A New Binary Coding Particle Swarm Optimization for Feeder Reconfiguration

Network reconfiguration for loss reduction in a distribution system is a very important way to save the electrical energy. This article proposes a hybrid evolutionary algorithm to solve the distribution feeder reconfiguration problem. The algorithm combines a fuzzy adaptive particle swarm optimization with a differential evolution algorithm, called the fuzzy adaptive particle swarm optimization–differential evolution. The fuzzy adaptive particle swarm optimization includes two parts. The first part is fuzzy adaptive binary particle swarm optimization, which determines the status of tie switches (open or closed), and the second part is fuzzy adaptive discrete particle swarm optimization, which determines the sectionalizing switch number. In the proposed algorithm, due to the results of binary particle swarm optimization and discrete particle swarm optimization algorithms that highly depend on the values of their parameters (such as the inertia weight and learning factors), a fuzzy system is employed to adaptively adjust the parameters during the search process. The differential evolution algorithm is combined with the fuzzy adaptive particle swarm optimization algorithm to improve its performance. The proposed algorithm is tested on two distribution test feeders. The results of simulation show that the proposed method is very powerful and is guaranteed to obtain the global optimization.

The Kyoto protocol recommended that industrialized countries limit their green gas emissions in 2012 to 5.2% below 1990 levels. Photovoltaic (PV) arrays provide clear and sustainable renewable energy to electric power systems. Solar PV arrays can be installed in distribution systems of rural and urban areas, as opposed to wind-turbine generators, which cause noise in surrounding environments. However, a large PV array (several MW) may incur several operation problems, for example, low power quality and reverse power. This work presents a novel method to reconfigure the distribution feeders in order to prevent the injection of reverse power into a substation connected to the transmission level. Moreover, a two-stage algorithm is developed, in which the uncertain bus loads and PV powers are clustered by fuzzy-c-means to gain representative scenarios; optimal reconfiguration is then achieved by a novel mean-variance-based particle swarm optimization. The system loss is minimized while the operational constraints, including reverse power and voltage variation, are satisfied due to the optimal feeder reconfiguration. Simulation results obtained from a 70-bus distribution system with 4 large PV arrays validate the proposed method.

Optimum PID controller design using real coded genetic algorithm (RGA) and particle swarm optimization (PSO) is addressed in this paper. The lower and upper bound of the design variables (KP, TI and TD) are selected in an intelligent manner around the values obtained from Ziegler-Nichols method. PID controller was designed by minimizing various performance measures such as ISE, IAE and ITAE for two different linear systems. The performance of RGA and PSO is compared with respect to time-response specifications, computation time and statistical performance in 20 independent trials. The simulation reveals that the performance of PSO and RGA with simulated binary crossover (SBX) produced almost same time-domain performances and take same computation time. Also, it is found that PSO converges with less number of functional evaluations, consistent for simple systems and in general ITAE is preferable for quick settling time.

Population topology of particle swarm optimization (PSO) will directly affect the dissemination of optimal information during the evolutionary process and will have a significant impact on the performance of PSO. Classic static population topologies are usually used in PSO, such as fully connected topology, ring topology, star topology, and square topology. In this paper, the performance of PSO with the proposed random topologies is analyzed, and the relationship between population topology and the performance of PSO is also explored from the perspective of graph theory characteristics in population topologies. Further, in a relatively new PSO variant which named logistic dynamic particle optimization, an extensive simulation study is presented to discuss the effectiveness of the random topology and the design strategies of population topology. Finally, the experimental data are analyzed and discussed. And about the design and use of population topology on PSO, some useful conclusions are proposed which can provide a basis for further discussion and research.

Coordinated controller tuning of the boiler turbine unit is a challenging task due to the nonlinear and coupling characteristics of the system. In this paper, a new variant of binary particle swarm optimization (PSO) algorithm, called probability based binary PSO (PBPSO), is presented to tune the parameters of a coordinated controller. The simulation results show that PBPSO can effectively optimize the control parameters and achieves better control performance than those based on standard discrete binary PSO, modified binary PSO, and standard continuous PSO.

