (DABC) for solving the traveling salesman problem with time windows (TSPTW) in order to minimize the total travel cost of a given tour. TSPTW is a difficult optimization problem arising in both scheduling and logistic applications. The proposed DABC algorithm basically relies on the destruction and construction phases of iterated greedy algorithm to generate neighboring food sources in a framework of ABC algorithm. In addition, it also relies on a classical 1-opt local search algorithm to further enhance the solution quality. The performance of the algorithm was tested on a benchmark set from the literature. Experimental results show that the proposed DABC algorithm is very competitive to or even better than the best performing algorithms from the literature. [ABSTRACT FROM PUBLISHER]

Clustering plays important role in many areas such as medical applications, pattern recognition, image analysis and statistical data analysis. Image clustering is an application of image analysis in order to support high-level description of image content for image understanding where the goal is finding a mapping of the images into clusters. This paper presents an Artificial Bee Colony (ABC) based image clustering method to find clusters of an image where the number of clusters is specified. The proposed method is applied to three benchmark images and the performance of it is analysed by comparing the results of K-means and Particle Swarm Optimization (PSO) algorithms. The comprehensive results demonstrate both analytically and visually that ABC algorithm can be successfully applied to image clustering. [ABSTRACT FROM PUBLISHER]

This paper presents a novel fuzzy particle swarm optimization with cross-mutated operation (FPSOCM), where a fuzzy logic is applied to determine the inertia weight of PSO and the control parameter of the proposed cross-mutated operation based on human knowledge. By introducing the fuzzy system, the value of the inertia weight of PSO becomes adaptive. The new cross-mutated operation effectively drives the solution to escape from local optima. To illustrate the performance of the FPSOCM, a suite of benchmark test functions are employed. Experimental results show the proposed FPSOCMmethod performs better than some existing hybrid PSO methods in terms of solution quality and solution reliability (standard deviation upon many trials). Moreover, an industrial application of economic load dispatch is given to show that the FPSOCM method performs statistically more significant than the existing hybrid PSO methods. [ABSTRACT FROM PUBLISHER]

Trading rules have been utilized in the stock market to make profit for more than a century. However, only using a single trading rule may not be sufficient to predict the stock price trend accurately. Although some complex trading strategies combining various classes of trading rules have been proposed in the literature, they often pick only one rule for each class, which may lose valuable information from other rules in the same class. In this paper, a complex stock trading strategy, namely weight reward strategy (WRS), is proposed. WRS combines the two most popular classes of trading rules-moving average (MA) and trading range break-out (TRB). For both MA and TRB, WRS includes different combinations of the rule parameters to get a universe of 140 component trading rules in all. Each component rule is assigned a start weight and a reward/penalty mechanism based on profit is proposed to update these rules' weights over time. To determine the best parameter values of WRS, we employ an improved time variant Particle Swarm Optimization (PSO) algorithm with the objective of maximizing the annual net profit generated by WRS. The experiments show that our proposed WRS optimized by PSO outperforms the best moving average and trading range break-out rules. [ABSTRACT FROM PUBLISHER]

Airborne mobile sensor network, which is composed of sensor nodes integrated airborne vehicles, draws the attention of many interesting topics in multifarious areas, such as environmental monitoring, battlefield surveillance or disaster management. In this function specific network, some application based rules should be obeyed completely, such as signal jamming, collision avoiding, formation matching and flock centering. Different principles treat the overall arrangement as dissimilar coverage problems fromdifferent perspectives. In this paper, considering the above factors, we give a modeling under some new restrictions, measure each candidate solution and do a multi-decision making process, thus solving coverage requirement. In the end the simulation proves our mechanism. [ABSTRACT FROM PUBLISHER]

This paper proposes a authentic and efficient method for solving optimal power flow (OPF) problem. The proposed approach uses iteration particle swarm optimization (IPSO) algorithm for optimal regulation of control variables of OPF problem. Employing of IPSO has a better performance than conventional PSO in converging to global optimum. It also avoids falling into local optimum and premature convergence, so search behavior is improved. To ensure the effectiveness of proposed method in comparison with previous approaches, the proposed algorithm has been applied to IEEE 30 bus test system with considering different objectives such as minimization of fuel cost plant, improvement of voltage profile and voltage stability intensification. Simulation results confirm the proficiency of the proposed algorithm. [ABSTRACT FROM PUBLISHER]

