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Swarm Intelligence Symposium, 2009. SIS '09. IEEE

Date March 30 2009-April 2 2009

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Displaying Results 1 - 25 of 29
  • [Front cover]

    Page(s): c1
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    Freely Available from IEEE
  • [Copyright notice]

    Page(s): ii
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  • Table of contents

    Page(s): iii - vi
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  • Welcome message

    Page(s): vii - ix
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  • Particle Swarm with graphics hardware acceleration and local pattern search on bound constrained problems

    Page(s): 1 - 8
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (512 KB) |  | HTML iconHTML  

    This paper presents a particle swarm - pattern search optimization (PS2) algorithm with graphics hardware acceleration for bound constrained nonlinear optimization problems. The objective of this study is to determine the effectiveness of using graphics processing units (GPU) as a hardware platform for particle swarm optimization (PSO). GPU, the common graphics hardware which can be found in many personal computers, can be used for desktop data-parallel computing. The classical PSO is adapted in the data-parallel GPU computing platform featuring dasiasingle instruction - multiple threadpsila (SIMT). PSO is also enhanced by adding a local pattern search (PS) improvement. The hybrid PS2 optimization method is implemented in the GPU environment and with a central processing unit (CPU) in a PC. Computational results indicate that GPU-accelerated SIMT-PS2 method is orders of magnitude faster than the corresponding CPU implementation. The main contribution of this paper is the parallelization analysis and performance analysis of the hybrid PS2 with GPU acceleration. View full abstract»

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  • Concentric spatial extension based particle swarm optimization inspired by brood sorting in ant colonies

    Page(s): 9 - 15
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (421 KB) |  | HTML iconHTML  

    In this paper, a concentric spatial extension based particle swarm optimization (CSE-PSO) is proposed by combining the spatial extension with the brood sorting in ant colonies, which leads to a concentric spatial extension scheme for the PSO. The brood sorting in ant colonies endows the particles in PSO with different radii adaptively according their distances to the best position of the swarm. In such a way, the search space in the CSE-PSO is not only enlarged greatly but also the diversity of the swarm in the CSE-PSO is increased accordingly. Meanwhile, a better trade-off between exploration and exploitation in the PSO is achieved by the concentric spatial extension. Simulation results on the fifteen benchmark test functions announced in IEEE CEC'2005 show that the proposed CSE-PSO is not only capable of speeding up the convergence but also improving the performance of global optimizer greatly on all the fifteen benchmark test functions. View full abstract»

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  • Local Optima Avoidable Particle Swarm Optimization

    Page(s): 16 - 21
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (356 KB) |  | HTML iconHTML  

    This paper proposes a local optima avoidable particle swarm optimization (LOAPSO) which remarkably outperforms the standard PSO in the sense that it can avoid entrapment in local optimum. Three benchmark functions are used to validate the proposed algorithm and compare its performance with that of the other algorithms known as hybrid PSOs and six functions reported in SIS2005 are used to better verification of the proposed algorithm. Numerical results indicate that LOAPSO is considerably competitive due to its ability to avoid being trapped in local optima and to find the functions' global optimum as well as better convergence performance. View full abstract»

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  • A cooperative combinatorial Particle Swarm Optimization algorithm for side-chain packing

    Page(s): 22 - 29
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (385 KB) |  | HTML iconHTML  

    Particle Swarm Optimization (PSO) is a well-known, competitive technique for numerical optimization with real-parameter representation. This paper introduces CCPSO, a new Cooperative Particle Swarm Optimization algorithm for combinatorial problems. The cooperative strategy is achieved by splitting the candidate solution vector into components, where each component is optimized by a particle. Particles move throughout a continuous space, their movements based on the influences exerted by static particles that then get feedback based on the fitness of the candidate solution. Here, the application of this technique to side-chain packing (a proteomics optimization problem) is investigated. To verify the efficiency of the proposed CCPSO algorithm, we test our algorithm on three side-chain packing problems and compare our results with the provably optimal result. Computational results show that the proposed algorithm is very competitive, obtaining a conformation with an energy value within 1% of the provably optimal solution in many proteins. View full abstract»

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  • Information sharing strategy among particles in Particle Swarm Optimization using Laplacian operator

    Page(s): 30 - 36
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (463 KB) |  | HTML iconHTML  