Fuzzy systems (FSs) have been extensively applied to automatic control, pattern recognition, and decision analysis. However, a common bottleneck is encountered in the derivation of fuzzy rules, which is often difficult, time consuming, and relies on expert knowledge. To automate the design of FSs, many metaheuristic learning algorithms have been proposed. One major optimization category uses Swarm Intelligence (SI) model (Kennedy et al., 2001). The SI technique studies collective behavior in decentralized systems. Its development was based on mimicking the social behavior of animals or insects in an effort to find the optima in the problem space. SI models are initialized with a population of random solutions. One well-known SI model is particle swarm optimization (PSO) (Kennedy & Eberhart, 1995). Many modified PSO models have been proposed and successfully applied to different optimization problems (Clerc & Kennedy, 2002; Bergh & Engelbrecht, 2004; Ratnaweera et al., 2004; Juang, 2004; Kennedy & Mendes, 2006; Parrott & Li, 2006; Chen & Li, 2007). FS design using PSO has also been proposed in several studies (Juang, 2004; Chatterjee et al., 2005; Juang et al., 2007; Araujo & Coelho, 2008; Sharma et al., 2009). Another well-known SI is ant colony optimization (ACO) (Dorigo & Stutzle, 2004). The ACO technique is inspired by real ant colony observations. It is a multi-agent approach that was originally proposed to solve difficult discrete combinatorial optimization problems, such as the traveling salesman problem (TSP) (Dorigo et al., 1996; Dorigo & Gambardella, 1997). In the original ACO meta-heuristic, artificial ant colonies cooperate to find good solutions for difficult discrete optimization problems. Different ACO models have been applied to FS design problems (Cassillas et al., 2000; Cassillas et al., 2005; Mucientes & Casillas; 2007; Juang & Lo, 2007; Juang et al., 2008; Juang & Lu; 2009). In (Cassillas et al.,2000; Mucientes & Casillas; 2007; Juang et al., 2008; Juang & Lu; 2009), the FS input space was partitioned in grid type with antecedent part parameters of an FS manually assigned in advance. In (Juang & Lo, 2007), the FS input space was flexibly partitioned using a fuzzy clustering-like algorithm in order to reduce the total number of rules. For all of these studies, the consequent part parameters were optimized in discrete space using ACO. Since only the consequent part parameters are optimized, and the optimization space is restricted to be discrete, the designed FSs are unsuitable for problems where high accuracy is a major concern. Source: Fuzzy Systems, Book edited by: Ahmad Taher Azar, ISBN 978-953-7619-92-3, pp. 216, February 2010, INTECH, Croatia, downloaded from SCIYO.COM

Particle swarm optimization (PSO) is a stochastic population-based algorithm which was originally introduced by Kennedy and Eberhart [1]. This optimization algorithm is motivated by intelligent collective behavior of some animals such as flocks of birds or schools of fish. As in most of the metaheuristic optimization algorithms, in PSO, a population of individuals, known as particles, are evolved through successive iterations. The most important advantages of PSO, compared to other optimization strategies, are its easy implementation and few parameters requiring adjustment. Since the initial development of PSO by Kennedy and Eberhart, several variants of this algorithm have been proposed by researchers. The first modification was introducing an inertia weight parameter in the velocity update equation of the initial PSO-a PSO model which is now accepted as the global best PSO algorithm [2]. The goal of the inertia weight parameter is to balance the exploration and exploitation characteristics of PSO. Generally, large inertia weight values are expected to increase the velocity of the particles and improve the long-range exploration of the PSO algorithm, while low inertia values increase the short-range exploration. Due to the strong effect of the inertia weight on the performance of PSO, many researchers have investigated different inertia weight control approaches during the past decades, and many different strategies have been proposed. Various inertia weighting strategies can be categorized into three main classes (Figure 7.1): (1) constant or random, (2) time-varying, and (3) adaptive inertia weights. The first class contains strategies in which the value of the inertia weight is constant during the search or is determined randomly. In the second class, the inertia weight is defined as a function of time (iteration number), and hence these strategies are referred to as time-varying inertia weight strategies. These methods are not considered adaptive since they do not monitor the situation of the particles in the search space, and the value of the inertia weight in each iteration is known before the execution of the algorithm. The third class of the inertia weight strategies consists of those methods which use a feedback parameter to monitor the state of the algorithm and adjust the value of the inertia weight based on the feedback parameter's value. This chapter presents the major existing inertia weight control strategies in the above three categories and discusses examples of each category in detail. The remainder of this chapter is organized as follows. The next section gives a short review of the PSO algorithm, and the role ofthe inertia weight parameter in the velocity update equation. Sections 7.2-7.4 review the three groups of inertia weight strategies, constant or random, time-varying, and adaptive models. Section 7.5 reports some experimental evaluations of different inertia weight models. Finally, Section 7.6 concludes this chapter. A short description of the symbols frequently used in this chapter is shown in Table 7.1.

