A novel dynamic interacting particle swarm optimization algorithm (DYN-PSO) is proposed. The algorithm can be considered to be the synthesis of two established trajectory methods for unconstrained minimization. In the new method, the minimization of a function is achieved through the dynamic motion of a strongly interacting particle swarm, where each particle in the swarm is simultaneously attracted by all other particles located at positions of lower function value. The force of attraction experienced by a particle at higher function value due to a particle at a lower function value is equal to the difference between the respective function-values divided by their stochastically perturbed position difference. The resultant motion of the particles under the influence of the attracting forces is computed by solving the associated equations of motion numerically. An energy dissipation strategy is applied to each particle. The specific chosen force law and the dissipation strategy result in the rapid collapse (convergence) of the swarm to a stationary point. Numerical results show that, in comparison to the standard particle swarm algorithm, the proposed DYN-PSO algorithm is promising.

S. Kok

J. A. Snyman

A Strongly Interacting Dynamic Particle Swarm  Optimization Method

For stochastic optimisation algorithms, knowing the probability distribution with which
an algorithm allocates new samples in the search space is very important, since this explains
how the algorithm really works and is a prerequisite to being able to match algorithms to
problems. This is the only way to beat the limitations highlighted by the no-free lunch theory.
Yet, the sampling distribution for velocity-based particle swarm optimisers has remained a
mystery for the whole of the first decade of PSO research. In this paper, a method is presented
that allows one to exactly determine all the characteristics of a PSO&#x27;s sampling distribution
and explain how it changes over time during stagnation (i.e., while particles are in search for
a better personal best) for a large class of PSO&#x27;s.

Riccardo Poli

Dynamics and Stability of the Sampling Distribution  of Particle Swarm Optimisers via Moment Analysis

This paper presents recent advances in applying particle swarm optimization (PSO) to antenna designs in engineering electromagnetics. By linking the PSO kernel with external electromagnetic (EM) analyzers, the algorithm has the flexibility to handle both real and binary variables, as well as multiobjective problems with more than one optimization goal. Three examples, including the designs of a dual-band patch antenna, an artificial ground plane of a surface wave antenna, and an aperiodic antenna array, are presented. Both simulation and measurement results are provided to illustrate the effectiveness of applying the swarm intelligence to design antennas with desired frequency response and radiation characteristics for practical EM applications.

Nanbo Jin

Yahya Rahmat-Samii

Particle Swarm Optimization for Antenna Designs in Engineering Electromagnetics

The discrete particle swarm optimization (DPSO) algorithm is an
optimization technique which belongs to the fertile paradigm of Swarm Intelligence.
Designed for the task of attribute selection, the DPSO deals with discrete
variables in a straightforward manner. This work empowers the DPSO algorithm
by extending it in two ways. First, it enables the DPSO to select attributes for a
Bayesian network algorithm, which is more sophisticated than the Naive Bayes
classifier previously used by the original DPSO algorithm. Second, it applies the
DPSO to a set of challenging protein functional classification data, involving a
large number of classes to be predicted. The work then compares the performance
of the DPSO algorithm against the performance of a standard Binary PSO
algorithm on the task of selecting attributes on those data sets. The criteria used
for this comparison are (1) maximizing predictive accuracy and (2) finding the
smallest subset of attributes.

Elon S. Correa

Alex A. Freitas

Colin G. Johnson

Particle Swarm for Attribute Selection in Bayesian Classification: An Application to Protein Function Prediction

This paper presents a macrocell placement constraint and overlap removal methodology using an improved particle swarm optimization (PSO). Several techniques have been proposed to improve PSO, such as methods to prevent the <?bhlt ?>floorplan<?ehlt ?> from falling into the local minimum and to assist in finding the global minimum. The proposed method can deal with various kinds of placement constraints and can process them simultaneously. Experiments employing MCNC and GSRC benchmarks show the difference in the efficiency and robustness of proposed method in the exploration for more optimal solutions through restricted placement and overlap removal compared with other methods. The proposed approach exhibits rapid convergence and leads to more optimal solutions than other related approaches; furthermore, it displays efficient packing with all the constraints satisfied.

