Swarm intelligence: the state of the art special issue of natural computing

Swarm intelligence: the state of the art special issue of natural computing
Content Type Journal ArticleDOI 10.1007/s11047-009-9172-6Authors
Eric Bonabeau, Icosystem, 10 Fawcett Street, Cambridge, MA 02138, USADavid Corne, Heriot-Watt University, Ed…

Swarm intelligence: the state of the art special issue of natural computing

  • Content Type Journal Article
  • DOI 10.1007/s11047-009-9172-6
  • Authors
    • Eric Bonabeau, Icosystem, 10 Fawcett Street, Cambridge, MA 02138, USA
    • David Corne, Heriot-Watt University, Edinburgh, EH14 4AS UK
    • Riccardo Poli, School of Computer Science and Electronic Engineering, University of Essex, Colchester, CO4 3SQ UK

Special issue on Nature Inspired Cooperative Strategies for Optimisation (NICSO)

Special issue on Nature Inspired Cooperative Strategies for Optimisation (NICSO)
Content Type Journal ArticleDOI 10.1007/s11047-009-9177-1Authors
N. Krasnogor, University of Catania Catania ItalyG. Nicosia, University of Catania Catania ItalyM. Pavo…

Special issue on Nature Inspired Cooperative Strategies for Optimisation (NICSO)

  • Content Type Journal Article
  • DOI 10.1007/s11047-009-9177-1
  • Authors
    • N. Krasnogor, University of Catania Catania Italy
    • G. Nicosia, University of Catania Catania Italy
    • M. Pavone, University of Catania Catania Italy
    • D. A. Pelta, University of Catania Catania Italy

Multiobjective particle swarm optimization with nondominated local and global sets

Abstract  In multiobjective particle swarm optimization (MOPSO) methods, selecting the local best and the global best for each particle
of the population has a great impact on the convergence and diversity of solutions, especially when optim…

Abstract  

In multiobjective particle swarm optimization (MOPSO) methods, selecting the local best and the global best for each particle
of the population has a great impact on the convergence and diversity of solutions, especially when optimizing problems with
high number of objectives. This paper presents an approach using two sets of nondominated solutions. The ability of the proposed
approach to detect the true Pareto optimal solutions and capture the shape of the Pareto front is evaluated through experiments
on well-known non-trivial multiobjective test problems as well as the real-life electric power dispatch problem. The diversity
of the nondominated solutions obtained is demonstrated through different measures. The proposed approach has been assessed
through a comparative study with the reported results in the literature.

  • Content Type Journal Article
  • DOI 10.1007/s11047-009-9171-7
  • Authors
    • M. A. Abido, Electrical Engineering Department, King Fahd University of Petroleum and Minerals, Dhahran, 31261 Saudi Arabia

Philosophy & engineering meeting, fPET-2010, call for abstracts extended until 15 January

The 2010 Forum on Philosophy, Engineering & Technology or fPET-2010 (to be held as a one-day intensive event on 9-10 May 2010, Sunday Evening-Monday, at the Colorado School of Mines in Golden, CO) has extended the deadline for abstract submission until 15 January 2010 (Friday).
An extended call for papers is available as a pdf file […]

The 2010 Forum on Philosophy, Engineering & Technology or fPET-2010 (to be held as a one-day intensive event on 9-10 May 2010, Sunday Evening-Monday, at the Colorado School of Mines in Golden, CO) has extended the deadline for abstract submission until 15 January 2010 (Friday).

An extended call for papers is available as a pdf file (here) or online (here).  Inquiries about fPET-2010 can be sent to co-chairs Diane Michelfelder (michelfelder@macalester.edu) or Dave Goldberg (deg@illinois.edu).

Bacteria that turn gears

An interesting inversion of several of the themes covered in this journal:http://news.yahoo.com/s/livescience/20091226/sc_livescience/scientistsharnessbacteriatoturnmicroscopicgearsIt would be even more interesting if gear-turning performance drove sel…

An interesting inversion of several of the themes covered in this journal:
It would be even more interesting if gear-turning performance drove selection…
-Lee

Flocking based approach for data clustering

Abstract  Data clustering is a process of extracting similar groups of the underlying data whose labels are hidden. This paper describes
different approaches for solving data clustering problem. Particle swarm optimization (PSO) has been rec…

Abstract  

Data clustering is a process of extracting similar groups of the underlying data whose labels are hidden. This paper describes
different approaches for solving data clustering problem. Particle swarm optimization (PSO) has been recently used to address
clustering task. An overview of PSO-based clustering approaches is presented in this paper. These approaches mimic the behavior
of biological swarms seeking food located in different places. Best locations for finding food are in dense areas and in regions
far enough from others. PSO-based clustering approaches are evaluated using different data sets. Experimental results indicate
that these approaches outperform K-means, K-harmonic means, and fuzzy c-means clustering algorithms.

  • Content Type Journal Article
  • DOI 10.1007/s11047-009-9173-5
  • Authors
    • Abbas Ahmadi, Department of Industrial Engineering, Amirkabir University of Technology, 424 Hafez Ave., Tehran, Iran
    • Fakhri Karray, Electrical and Computer Engineering Department, University of Waterloo, Waterloo, ON, Canada
    • Mohamed S. Kamel, Electrical and Computer Engineering Department, University of Waterloo, Waterloo, ON, Canada

BGSA: binary gravitational search algorithm

Abstract  Gravitational search algorithm is one of the new optimization algorithms that is based on the law of gravity and mass interactions.
In this algorithm, the searcher agents are a collection of masses, and their interactions are based…

Abstract  

Gravitational search algorithm is one of the new optimization algorithms that is based on the law of gravity and mass interactions.
In this algorithm, the searcher agents are a collection of masses, and their interactions are based on the Newtonian laws
of gravity and motion. In this article, a binary version of the algorithm is introduced. To evaluate the performances of the
proposed algorithm, several experiments are performed. The experimental results confirm the efficiency of the BGSA in solving
various nonlinear benchmark functions.