Improved particle swarm optimization algorithm with harmony search (IHPSO) is proposed in this paper. This algorithm takes particle swarm search direction estimation mechanism and harmony search (HS) approach to particle swarm optimization (PSO) algorithm, which increases the search capability of PSO algorithm considerably. The proposed algorithm initializes a new search with harmony pitch adjusting or random selection when PSO search direction is estimated incorrectly. This can provide further opportunities of finding better solutions for the particle swarm by guiding the entire particle swarm to promising new regions of the search space and accelerating the search. PSO, HPSO and IHPSO, as well as other advanced PSO procedures from the literature were compared on several benchmark test functions extensively. Statistical analyses of the experimental results indicate that the performance of IHPSO is better than the performance of PSO and HPSO.

In this paper, a new method to deal with equality or inequality constraints in constrained optimization and a new neighborhood structure for Particle Swarm Optimization (PSO) called Grouped Directed Structure (GDS) are proposed. We use the new method and GDS together with the PSO algorithm to solve the well-known 24 benchmark constrained optimization problems in the literature. The PSO algorithm is well known for its fast convergence to the possible optimal position. However, in constrained optimization, the performance of PSO is not as good as it is in unconstrained optimization, partly because PSO is not good at finding the feasible region. Due to the motivation by this weakness of PSO, we develop a method called Numerical Gradient (NG) to find the feasible region. By means of the information that NG can provide, we utilize the PSO algorithm with GDS to find the optimal position of the problem. We call this new PSO variant Numerical Gradient Particle Swarm Optimization (NGPSO). A detailed description of the mechanism for NGPSO is provided and some numerical results are presented compared with the results from the existing PSO variants dealing with constraint optimization problems.

Feeder reconfiguration is one of the most important method to optimize the distribution system operation. The efficiency of finding the solution by optimization is the key point for solving feeder recontiguration problem. However, different optimization methods have its own Pros and Cons. Such as, the local search ability of the discrete particle swarm optimization is weak and the global search performance of the ant colony system is also not strong. Furthermore, it is difficult to avoid their disadvantages only by modifying the rules. In this paper, a hybrid optimization method based on ant colony system and particle swarm optimization method is proposed. The proposed method applies a novel combination theory to combine the ant colony system and the discrete particle swarm optimization together rather than use them separately in different stages. The selection rules in the proposed method not only based on the pheromone trails like ants but also consider the velocity and position such as particles. The conclusions of the tests are verified the good performance of the hybrid optimization method.

Particle swarm optimisation (PSO) is an intelligent optimisation algorithm with different variants that are developed according to the targeted application. These variants are obtained by changing the parameters of the PSO algorithm like inertia weight, constriction factor, and acceleration factor. These variants can be utilised for various power system optimisation requirements such as distribution automation, for a targeted application. Distribution network reconfiguration (DNR) is one such application that involves high computational complexity and requires an intelligent optimisation technique for its solution. In this paper different variants of PSO are applied to DNR problem like binary PSO, crazy PSO, and enhanced inter coded PSO for 16 bus and 33 bus test systems. These variants ensure proper exploration and exploitation of the search space to obtain optimum and quick solution for the DNR problem.

Evolutionary and natural computing techniques have been drawn considerable interest for analyzing large datasets with large number of features. Various flavors of Particle Swarm Optimization (PSO) have been applied in the various research applications like Control and Automation, Function Optimization, Dimensionality Reduction, classification. In the present work, we have applied the SVM based classifier along with Novel PSO and Binary PSO on Huesken dataset of siRNA features as well as on nine other benchmark dataset and achieved results are quite satisfactory. The results of our study have been compared with other results available in the literature.