Sheng-Ta Hsieh

Tsung-Ying Sun

Chan-Cheng -Liu

Cheng-Wei Lin

An Improved Particle Swarm Optimizer for  Placement Constraints

Using a geometric framework for the interpretation of crossover of recent introduction, we show an intimate connection between particle swarm optimisation (PSO) and evolutionary algorithms. This connection enables us to generalise PSO to virtually any solution representation in a natural and straightforward way. The new Geometric
PSO (GPSO) applies naturally to both continuous and combinatorial spaces. We demonstrate this for the cases of Euclidean, Manhattan and Hamming spaces and report extensive experimental results. We also demonstrate the applicability of GPSO to more challenging combinatorial spaces. The Sudoku puzzle is a perfect candidate to test new
algorithmic ideas because it is entertaining and instructive as well as being a nontrivial constrained combinatorial problem. We apply GPSO to solve the Sudoku puzzle.

Alberto  Moraglio

Cecilia  Di Chio

Julian  Togelius

Riccardo  Poli

Geometric Particle Swarm Optimization

Simplified forms of the particle swarm algorithm are very 
    beneficial in contributing to understanding how a particle 
    swarm optimization (PSO) swarm functions. One of these forms, 
    PSO with discrete recombination, is extended and analyzed, 
    demonstrating not just improvements in performance relative to 
    a standard PSO algorithm, but also significantly different 
    behavior, namely, a reduction in bursting patterns due to the 
    removal of stochastic components from the update 
    equations.

Dan  Bratton

Tim  Blackwell

A Simplified Recombinant PSO

The tail of the particle swarm optimisation (PSO) position distribution at stagnation
is shown to be describable by a power law. This tail fattening is attributed to
particle bursting on all length scales. The origin of the power law is concluded to
lie in multiplicative randomness, previously encountered in the study of first-order
stochastic difference equations, and generalised here to second-order equations. It
is argued that recombinant PSO, a competitive PSO variant without multiplicative
randomness, does not experience tail fattening at stagnation.

Examination of Particle Tails

Evolutionary algorithms (EAs) can be used in order to design particle swarm optimization (PSO) algorithms
that work, in some cases, considerably better than the human-designed ones. By analyzing the
evolutionary process of designing PSO algorithms, we can identify different swarm phenomena (such as patterns
or rules) that can give us deep insights about the swarm behavior. The rules that have been observed
can help us design better PSO algorithms for optimization. We investigate and analyze swarm phenomena
by looking into the process of evolving PSO algorithms. Several test problems have been analyzed in the
experiments and interesting facts can be inferred from the strategy evolution process (the particle quality
could influence the update order, some particles are updated more frequently than others, the initial swarm
size is not always optimal).

Laura  Dio&#x015F;an

Mihai  Oltean

What Else Is the Evolution of PSO Telling Us?

A dynamic system is represented as a set of equations that specify how variables
change over time. The equations in the system specify how to compute the
new values of the state variables as a function of their current values and the values
of the control parameters. If those parameters change beyond certain values,
the system exhibits qualitative changes in its behavior. Those qualitative changes
are called bifurcations, and the values for the parameters where those changes occur
are called bifurcation points. In this contribution, we present an application
of particle swarm optimization methods for dynamic environments for plotting bifurcation
diagrams used in the analysis of dynamical systems. The use of particle
swarm optimization methods presents various advantages over traditional methods.

Julio  Barrera

Juan J. Flores

Claudio  Fuerte-Esquivel

Generating Complete Bifurcation Diagrams Using a Dynamic Environment Particle Swarm Optimization Algorithm

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Journal of Artificial Evolution and Applications

Particle Swarms: The Second Decade

Particle Swarms: The Second Decade

Articles

A Strongly Interacting Dynamic Particle Swarm Optimization Method

Dynamics and Stability of the Sampling Distribution of Particle Swarm Optimisers via Moment Analysis