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Swarm Intelligence Symposium, 2007. SIS 2007. IEEE

Date 1-5 April 2007

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  • [Commentary]

    Publication Year: 2007 , Page(s): nil1
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    Freely Available from IEEE
  • Organizing Committee

    Publication Year: 2007 , Page(s): nil2 - nil4
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    Freely Available from IEEE
  • IEEE Swarm Intelligence Symposium (SIS2007)

    Publication Year: 2007 , Page(s): nil5 - nil7
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  • A Particle Swarm Optimizer for Finding Minimum Free Energy RNA Secondary Structures

    Publication Year: 2007 , Page(s): 1 - 8
    Cited by:  Papers (5)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (9173 KB) |  | HTML iconHTML  

    This paper introduces the HelixPSO particle swarm optimization (PSO) algorithm for finding minimum energy RNA secondary structures. It is shown experimentally that HelixPSO profits when it is combined with a genetic algorithm that finds a good starting population for HelixPSO. On all test instances this hybrid variant of HelixPSO performs significantly better than a state-of-the-art genetic algorithm. Also compared with another PSO algorithm that has been proposed very recently for the prediction of RNA secondary structures, HelixPSO is more efficient both in terms of free energy and correctly predicted base pairs View full abstract»

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  • Combined Training of Recurrent Neural Networks with Particle Swarm Optimization and Backpropagation Algorithms for Impedance Identification

    Publication Year: 2007 , Page(s): 9 - 15
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (7756 KB) |  | HTML iconHTML  

    A recurrent neural network (RNN) trained with a combination of particle swarm optimization (PSO) and backpropagation (BP) algorithms is proposed in this paper. The network is used as a dynamic system modeling tool to identify the frequency-dependent impedances of power electronic systems such as rectifiers, inverters, and DC-DC converters. As a category of supervised learning methods, the various backpropagation training algorithms developed for recurrent neural networks use gradient descent information to guide their search for optimal weights solutions that minimize the output errors. While they prove to be very robust and effective in training many types of network structures, they suffer from some serious drawbacks such as slow convergence and being trapped at local minima. In this paper, a modified particle swarm optimization technique is used in combination with the backpropagation algorithm to traverse in a much larger search space for the optimal solution. The combined method preserves the advantages of both techniques and avoids their drawbacks. The method is implemented to train a RNN that successfully identifies the impedance characteristics of a three-phase inverter system. The performance of the proposed method is compared to those of both BP and PSO when used separately to solve the problem, demonstrating its superiority View full abstract»

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  • Enhanced Learning in Fuzzy Simulation Models Using Memetic Particle Swarm Optimization

    Publication Year: 2007 , Page(s): 16 - 22
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (7939 KB) |  | HTML iconHTML  

    Fuzzy cognitive maps constitute an important simulation methodology that combines neural networks and fuzzy logic. The Fuzzy cognitive maps designed by the experts can be enhanced significantly through learning algorithms, which proved to increase their efficiency and accuracy of simulation. Recently, learning algorithms that employ particle swarm optimization for the minimization of properly defined objective functions have been introduced. In this work, we enhance these learning schemes by incorporating local search in PSO, resulting in a memetic particle swarm optimization learning algorithm. Three variants of the memetic algorithm are applied successfully for the optimization of an Ecological Industrial Park simulation system and they are compared also with the established particle swarm optimization learning schemes. Results are reported and discussed, deriving useful conclusions View full abstract»

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  • Allocating Multiple Base Stations under General Power Consumption by the Particle Swarm Optimization

    Publication Year: 2007 , Page(s): 23 - 28
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (6344 KB) |  | HTML iconHTML  

    In this paper, a two-tiered wireless sensor networks consisting of small sensor nodes, application nodes and base-stations is considered. An algorithm based on particle swarm optimization (PSO) is proposed for multiple base stations under general power-consumption constraints. The proposed approach can search for nearly optimal BS locations in heterogeneous sensor networks, where application nodes may own different data transmission rates, initial energies and parameter values. Experimental results also show the good performance of the proposed PSO approach and the effects of the parameters on the results View full abstract»

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  • Control of a robotic swarm for the elimination of marine oil pollutions

    Publication Year: 2007 , Page(s): 29 - 36
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (468 KB) |  | HTML iconHTML  