The Particle Swarm Optimization or PSO is a heuristic based on a population of individuals, in which the candidates for a solution of the problem at hand evolve through a simulation process of a social adaptation simplified model. Combining robustness, efficiency and simplicity, PSO has gained great popularity as many successful applications are reported. The algorithm has proven to have several advantages over other algorithms that based on swarm intelligence principles. The use of PSO solving problems that involve computationally demanding functions often results in low performance. In order to accelerate the process, one can proceed with the parallelization of the algorithm and/or mapping it directly onto hardware. This paper presents a novel, massively parallel PSO co-processor which was implemented by using reconfigurable hardware. The implementation results show that the proposed architecture is very promising as it achieved superior performance in terms of execution time when compared to the direct software execution of the algorithm. [ABSTRACT FROM PUBLISHER]

In order to conserve battery power in wireless sensor networks, some sensor nodes may be put into the sleep state while other sensor nodes remain active for the sensing and communication tasks. However, determining which of the sensor nodes should be put into the sleep state is non-trivial. In this paper, an Energy-balanced Sleep Scheduling scheme based on Particle Swarm Optimization (EBSS-PSO) in the context of cluster-based sensor networks is proposed. The scheme aims to balance the energy load of the sensing and communication tasks among all the nodes in the cluster while provide adequate sensing coverage area and reduce the overlapping area. Analytical and simulation results are presented to evaluate the proposed EBSS-PSO scheme. It is shown that the EBSS-PSO scheme can extends the cluster's overall network lifetime and reduce the overlapping area effectively while maintaining a similar sensing coverage compared with three related sleep scheduling schemes, the Randomized Scheduling (RS) scheme, the Neighbor-based Scheduling(NS) scheme and the Energy-Neighbor-based Scheduling ( ENS) scheme. [ABSTRACT FROM PUBLISHER]

This paper is used to investigate the harmonic filters planning of a power system with three-phase to two-phase specially connected transformers. A particle swarm optimization method with nonlinear time-varying evolution (PSO-NTVE) is employed in the planning of large-scale passive harmonic filters. The objective is to minimize the cost of the filter, the filters loss, the total harmonic distortion of currents and voltages at each bus simultaneously. The reactive power compensation and constraints of individual harmonics are also considered. Three design cases are compared to demonstrate the design results. The study results shows that the scheme type of three-phase to two-phase specially connected transformers have significant effects on the harmonic distribution. The phase B of VV connection scheme has reduced 3rd order harmonic distortion, the Scott and Le Blanc connection scheme batter than VV connection scheme for improved harmonic distortion, and the many typical passive filters structure batter than one typical filter. From the results of the illustrative examples, the feasibility of the PSO-NTVE to design an optimal passive harmonic filter is verified. [ABSTRACT FROM PUBLISHER]

A real-time parallel application can be divided into a number of tasks and executed concurrently on distinct nodes of a Distributed System (DS). Distributed System Reliability (DSR) can be defined as the probability that all the tasks in the system run successfully. Due to different hazard rates of nodes and links, DSR critically depends on the optimal allocation of these tasks onto the available nodes. In this paper, we have presented a mathematical model for analyzing DSR in a DS on which hard real-time periodic tasks are executed. In addition, to maximize reliability besides satisfying the constraints, we have proposed an offline task allocation algorithm. The algorithm is a new swarm intelligence approach based on Ant Colony Optimization (ACO). For evaluating the algorithm, ACO is compared with Honey Bee Mating Optimization (HBMO) and Particle Swarm Optimization (PSO). Simulation results manifest that ACO produces better solutions than PSO and HBMO. Meanwhile, it leads to shorter execution time. The results also reveal the flexibility and scalability of the proposed algorithm. [ABSTRACT FROM PUBLISHER]

This paper presents the simulation and construction of a 9-level Flying Capacitor Multilevel Inverter (FCMI), with a control based on the Fundamental Frequency Switching Method (FFSM) and redundant switching states. The system was built and the hardware implementation process is documented. A Particle Swarm Optimization (PSO) algorithm was developed to determine the MOSFETs firing angles to reduce the resulting Total Harmonic Distortion (THD). MATLAB/Simulink was used to simulate the FCMI and implement the PSO algorithm. A microcontroller was used to generate the sixteen different signals to control the firing angles for the hardware implementation. Simulation and experimental results confirmed the proper function of the FFSM control and capacitor balancing. [ABSTRACT FROM PUBLISHER]