    Particle swarm optimization (PSO) has been extensively used in recent years for the optimization of nonlinear optimization problems. Two of the most popular variants of PSO are PSO-W (PSO with inertia weight) and PSO-C (PSO with constriction factor). Typically particles in swarm use information from global best performing particle, gbest and their own personal best, pbest. Recently, studies have focused on incorporating influences of other particles other than gbest. In this paper, we develop a methodology to share information between two particles using a Laplacian operator designed from Laplace probability density function. The properties of this operator are analyzed. Two particles share their positional information in the search space and a new particle is formed. The particle, called as Laplacian particle, replaces the worst performing particle in the swarm. Using this new operator, this paper introduces two algorithms namely Laplace Crossover PSO with inertia weight (LXPSO-W) and Laplace Crossover PSO with constriction factor (LXPSO-C). The performance of the newly designed algorithms is evaluated with respect to PSO-W and PSO-C using 15 benchmark test problems. The empirical results show that the new approach improves performance measured in terms of efficiency, reliability and robustness. View full abstract»

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  • Particle swarm optimizer for variable weighting in clustering high-dimensional data

    Page(s): 37 - 44
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (511 KB) |  | HTML iconHTML  

    This paper proposes a particle swarm optimizer to solve the variable weighting problem in subspace clustering of high-dimensional data. Many subspace clustering algorithms fail to yield good cluster quality because they do not employ an efficient search strategy. In this paper, we are interested in soft subspace clustering and design a suitable weighting k-means objective function, on which a change of variable weights is exponentially reflected. We transform the original constrained variable weighting problem into a problem with bound constraints using a potential solution coding method and we develop a particle swarm optimizer to minimize the objective function in order to obtain global optima to the variable weighting problem in clustering. Our experimental results on synthetic datasets show that the proposed algorithm greatly improves cluster quality. In addition, the result of the new algorithm is much less dependent on the initial cluster centroids. View full abstract»

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  • Text Clustering via Particle Swarm Optimization

    Page(s): 45 - 51
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (462 KB) |  | HTML iconHTML  

    This paper presents an approach which extends a particle swarm optimizer for variable weighting (PSOVW) to handle the problem of text clustering, called text clustering via particle swarm optimization (TCPSO). PSOVW has been exploited for evolving optimal feature weights for clusters and has demonstrated to improve the clustering quality of high-dimensional data. However, when applying it for text clustering, there exist some modifications such as the similarity measure, parameter selection and the criterion function. Our experimental results on both four structured text datasets built from 20 newsgroups as well as four large-scale text datasets selected from CLUTO show that the proposed algorithm is able to greatly improve the quality of text clustering compared to four typical clustering algorithms and one competitive subspace clustering method. View full abstract»

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  • An Agent Based Parallel Particle Swarm Optimization - APPSO

    Page(s): 52 - 59
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (501 KB) |  | HTML iconHTML  

    As the complexity of optimization problems increases, new scalable architectures for variable problem complexity are needed. In this paper we introduce an agent based framework for distributing and managing a particle swarm on several interconnected computers. Agent Based Parallel Particle Swarm Optimization (APPSO) accelerates the optimization through parallelization and strategical niching, offers dynamic scalability at runtime, and fault tolerance. Due to its load balancing feature APPSO runs efficient on heterogeneous system. Two experiment series on a prototype implementation demonstrate the performance gain achieved by APPSO. View full abstract»

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  • Multi-swarm parallel PSO: Hardware implementation

    Page(s): 60 - 66
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (441 KB) |  | HTML iconHTML  

    The ever increasing popularity of the particle swarm optimization (PSO) algorithm is recently attracting attention to the embedded computing world. Although PSO is considered efficient compared to other contemporary population based optimization techniques, for many continuous multimodal and multidimensional problems, it still suffers from performance loss when it is targeted onto embedded application platforms. Examples of such target applications include small mobile robots and distributed sensor nodes in sensor network applications. In a previous work we presented a novel, modular, efficient and portable hardware architecture to accelerate the performance of the PSO for embedded applications. This paper extends the work by presenting a parallelization technique for further speedup of the PSO algorithm by dividing the swarm into a set of subswarms that are executing in parallel. The underlying communication topology and messaging protocols are described. Finally, the performance of the proposed system is evaluated on mathematical and real-world benchmark functions. View full abstract»

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  • Enhancing performance of PSO with automatic parameter tuning technique

    Page(s): 67 - 73
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (472 KB) |  | HTML iconHTML  

    Particle swarm optimization (PSO) has gained growing popularity in the recent years and is finding a wide range of important applications. Like other population based, stochastic meta-heuristics, PSO has a few algorithm parameters that need to be carefully set to achieve best execution results. This paper develops an automatic parameter tuning technique for enhancing its performance. The effectiveness of the proposed method is demonstrated on mathematical benchmark functions as well as on a real world application problem. View full abstract»

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  • Particle swarm optimization for chaotic system parameter estimation

    Page(s): 74 - 80
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (463 KB) |  | HTML iconHTML  