    This paper presents the concept as well as first results of the EU-MOP ("elimination units for marine oil pollutions") project. The basic idea of this project is a swarm out of autonomous marine robots which are able to recover oil with the help of oil skimmers. In order to achieve a flexible and robust system, the swarm intelligence (SI) approach has been used as control paradigm for the EU-MOP robots. Within the SI approach interaction between the robots plays an important role for the performance of the whole multi robot system. Thus, three control approaches, all basing on SI, but with different levels of interaction, have been developed. Furthermore a method for the evaluation of swarms in comparison to single robot systems will be presented View full abstract»

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  • Alice in Pheromone Land: An Experimental Setup for the Study of Ant-like Robots

    Publication Year: 2007 , Page(s): 37 - 44
    Cited by:  Papers (7)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1001 KB) |  | HTML iconHTML  

    The pheromone trail laying and trail following behaviors of ants have proved to be an efficient mechanism to optimize path selection in natural as well as in artificial networks. Despite this efficiency, this mechanism is under-used in collective robotics because of the chemical nature of pheromones. In this paper we present a new experimental setup which allows to investigate with real robots the properties of a robotics systems using such behaviors. To validate our setup, we present the results of an experiment in which a group of 5 robots has to select between two identical alternatives a path linking two different areas. Moreover, a set of computer simulations provides a more complete exploration of the properties of this system. At last, experimental and simulation results lead us to interesting prediction that will be testable in our setup. View full abstract»

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  • An Analysis of Emergent Taxis in a Wireless Connected Swarm of Mobile Robots

    Publication Year: 2007 , Page(s): 45 - 52
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (217 KB) |  | HTML iconHTML  

    In swarm robotic systems emergent swarm properties are particularly difficult to analyse and model. This paper describes a simple but effective algorithm for emergent swarm taxis (swarm motion toward a beacon) in a 2D or 3D wireless connected swarm of minimalist mobile robots. The paper then undertakes a deep analysis of the swarm taxis by identifying both first and second order micro-level robot interactions and quantifying the contribution of each such interaction to the macro-level swarm behaviour. From the analysis we develop a simple quantitative model that is able to predict swarm velocity with reasonable accuracy. Although the analysis is specific to the swarm algorithm in question, we believe that the methodology presented has generic value to swarm modellers. View full abstract»

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  • Using the Particle Swarm Optimization Algorithm for Robotic Search Applications

    Publication Year: 2007 , Page(s): 53 - 59
    Cited by:  Papers (16)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (7657 KB) |  | HTML iconHTML  

    This paper describes the experimental results of using the particle swarm optimization (PSO) algorithm to control a suite of robots. In our approach, each bot is one particle in the PSO; each particle/bot makes measurements, updates its own position and velocity, updates its own personal best measurement (pbest) and personal best location (if necessary), and broadcasts to the other bots if it has found a global best measurement/position. We built three bots and tested the algorithm by letting the bots find the brightest spot of light in the room. The tests show that using the PSO to control a swarm can successfully find the target, even in the presence of obstacles View full abstract»

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  • Ant Colony Systems for Large Sequential Ordering Problems

    Publication Year: 2007 , Page(s): 60 - 67
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (265 KB) |  | HTML iconHTML  

    The sequential ordering problem is a version of the asymmetric traveling salesman problem where precedence constraints on vertices are imposed. A tour is feasible if these constraints are respected, and the objective is to find a feasible solution with minimum cost. The sequential ordering problem models a lot of real world applications, mainly in the fields of transportation and production planning. In this paper we propose an extension of a well known ant colony system for the problem, aiming at making the approach more efficient on large problems. The extension is based on a problem manipulation technique that heuristically reduces the search space. Computational results, where the extended ant colony system is compared to the original one, are presented View full abstract»

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  • A Stochastic Rank-Based Ant System for Discrete Structural Optimization

    Publication Year: 2007 , Page(s): 68 - 75
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (9073 KB) |  | HTML iconHTML  

    Penalty methods are often used to handle constraints in optimization problems. However, to find the optimal or near optimal set of penalty parameters is a hard task. Also, such values are problem dependent. This paper introduces the stochastic ranking approach to balance objective and penalty functions stochastically in a rank-based ACO metaheuristic. The results presented show that the simple inclusion of the procedure leads to an improved search performance, with respect to the standard penalty technique, when applied to discrete structural optimization problems View full abstract»

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  • Ant Colony Optimization Approaches for the Dynamic Load-Balanced Clustering Problem in Ad Hoc Networks

    Publication Year: 2007 , Page(s): 76 - 83
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (153 KB) |  | HTML iconHTML  