It is our great pleasure to welcome you to BADS 2011, the third Workshop on Bio-Inspired and Self-* Algorithms for Distributed Systems, co-located with ICAC 2011, the 8th IEEE/ACM International Conferenceon Autonomic Computing, Karlsruhe, Germany, June 14-18, 2011. BADS 2011 aims to provide a forum for exploring bio-inspired and self-* algorithms to build autonomic distributed systems; i.e., self-managing distributed systems that can organize, configure, optimize, scale, protect and/or heal themselves with minimal human intervention. Computing inspired by biology has a long history since 1950s. Its common objectives are to formulate theoretical models that are faithfully designed after biological principles, phenomena and processes and to exploit the models for developing computational tools (e.g., algorithms) and systems. Well-known bio-inspired algorithms include artificial neural networks, evolutionary algorithms, swarm intelligence, artificial immune systems, reaction-diffusion algorithms, epidemic algorithms, cellular automata algorithms, gene expression/regulatory algorithms and Lindenmayer system. They have been successfully used as alternative and/or superior solutions to the problems that traditional algorithms cannot solve satisfactorily. In recognition of their achievements and potential, bio-inspired algorithms were named one of Scientific American magazine's 10 "World Changing Ideas 2010." The BADS workshop series focuses on distributed computing and examines the opportunities, current state-of-the-art, challenges and future outlook to create autonomic distributed systems with bio-inspired algorithms. We cover a broad range of aspects in distributed computing, including communication protocols, resource clustering, allocation and management, topology management, parallelism and concurrency, overlay construction and maintenance, decentralized information search and delivery, load distribution, synchronization, service selection, composition and deployment, labor division and task allocation, anomaly and misbehavior detection, malware propagation and detection. Following the success of its former editions in 2009 and 2010, BADS 2011 continues to focus on bio-inspired, autonomic distributed systems. In addition, it expands its themes to cover a general class of algorithms that exhibit self-* properties (e.g., self-configuring, self-organizing, self-managing, self-healing, self-scaling and selfadaptive properties) in the context of distributed computing. BADS 2011 has seven research papers selected through a rigorous review process. Each paper was reviewed and judged by at least three PC members on merits including correctness, originality, technical strength, quality of presentation and relevance to the workshop themes. We would like to sincerely thank the Program Committee members who have greatly contributed to the success of BADS 2011. BADS 2011 is organized in two sessions and one keynote presentation. The first session includes papers that present bio-inspired approaches for distributed computing. The second session focuses on solving optimization problems with distributed bio-inspired and self-* algorithms. The keynote presentation, entitled "Methods for Self-organizing Distributed Software", is given by Elisabetta Di Nitto, and focuses on the applications of self-organization in distributed systems. In nature, self-organization occurs such that a global behavior emerges from simple and local decisions made autonomously by each element of the system. This keynote overviews the benefits and challenges to apply the notion of self-organization for the development of distributed systems. It also presents several example applications such as energy efficiency and configuration optimization in cloud computing. The first session, "Bio-inspired Approaches for Distributed Computing", comprises four papers. The first paper, "Memetic Algorithm for Web Service Selection" by Ludwig, studies the service selection process, whose aim is to locate services of interest in service-oriented environments. The author proposes two approaches based on a genetic algorithm and a memetic algorithm to match consumers with services based on Quality of Service attributes. Both approaches are compared with an optimal assignment algorithm, called the Munkres algorithm, as well as with random approaches. The next paper, "OZMOS: Bio-Inspired Load Balancing in a Chord-based P2P Grid" by Brocco, proposes a load balancing mechanism, called Ozmos, that follows the principle of osmosis to relocate tasks between nodes in a P2P-based Grid. Bio-inspired agents are deployed to share information about the status of the Grid as well as to reschedule tasks between nodes. The efficacy of the proposed algorithm is illustrated in achieving system-wide load balance in Grids of different scales, with both homogenous and heterogeneous resources. The third paper, "Model-Driven Performance Engineering for Wireless Sensor Networks with Feature Modeling and Event Calculus" by Boonma and Suzuki, proposes an application development framework for wireless sensor networks. The proposed framework allows developers to graphically configure their applications and generate application code. With event calculus, the framework also estimates each application's performance such as per-node power consumption and network lifetime without running it on simulators and real networks. The proposed framework currently targets a shortest-path routing; however, it is a promising next step to estimate the performance of bio-inspired routing protocols such as pheromone-based chemotaxis routing. The last paper of the first session, "Description and Composition of Bio-Inspired Design Patterns: the Gradient Case" by Fernandez-Marquez, Arcos, Di Marzo Serugendo, Viroli and Montagna, investigates a group of bioinspired mechanisms in terms of design patterns. The authors show the relations between the different patterns and identify the boundaries of each mechanism. The mechanisms are organized into different levels. In basic level, Spreading, Aggregation and Evaporation are studied. Gradient is investigated in mid-level, and top-level mechanisms are Chemotaxis, Morphogenesis, and Quorum sensing. The second session, "Solving Optimization Problems with Distributed Evolutionary Algorithms", opens with a paper entitled "Protein Structure Prediction Using Particle Swarm Optimization and a Distributed Parallel Approach" by Kondov and Berlich. It demonstrates the efficiency of the standard Particle Swarm Optimization (PSO) algorithm to find the folded state of two proteins of different sizes starting from completely extended conformations. They illustrate that the predicted structure of the larger protein is in good agreement with the structure from the Protein Data Bank within the experimental resolution. The parallelization of PSO is shown to speed up the simulation linearly with the number of workers and it reduces the prediction time dramatically without loss of accuracy. The next paper, "Discrete Optimization Problem Solving with three Variants of Hybrid Binary Particle Swarm Optimization" by Singh V., Singh D., Tiwari and Shukla, presents three variants of the binary PSO algorithm in order to solve discrete optimization problems. This is achieved by introducing an additional step of crossover that varies in all the three variants. These hybrid algorithms show competitive results compared to other state-of-theart metaheuristics. The third paper, "Self-organized Invasive Parallel Optimization" by Mostaghim, Pfeiffer and Schmeck, proposes a new parallelization framework for optimization algorithms. In this approach, the self-organized resources are represented as a unified resource to the user who only specifies the optimization problem and his/her preferences to the system. The invasive approach starts with one resource and automatically divides the optimization task stepwise into smaller tasks, which are assigned to more resources. The job assignment pattern is decided on demand, i.e., the number of required resources is estimated during the optimization process. The authors examine their generic framework on multi-objective problems.