Ever process scheduling in single and multi processors systems is one of the most focused research problem. In this paper we propose an approach for process scheduling based on back tracking technique. This approach considered TFT (Total Finish Time) as a main parameter. We limited load of each processor that not greater than ideal measure. Proposed approach always is resulted to optimized solution. Simulation shows that its results are better than LPT (Longest processing time), SPT (Shortest processing time) and PSO (Particle swarm optimization) algorithms. [ABSTRACT FROM PUBLISHER]

This paper deals with a new scheme for the automatic diagnosis of multiphase inter-turn short circuit faults in a non-salient permanent magnet synchronous motor (PMSM) stator windings. Base on a general dynamic model of a non-salient PMSM subjected to the multiphase inter-turn short circuit fault, a particle swarm optimization (PSO) algorithm is developed which can provide a diagnosis of the fault position and level by identifying the amount of the shorted turns for each phase. The advantages of the proposed algorithm, is the capability of single, multiple and simultaneous phase ITSC fault diagnosis. The proposed method is simulated in MATLAB/ Simulink environment. Extensive simulation studies provide preliminary verification of the fault diagnosis scheme. [ABSTRACT FROM PUBLISHER]

In cloud computing environments, users can rent virtual machines (VMs) from cloud providers to execute their programs or provide network services. While using this kind of cloud services, one of the biggest problems for the users is to determine the proper number of VMs to complete the jobs considering both budget and time. In this paper, we propose a resource prediction framework (RPF), which can help users choose the minimum number of virtual machines to complete their jobs within a user specified time constraint. In order to verify the feasibility of RPF, we have done three case studies, namely parallel frequent pattern growth (FP-Growth), parallel K-means, and Particle Swarm Optimization (PSO). FP-growth, K-means and PSO are data intensive algorithms. These algorithms are typically executed repeatedly with different execution parameters to find the optimal results. When evaluating RPF by these algorithms in cloud environments, we have to modify them to parallel versions. The evaluation results indicate that RPF can successfully obtain the minimum number of VMs with acceptable errors. According to our case studies, the proposed RPF can be adopted by data intensive jobs by providing flexibility to both end users and cloud system providers. [ABSTRACT FROM PUBLISHER]

In this paper, a hybrid particle swarm optimization algorithm (HPSO) is proposed for the DNA fragment assembly (DFA) problem by maximizing the overlapping-score measurement. The smallest position value (SPV) rule is used for encoding the particles to enable PSO to be suitable for DFA, and the Tabu search algorithms are used to initialize the particles. Additionally, a simulated annealing (SA) algorithm-based local search is utilized for local search to improve the best solution after the PSO search process. Finally, the results show that HPSO can significantly get better overlap score than other PSO-based algorithms with different-sized benchmarks. [ABSTRACT FROM PUBLISHER]

This paper proposes a bio-inspired particle swarm optimization algorithm that incorporates random walk for local search techniques in the non-dominated sorting Particle Swarm Optimization (PSO) algorithm in addition to the mechanism of crowding distance, resulting in an efficient and effective optimization method. The proposed algorithm was implemented and evaluated using different benchmark test problems including unconstrained and constrained problems. The obtained results were compared with published ones. The results showed that the proposed bio-inspired algorithm generates a precise well distributed set of non-dominated solutions justifying the superiority of the random walk method. [ABSTRACT FROM PUBLISHER]

In this paper, we introduce a hybrid approach of particle swarm optimization (PSO) for solving n-queens problem. Since the solution of n-queens problem is to create a set of non-conflict queens, we propose a swarm refinement technique to reduce conflicts in main swam and then locate final solutions by swapping permutations. The swarm refinement PSO (SR-PSO) performs much faster and more accurate than the original discrete PSO. [ABSTRACT FROM PUBLISHER]

In this paper, A Modified Differential Algorithm (MDE) developed to solve unconstrained problems. The MDE algorithm use pitch adjustment replaces the traditional mutation operation and design a dynamic method to adjust the crossover rate (CR). The former can enhance the convergence of the MDE, and the latter can effectively prevent the MDE from being trapped into the local optimum. Based on several benchmark experiments, the MDE has demonstrated stronger convergence and stability than original differential (DE) algorithm and its two improved algorithms (JADE and SaDE) [ABSTRACT FROM PUBLISHER]