    This paper presents three ant colony optimization (ACO) approaches for a difficult graph theoretic problem formulated from the task of computing load-balanced clusters in ad hoc networks. These three approaches contain novel strategies for adapting the search process to the new problem structure whenever an environment change occurs. An environment change occurs when nodes in the network move. Dynamic changes in problem structure pose a great challenge for ACO algorithms because the pheromone information is rendered inaccurate and inconsistent. Hence, all three strategies to enable ACO to work in a dynamic setting have a common objective, that is, to adapt the pheromone information to closely reflect the new problem structure. The first approach is the population-based ACO algorithm (P-ACO) that incorporates a novel solution repair procedure. The second approach, which we call PAdapt, works by adapting three major algorithm parameters following an innovative strategy. The third approach, which we term GreedyAnts, uses a greedy solution construction strategy to bias the pheromone information towards the new problem structure. Empirical results show that GreedyAnts is very competitive with P-ACO, while PAdapt is less impressive. The GreedyAnts approach is advantageous over P-ACO because it does not require a solution repair heuristic that incurs additional processing View full abstract»

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  • A Metaheuristic Approach to the Graceful Labeling Problem of Graphs

    Publication Year: 2007 , Page(s): 84 - 91
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (8146 KB) |  | HTML iconHTML  

    In this paper, an algorithm based on ant colony optimization metaheuristic is proposed for finding solutions to the well-known graceful labeling problem of graphs. Despite the large number of papers published on the theory of this problem, there are few particular techniques introduced by researchers for gracefully labeling graphs. The proposed algorithm is applied to many classes of graphs, and the results obtained have proven satisfactory when compared to those of the existing methods in the literature View full abstract»

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  • Stagnation Analysis in Particle Swarm Optimization

    Publication Year: 2007 , Page(s): 92 - 99
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (7138 KB) |  | HTML iconHTML  

    Particle swarm optimization (PSO) has shown to be an efficient, robust and simple optimization algorithm, and has been successfully applied to many different kinds of problems. But it is still an open problem that why PSO can be successful. Most of current PSO studies are empirical, with only a few theoretical analyses, and these theoretical studies concentrate mainly on simplified PSO systems, discarding randomness. In order to improve the understanding of real stochastic PSO algorithm, this paper presents a formal stochastic analysis of the stochastic PSO algorithm, which involves with randomness. The stochastic properties of particle trajectories in stagnation phase are studied in details View full abstract»

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  • High-speed Interconnect Simulation Using Particle Swarm Optimization

    Publication Year: 2007 , Page(s): 100 - 104
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4635 KB) |  | HTML iconHTML  

    Particle swarm optimization (PSO) is proposed as an efficient algorithm for simulation of high speed interconnects used in today's digital applications. First, a generic methodology is proposed for high speed interconnects simulation using PSO and finally comparisons are made between the performance of PSO compared to traditional optimization techniques used in high-speed serial bus simulation View full abstract»

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  • Solving Multi-agent Control Problems Using Particle Swarm Optimization

    Publication Year: 2007 , Page(s): 105 - 111
    Cited by:  Papers (5)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (147 KB) |  | HTML iconHTML  

    This paper outlines an approximate algorithm for finding an optimal decentralized control in multi-agent systems. Decentralized partially observable Markov decision processes and their extension to infinite state, observation and action spaces are utilized as a theoretical framework. In the presented algorithm, policies of each agent are represented by a feedforward neural network. Then, a search is performed in a joint weight space of all networks. Particle swarm optimization is applied as a search algorithm. Experimental results are provided showing that the algorithm finds good solutions for the classical Tiger problem extended to multi-agent systems, as well as for a multi-agent navigation task involving large state and action spaces View full abstract»

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  • Differential Evolution Based Particle Swarm Optimization

    Publication Year: 2007 , Page(s): 112 - 119
    Cited by:  Papers (8)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (264 KB) |  | HTML iconHTML  

    A new, almost parameter-free optimization algorithm is developed in this paper as a hybrid of the barebones particle swarm optimizer (PSO) and differential evolution (DE). The DE is used to mutate, for each particle, the attractor associated with that particle, defined as a weighted average of its personal and neighborhood best positions. Results of this algorithm are compared to that of the barebones PSO, Von Neumann PSO, a DE PSO, and DE/rand/1/bin. These results show that the new algorithm provides excellent results with the added advantage that no parameter tuning is needed View full abstract»

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  • Defining a Standard for Particle Swarm Optimization

    Publication Year: 2007 , Page(s): 120 - 127
    Cited by:  Papers (151)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (162 KB) |  | HTML iconHTML  