Particle swarm optimisation (PSO) was developed by Kennedy and Eberhart in 1995 (Kennedy & Eberhart, 1995) inspired by the collective behaviour of natural birds or fish. PSO is a stochastic optimisation technique that uses a behaviour of population composed by many search points called particle. In spite of easy implementation in computer algorithms, it is well known as a powerful numerical optimizer. In the typical PSO algorithms, a set of particles searches the optimal solution in the problem space efficiently, by sharing the common attractor called global best. There are many modified versions of PSO by improving convergence property to a certain problem. While, a standard PSO is defined by Bratton and Kennedy (Bratton & Kennedy, 2007) to give a real standard for PSO studies. PSO seems as one of the evolutionary computations (ECs), and it has been shown that PSO is comparable to a genetic algorithm (Angeline, 1998). Thus, a lot of studies have demonstrated the effectiveness of PSO family in optimizing various continuous and discrete optimization problems. And a plenty of applications of PSO, such as the neural network training, PID controller tuning, electric system optimisation have been studied and achieved well results (Kennedy, 1997). However, PSO is often failed in searching the global optimal solution in the case of the objective function has a large number of dimensions. The reason of this phenomenon is not only existence of the local optimal solutions, the velocities of the particles sometimes lapsed into the degeneracy, so that the successive range is restricted in the sub-plain of the whole search hyper-plain. The sub-plane that is defined by finite number of particle velocities is a partial space in the whole search space. The issue of local optima in PSO has been studied and proposed several modifications on the basic particle driven equation (Parsopoulos et al., 2001; Hendtlass, 2005; Liang et al., 2006). There used a kind of adaptation technique or randomized method (e.g. mutation in evolutionary computations) to keep particles velocities or to accelerate them. Although such improvements work well and have ability to avoid fall in the local optima, the problem of early convergence by the degeneracy of some dimensions is still remaining, even if there are no local optima. Hence the PSO algorithm does not always work well for the high-dimensional function.

Feeder reconfiguration (FRC) is an important function of distribution automation system. It modifies the topology of distribution network through changing the open/close statuses of tie switches and sectionalizing switches. The change of topology redirects the power flow within the distribution network, in order to obtain a better performance of the system. Various methods have been explored to solve FRC problems. This paper presents a literature survey on distribution system FRC. Among many aspects to be reviewed for a comprehensive study, this paper focuses on FRC objectives and solution methods. The problem definition of FRC is first discussed, the objectives are summarized, and various solution methods are categorized and evaluated.