Particle Swarm Optimization has been applied in many optimization problems. In this paper, we proposed a new algorithm based on PSO and applied to optimal the weighted fuzzy neural network. It takes some measures to avoid the algorithm into a local convergence and determine the structure of the fuzzy neural network based on the effective rules. Satisfactory results through experiments are obtained. [ABSTRACT FROM PUBLISHER]

Clustering has been the most popular method for data exploration. Clustering is partitioning the data set into sub-partitions based on some measures say the distance measure, each partition has its own significant information. There are a number of algorithms explored for this purpose, one such algorithm is the Particle Swarm Optimization(PSO) which is a population based heuristic search technique derived from swarm intelligence. in this paper we present an improved version of the Particle Swarm Optimization where, each feature of the data set is given significance accordingly by adding some random weights, which also minimizes the distortions in the dataset if any. the performance of the above proposed algorithm is evaluated using some benchmark datasets from Machine Learning Repository. the experimental results shows that our proposed methodology performs significantly better than the previously performed experiments. [ABSTRACT FROM PUBLISHER]

Aiming at shortcomings in existing scheduling methods for batch processing workflow, this paper attempt to investigate and solve the optimization problem for grouping and scheduling multiple activity instances in batch processing workflow. A multiple objective optimal model of problem with constraints is presented firstly. Then, a discrete particle swarm optimization algorithm is proposed to produce a set of optimal Pareto solutions by optimizing the two objective functions simultaneously. The result of simulation experiment shows the effectiveness of this algorithm. [ABSTRACT FROM PUBLISHER]

This paper presents a new approach in order to determine the optimal placement and size of capacitors in TABRIZ radial distribution system to minimize investment cost, energy loss reduction, improvement of voltage profile. Capacitor optimal placement & sizing are done by determination of loss sensitivity factors and PSO. Loss sensitivity factors can be introduced as an important parameter for sequencing of effective nodes in loss reduction. In this paper, combination of this method with PSO algorithm is used as a novel approach for optimal capacitor placement. This method is tested on a 10 buses distribution system and applied on the all feeders of ROSHANAEI and GOLESTAN regions of TABRIZ Distribution Company. The findings clearly demonstrate the advantage of proposed method comparing with fuzzy-reasoning. Finally, investment return time, loss reduction percentage and capacity release of feeders have economically justified the practical usage of this method. [ABSTRACT FROM PUBLISHER]

This paper presents an intelligent method for gear fault diagnosis based on wavelet packet analysis and support vector machine (SVM). For this purpose, two experiments were selected to verify the proposed method. One is a spur gear of the motorcycle gearbox system. Slight-worn, medium-worn, and broken-tooth were selected as the faults. In fault simulating, two very similar models of worn gear have been considered with partial difference for evaluating the preciseness of the proposed method. The other one is a helical gear of a gearbox system. Broken-tooth and crack in root of gear were selected as the faults. Raw vibration signals were segmented into the signals recorded during one complete revolution of the input shaft using tachometer information and then synchronized using cubic spline interpolation to construct the sample signals with the same length. Next, standard deviations of wavelet packet coefficients of the vibration signals which have been normalized and dimension deducted using principal component analysis (PCA) were considered as the feature vector for training purposes of the SVM. The parameters of SVM are optimized using particle swarm optimization (PSO). Its effectiveness is verified by experimental results. [ABSTRACT FROM PUBLISHER]

The huge increase of hyperspectral data dimensionality and information redundancy has brought high computational cost as well as the over-fitting risk of classification. In this paper, we present an automatic band selection and classification method based on a novel wrapper Multiple Improved particle swarm cooperative optimization and support vector machine model (MIPSO-SVM). The MIPSO-SVM model optimizes both the band subset and SVM kernel parameters simultaneously. In the proposed model, the particle swarm is divided into two sub-swarms. And PSO is improved firstly, by the new update strategy of position and velocity. Then the sub-swarms perform the improved PSO (IPSO) for band selection and classifier parameters optimization independently. Finally, in the process of cooperative evolution, extremal optimization (EO) is incorporated to maintain the diversity of swarms and enhance the space exploration ability of the proposed model. Experimental results demonstrate the effectiveness of the proposed method for band selection and classification of hyperspectral images. [ABSTRACT FROM PUBLISHER]