    Particle swarm optimization has become a common heuristic technique in the optimization community, with many researchers exploring the concepts, issues, and applications of the algorithm. In spite of this attention, there has as yet been no standard definition representing exactly what is involved in modern implementations of the technique. A standard is defined here which is designed to be a straightforward extension of the original algorithm while taking into account more recent developments that can be expected to improve performance on standard measures. This standard algorithm is intended for use both as a baseline for performance testing of improvements to the technique, as well as to represent PSO to the wider optimization community View full abstract»

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  • A Memetic PSO Algorithm for Scalar Optimization Problems

    Publication Year: 2007 , Page(s): 128 - 134
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (234 KB) |  | HTML iconHTML  

    In this paper we introduce line search strategies originating from continuous optimization for the realization of the guidance mechanism in particle swarm optimization for scalar optimization problems. Since these techniques are well-suited for-but not restricted to-local search the resulting algorithm can be considered to be memetic. Further, we will use the same techniques for the construction of a new variant of a hill climber. We will discuss possible realizations and will finally present some numerical results indicating the strength of the two algorithms View full abstract»

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  • Building Nearest Prototype Classifiers Using a Michigan Approach PSO

    Publication Year: 2007 , Page(s): 135 - 140
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (155 KB) |  | HTML iconHTML  

    This paper presents an application of particle swarm optimization (PSO) to continuous classification problems, using a Michigan approach. In this work, PSO is used to process training data to find a reduced set of prototypes to be used to classify the patterns, maintaining or increasing the accuracy of the nearest neighbor classifiers. The Michigan approach PSO represents each prototype by a particle and uses modified movement rules with particle competition and cooperation that ensure particle diversity. The result is that the particles are able to recognize clusters, find decision boundaries and achieve stable situations that also retain adaptation potential. The proposed method is tested both with artificial problems and with three real benchmark problems with quite promising results View full abstract»

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  • Probabilistically Driven Particle Swarms for Optimization of Multi Valued Discrete Problems : Design and Analysis

    Publication Year: 2007 , Page(s): 141 - 149
    Cited by:  Papers (10)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (761 KB) |  | HTML iconHTML  

    A new particle swarm optimization (PSO) algorithm that is more effective for discrete, multi-valued optimization problems is presented. The new algorithm is probabilistically driven since it uses probabilistic transition rules to move from one discrete value to another in the search for an optimum solution. Properties of the binary discrete particle swarms are discussed. The new algorithm for discrete multi-values is designed with the similar properties. The algorithm is tested on a suite of benchmarks and comparisons are made between the binary PSO and the new discrete PSO implemented for ternary, quaternary systems. The results show that the new algorithm's performance is close and even slightly better than the original discrete, binary PSO designed by Kennedy and Eberhart. The algorithm can be used in any real world optimization problems, which have a discrete, bounded field View full abstract»

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  • On Trajectories of Particles in PSO

    Publication Year: 2007 , Page(s): 150 - 155
    Cited by:  Papers (7)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1890 KB) |  | HTML iconHTML  

    The moving behaviour of the particles in particle swarm optimization (PSO) algorithms is studied in this paper. It is shown that particles in standard PSO have a clear bias in their movement direction that depends on the direction of the coordinate axes. This has the effect that the optimization behaviour of standard PSO is not invariant to rotations of the optimization function. A second problem of standard PSO is that non-oscillatory trajectories can quickly cause a particle to stagnate. A sidestep mechanism is proposed to improve the movement of the particles. A particle performs a sidestep with respect to a certain dimension when stagnation of movement along this dimension is observed. It is shown for simple test functions that the movement behaviour of sidestep PSO can prevent the unwanted bias and makes PSO less dependent on rotations of the optimization function. It is also shown for standard benchmark functions that sidestep PSO outperforms standard PSO View full abstract»

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  • Finding Least Cost Proofs Using a Hierarchical PSO

    Publication Year: 2007 , Page(s): 156 - 161
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (6404 KB) |  | HTML iconHTML  

    Abduction is the process of proceeding from data describing a set of observations or events, to a set of hypotheses which best explains or accounts for the data. Cost-based abduction (CBA) is a formalism in which evidence to be explained is treated as a goal to be proven, proofs have costs based on how much needs to be assumed to complete the proof, and the set of assumptions needed to complete the least-cost proof are taken as the best explanation for the given evidence. In this paper, we explore using a hierarchical PSO to find least-cost proofs in cost-based abduction systems, comparing performance to simulated annealing using a difficult problem instance. View full abstract»

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