    A study is presented on the application of particle swarm optimization (PSO) for estimation of parameters in chaotic systems. The parameter estimation is formulated as a nonlinear optimization problem using PSO to minimize the synchronization error for the observable states of the actual system and its mathematical model. The procedure is illustrated using a typical chaotic system of Lorenz equations. The effectiveness of different variants of PSO on parameter estimation is studied with a wide search range of parameters. The results show the capability of the proposed PSO based approach in estimating the chaotic system parameters even in the presence of observation noise. View full abstract»

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  • Swarm intelligence managed UWB waveform and cognitive sensor network protocol

    Page(s): 81 - 87
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (521 KB) |  | HTML iconHTML  

    In this paper, an ultra-wideband waveform that is designed for optimum power and communication protocol that utilizes cognitive processing to make intelligent communication parameter decisions is proposed for wireless sensor application. Due to multiple objectives such as adaptive data rate, power control and quality-of-service, the message transmission becomes a non-deterministic polynomial computation time hard problem requiring cognitive processing. Thus a swarm algorithm that provides an optimal and reliable solution is applied. The low-cost, low-power hardware Bi-pulse-ultra-wideband is resilient to multipath fading, which occurs due to the wall-penetration, shadowing and propagation loss. The designed cross layer protocol incorporates the signal's physical properties. Hence, a cross layer protocol helps to balance the throughput while reducing latency, sensor resources thus longevity of network is attained. View full abstract»

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  • Modeling self-organized aggregation in swarm robotic systems

    Page(s): 88 - 95
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (571 KB) |  | HTML iconHTML  

    In this paper, we propose a model for the self-organized aggregation of a swarm of mobile robots. Specifically, we use a simple probabilistic finite state automata (PFSA) based aggregation behavior and analyze its performance using both a point-mass and a physics-based simulator and compare the results against the predictions of the model. The results show that the probabilistic model predictions match simulation results and PFSA-based aggregation behaviors with fixed probabilities are unable to generate scalable aggregations in low robot densities. Moreover, we show that the use of a leave probability that is inversely proportional to the square of the neighbor count (as an estimate of aggregate size) does not improve the scalability of the behavior. View full abstract»

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  • Flocking-based distributed terrain coverage with dynamically-formed teams of mobile mini-robots

    Page(s): 96 - 103
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1336 KB) |  | HTML iconHTML  

    We consider the problem of distributed terrain coverage within an unknown environment by teams of mobile, resource-constrained mini-robots. Previous research on this topic mainly focuses on multiple robots that cover the environment individually while sharing limited information about their actions using local heuristics. However, these methods do not address the issue of dynamically building robot teams and restructuring the team formations based on the operational constraints within the environment. In this paper, we combine a flocking-based mechanism for robot team formation with a utility-based mechanism that enables robots to change teams to dynamically to improve the coverage of the environment. We have tested our techniques empirically on accurate models of e-puck robots on the Webots simulator within different environments. Our experimental results show that our team-based coverage technique performs significantly better in terms of coverage, by about 25%, and slightly better in terms of redundancy in area covered, than comparable distributed terrain coverage strategies for resource-constrained mini-robots. View full abstract»

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  • Incorporating swarm behavior into the adaptation mechanism of an order-driven artificial financial market

    Page(s): 104 - 108
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (347 KB) |  | HTML iconHTML  

    Agent-based artificial financial markets are an area of increasing interest in computational finance. Recent work by LeBaron and Yamamoto proposes an order-driven market model based on evolutionary algorithm based artificial agents. In this paper, we present a mechanism for incorporating elements of swarm intelligence into this model, and find that our model produces market price behavior that, in some ways, is closer to that of real financial markets. View full abstract»

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  • Key node selection for containing infectious disease spread using particle swarm optimization

    Page(s): 109 - 113
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (333 KB) |  | HTML iconHTML  

    In recent years, some emerging and reemerging infectious diseases have grown into global health threats due to high human mobility. It is important to have intervention plans for containing the spread of such infectious diseases. Among various intervention strategies, screening infected people is an efficient way for evaluating the infection scale and controlling the spread of infectious diseases. Considering the cost in manpower and limited screening machines available, we face to challenges for selecting the optimal nodes (sites) in order to obtain better screening and control effects. In this paper, particle swarm optimization technique is used to determine key nodes for controlling infectious disease spread, through evaluating the number of people captured at each key node. The research example is shown on evaluating the screening control over train stations in Singapore. The optimization algorithm and control concept can be easily extended to large-scale infectious disease control in other kinds of key nodes and in other geographical regions. The selection for optimal control set of the multi objective optimization problem is done using particle swarm optimization. Numerical simulation shows the effectiveness of the proposed algorithm. View full abstract»

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  • Bounded diameter overlay construction: A self organized approach

    Page(s): 114 - 121
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2590 KB) |  | HTML iconHTML  