The paper proposes a novel endoscope motion estimation method that bases an aggressive particle filter (APF) for enhancing electromagnetic tracking (EMT) during guided endoscopy. We explore an APF strategy to resolve two main limitations of EMT sensor measurements: (1) inaccuracy due to airway deformation and (2) instability or jitter errors because of magnetic field distortion. During such a strategy, a swarm intelligent technique — particle swarm optimization that was modified to traceably determine evolutionary parameters in this paper was embedded into a generic particle filter (GPF) to address particle impoverishment that usually occurs in GPF. Experimental results from validating our method on a dynamic bronchial phantom demonstrate our method's effectiveness and robustness during EMT-guided endoscopy. The tracking pose error was significantly reduced from (4.3, 7.8) to (2.7 mm, 5.6°). [ABSTRACT FROM PUBLISHER]

This paper presents the automatic loop shaping design of robust fractional order controllers for non-minimum phase or unstable plants with only one unstable pole based on quantitative feedback theory (QFT). Fractional-order controllers, with a flexible controller structure, are introduced into the QFT design to give a better approximation of optimum open loop in Nichols. The fractional order controller is designed for not only minimum phase system but also uncertain non-minimum phase and unstable plants in QFT. It has been demonstrated that the fractional order controller can provide smaller high frequency gain than the integer order controller due to its extra tunable parameters. [ABSTRACT FROM PUBLISHER]

Smart Grid realization has been an ultimate goal for the power research community. The goal makes it necessary to incorporate the advanced algorithm and new technologies to make the power system more robust and secure. In order to reduce system-wide blackouts, high order contingency has to be analyzed and extensive work has been performed on N-k contingency studies. This paper proposes to use the Artificial Intelligence algorithm Particle Swarm Optimization (PSO) in solving cascading power outage failure problems, identifying potential risk, quantifying fitness results, and providing important guidance for system operators and business owners to make informed decisions. [ABSTRACT FROM PUBLISHER]

This paper presents a novel algorithm, which is based on Gravitational Search Algorithm (GSA), for multiobjective optimization problems. The proposed algorithm, which is called Vector Evaluated Gravitational Search Algorithm (VEGSA), uses a number of populations of particles. In particular, a population of particles corresponds to one objective function to be minimized or maximized. Simultaneous minimization or maximization of every objective function is realized by exchanging a variable between populations. Two versions of VEGSA algorithm are presented in this study. Convex and non-convex test functions on biobjective optimization problems are used to evaluate the effectiveness of the proposed VEGSA. [ABSTRACT FROM PUBLISHER]

When using the standard particle swarm optimization to optimize the complex problems with high dimension, low convergence efficiency and falling into local optimization usually occur because of its inherent disadvantages. To avoid these disadvantages, a novel hybrid particle swarm optimization improved by mutative scale chaos method is proposed in this paper. This hybrid algorithm combines global high-speed convergence ability of particle swarm optimization with chaos method's advantage, i.e., breaking away from local optimal points easily. The variance of the population's fitness is used to judge premature state of the whole population. The searching space of chaos method can be reduced dynamically by mutative scale scheme, and then searching efficiency of the proposed algorithm is improved further. The test results for benchmark functions show that this novel hybrid algorithm not only surpasses the standard particle swarm optimization obviously in many respects, such as optimization precision, efficiency, success ratio and so on, but also has good stability and low sensitivity to different dimensions of functions. [ABSTRACT FROM PUBLISHER]

The artificial bee colony (ABC) algorithm is a swarm intelligence optimization algorithm inspired by the intelligent foraging behavior of honeybees. ABC algorithm gets the new solution by searching the neighborhood of the current solution in the search process and the scope searched is small, which leads to slow convergence and easily gets stuck to the local optimal solution. In this paper, an improved ABC algorithm is proposed based on multi-exchange neighborhood (MNABC) by exchanging neighborhood in the search process. The simulation experiment comparing MNABC with the basic ABC and PSO algorithms, shows that the proposed method can improve the convergence speed and global searching capability of ABC algorithm. [ABSTRACT FROM PUBLISHER]

In this paper, we present a new multimodal optimization algorithm, role based particle swarm optimization (RPSO), for finding and maintaining multiple optima in objective function landscape. Instead of generating all trial vectors randomly, the swarm population is divided into three kinds of roles, each part of swarms generating offsprings with different strategy. A species conservation procedure is employed during the optimization process to save the newly found peaks. Numerical experiments are performed to compare the proposed method with canonical species conservation GA on a series of benchmark functions. Based on the results, we conclude that the proposed technique is comparatively effective on selected benchmark functions in terms of locating and maintaining the multiple optima. [ABSTRACT FROM PUBLISHER]