  • Content Type Journal Article
  • DOI 10.1007/s11047-009-9175-3
  • Authors
    • Esmat Rashedi, Department of Electrical Engineering, Shahid Bahonar University of Kerman, P.O. Box 76169-133, Kerman, Iran
    • Hossein Nezamabadi-pour, Department of Electrical Engineering, Shahid Bahonar University of Kerman, P.O. Box 76169-133, Kerman, Iran
    • Saeid Saryazdi, Department of Electrical Engineering, Shahid Bahonar University of Kerman, P.O. Box 76169-133, Kerman, Iran

A novel particle swarm niching technique based on extensive vector operations

Abstract  Several techniques have been proposed to extend the particle swarm optimization (PSO) paradigm so that multiple optima can
be located and maintained within a convoluted search space. A significant number of these implementations ar…

Abstract  

Several techniques have been proposed to extend the particle swarm optimization (PSO) paradigm so that multiple optima can
be located and maintained within a convoluted search space. A significant number of these implementations are subswarm-based,
that is, portions of the swarm are optimized separately. Niches are formed to contain these subswarms, a process that often
requires user-specified parameters. The proposed technique, known as the vector-based PSO, uses a novel approach to locate
and maintain niches by using additional vector operations to determine niche boundaries. As the standard PSO uses weighted
vector combinations to update particle positions and velocities, the niching technique builds upon existing knowledge of the
particle swarm. Once niche boundaries have been calculated, the swarm can be organized into subswarms without prior knowledge
of the number of niches and their corresponding niche radii. This paper presents the vector-based PSO with emphasis on its
underlying principles. Results for a number of functions with different characteristics are reported and discussed. The performance
of the vector-based PSO is also compared to two other niching techniques for particle swarm optimization.

  • Content Type Journal Article
  • DOI 10.1007/s11047-009-9170-8
  • Authors
    • I. L. Schoeman, Department of Computer Science, University of Pretoria, Lynnwood Road, Pretoria, South Africa
    • A. P. Engelbrecht, Department of Computer Science, University of Pretoria, Lynnwood Road, Pretoria, South Africa

Discrete and continuous optimization based on multi-swarm coevolution

Abstract  This paper presents a novel Multi-swarm Particle Swarm Optimizer called PS2O, which is inspired by the coevolution of symbiotic species in natural ecosystems. The main idea of PS2O is to extend the single population PSO to the inter…

Abstract  

This paper presents a novel Multi-swarm Particle Swarm Optimizer called PS2O, which is inspired by the coevolution of symbiotic species in natural ecosystems. The main idea of PS2O is to extend the single population PSO to the interacting multi-swarms model by constructing hierarchical interaction topology
and enhanced dynamical update equations. With the hierarchical interaction topology, a suitable diversity in the whole population
can be maintained. At the same time, the enhanced dynamical update rule significantly speeds up the multi-swarm to converge
to the global optimum. The PS2O algorithm, which is conceptually simple and easy to implement, has considerable potential for solving complex optimization
problems. With a set of 17 mathematical benchmark functions (including both continuous and discrete cases), PS2O is proved to have significantly better performance than four other successful variants of PSO.

  • Content Type Journal Article
  • DOI 10.1007/s11047-009-9174-4
  • Authors
    • Hanning Chen, Key Laboratory of Industrial Informatics, Shenyang Institute of Automation, Chinese Academy of Sciences, Faculty Office III, Nanta Street 114#, Dongling District, 110016 Shenyang, China
    • Yunlong Zhu, Key Laboratory of Industrial Informatics, Shenyang Institute of Automation, Chinese Academy of Sciences, Faculty Office III, Nanta Street 114#, Dongling District, 110016 Shenyang, China
    • Kunyuan Hu, Key Laboratory of Industrial Informatics, Shenyang Institute of Automation, Chinese Academy of Sciences, Faculty Office III, Nanta Street 114#, Dongling District, 110016 Shenyang, China

GECCO 2010 Submission Deadline (Extended)

If you are planning to submit a paper for the 2010 Genetic and Evolutionary Computation Conference, the deadline is January 13, 2010 (and now extended to January 27th). You can find more information at the GECCO 2010 calendar site. Related posts:GECCO 2009 paper submission deadline extended till January 28 GECCO 2007 deadline extended GECCO-2006 submissions […]

Related posts:

  1. GECCO 2009 paper submission deadline extended till January 28
  2. GECCO 2007 deadline extended
  3. GECCO-2006 submissions deadline extended to February 1st

If you are planning to submit a paper for the 2010 Genetic and Evolutionary Computation Conference, the deadline is January 13, 2010 (and now extended to January 27th). You can find more information at the GECCO 2010 calendar site.

Related posts:

  1. GECCO 2009 paper submission deadline extended till January 28
  2. GECCO 2007 deadline extended
  3. GECCO-2006 submissions deadline extended to February 1st