    This paper describes a distributed algorithm to construct and maintain a peer-to-peer network overlay with bounded diameter. The proposed approach merges a bio-inspired self-organized behavior with a pure peer-to-peer approach, in order to adapt the overlay to underlying changes in the network topology. Ant colonies are used to collect and spread information across all peers, whereas pheromone trails help detecting crashed nodes. Construction of the network favors balanced distribution of links across all peers, so that the resulting topology does not exhibit large hubs. Fault resilience and recovery mechanisms have also been implemented to prevent network partition in the event of node crashes. Validation has been conducted through simulations of different network scenarios. View full abstract»

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  • Achieving spectrum efficiency through signal design for ultra wide band sensor networks

    Page(s): 122 - 128
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (572 KB) |  | HTML iconHTML  

    In this paper, a novel approach is presented for ultra wide band signal design. The bottom up approach designs the signals that utilize the ultra wide band spectrum efficiently. 1st-6th derivative Gaussian pulses are linearly combined using a particle swarm optimization algorithm to form one single pulse. A binary PSO determines the order of the derivative of the pulses that are combined. The continuous PSO determines the time duration and amplitudes for different pulses in the composite pulse. The power spectral density (PSD) of the resultant pulse conforms to the FCC spectral mask and effectively exploits the allowable bandwidth and power. The particle swarm optimization algorithm achieves multiple orthogonal pulses. The newly designed pulses achieve higher spectral efficiencies which is shown theoretically and in simulations. The proposed method presents a flexible and effective way for generating UWB pulses that satisfy the FCC mask. The method can be generalized to design UWB pulses for any given spectral mask. View full abstract»

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  • A novel ultrawide band locationing system using swarm enabled learning approaches

    Page(s): 129 - 136
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (574 KB) |  | HTML iconHTML  

    In this paper, an adaptive learning algorithm is developed using particle swarm to identify and mitigate the non-line of sight (NLOS) signals in ranging measurements. Training data is generated using the IEEE 802.15.4a UWB channel model for different conditions. Multiple metrics derived from this data are fused to identify the NLOS signals. Specifically, kurtosis, mean excess delay and root mean square delay are used as metrics for fusion. The fusion strategy is derived using PSO, considering the correlation between multiple classifiers. We compare the fusion methodology achieved by PSO for the correlated data set to the likelihood ratio based fusion methodology assuming independence. Further, the paper presents an NLOS mitigation approach derived using PSO. A scalar called ldquoerror mitigation ratio (EMR)rdquo is defined. The EMR transform a NLOS measurement into an equivalent LOS measurement. The PSO identifies the EMR using the training data. Application of PSO generated EMR enhances the positioning accuracy and is demonstrated in this paper for indoor wireless channel. This mitigation approach enables us to arrive at a position for the unknown node even when one of the measurements is identified as NLOS. Finally, PSO is used for multilateration to combine measurements from three nodes. Comparisons are done with the linearized least square method. View full abstract»

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  • Ant Colony Optimization for power efficient routing in manhattan and non-manhattan VLSI architectures

    Page(s): 137 - 144
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (489 KB) |  | HTML iconHTML  

    Rapid advances in VLSI technology have increased the number of transistors that fit on a single chip to about two billion. In such complex designs, a primary design goal is to limit the power consumption of the chip. Power consumption depends on capacitance, which depends on the length of wires on the chip and the number of vias which connect wires on different layers of the chip. We use ant colony optimization (ACO) algorithms to minimize wirelength, vias and capacitance. ACO provide a multi-agent framework for combinatorial optimization by combining memory, stochastic decision making and strategies of collective and distributed learning by ant-like agents. This paper applies ACO to the NP-hard problem of finding optimal routes with minimum capacitance for interconnect routing on VLSI chips. The constraints on interconnect routing are used by ants as heuristics which guide their search process. We implemented ACO algorithms on both manhattan and non-manhattan routing architectures. The results are compared with several state of the art academic routers. The ACO routing algorithm was able to obtain an overall improvement of 8% in terms of wire-length, 7% in terms of vias and capacitance. Running times were longer than those routers, but very similar to the other router which is able to route all wires on all benchmark chips. View full abstract»

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  • Swarm pattern transformation methodologies

    Page(s): 145 - 152
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (890 KB) |  | HTML iconHTML  

    The work reported in this paper is motivated by the need for developing swarm pattern transformation methodologies. Two methods, namely a macroscopic method and a mathematical method are investigated for pattern transformation. The first method is based on macroscopic parameters while the second method is based on both microscopic and macroscopic parameters. A formal definition to pattern transformation considering four special cases of transformation is presented. Simulations on a physics simulation engine are used to confirm the feasibility of the proposed transformation methods. A brief comparison between the two methods is also presented. View full abstract»

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