We have previously proposed a hybrid particle swarm optimisation/ant colony optimisation (PSO/ACO) algorithm for the discovery of classification rules. Unlike a conventional PSO algorithm, this hybrid algorithm can directly cope with nominal attributes, without converting nominal values into binary numbers in a preprocessing phase. PSO/ACO2 also directly deals with both continuous and nominal attribute values, a feature that current PSO and ACO rule induction algorithms lack. We evaluate the new version of the PSO/ACO algorithm (PSO/ACO2) in 27 public-domain, real-world data sets often used to benchmark the performance of classification algorithms. We compare the PSO/ACO2 algorithm to an industry standard algorithm PART  and compare a reduced version of our PSO/ACO2 algorithm, coping only with continuous data, to our new classification algorithm for continuous data based on differential evolution. The results show that PSO/ACO2 is very competitive in terms of accuracy to PART and that PSO/ACO2 produces significantly simpler (smaller) rule sets, a desirable result in data mining&#x2014;where the goal is to discover knowledge that is not only accurate but also comprehensible to the user. The results also show that the reduced PSO version for continuous attributes provides a slight increase in accuracy when compared to the differential evolution variant.

Nicholas Holden

Alex A. Freitas

A Hybrid PSO/ACO Algorithm for Discovering  Classification Rules in Data Mining

Riccardo Poli

Jim Kennedy

Tim Blackwell

Alex Freitas

Particle Swarms: The Second Decade

A generalized form of the particle swarm optimization (PSO) algorithm is
presented. Generalized PSO (GPSO) is derived from a continuous version of PSO adopting a
time step different than the unit. <?bhlt ?>Generalized continuous particle swarm optimizations<?ehlt ?> are compared in terms of
attenuation and oscillation. The deterministic and stochastic stability regions and their respective
asymptotic velocities of convergence are analyzed as a function of the time step and the
GPSO parameters. The sampling distribution of the GPSO algorithm helps to study the effect
of stochasticity on the stability of trajectories. The stability regions for the second-, third-, and
fourth-order moments depend on inertia, local, and global accelerations and the time step and are
inside of the deterministic stability region for the same time step. We prove that stability regions
are the same under stagnation and with a moving center of attraction. Properties of the 
second-order moments variance and covariance serve to propose some promising parameter sets. High
variance and temporal uncorrelation improve the exploration task while solving ill-posed inverse
problems. Finally, a comparison is made between PSO and GPSO by means of numerical experiments
using well-known benchmark functions with two types of ill-posedness commonly found in inverse
problems: the Rosenbrock and the &#8220;elongated&#8221; DeJong functions (global minimum located in a
very flat area), and the Griewank function (global minimum surrounded by multiple minima).
Numerical simulations support the results provided by theoretical analysis. Based on these results,
two variants of Generalized PSO algorithm are proposed, improving the convergence and the
exploration task while solving real applications of inverse problems.

J. L. Fern&#225;ndez Mart&#237;nez

E. Garc&#237;a Gonzalo

The Generalized PSO: A New Door to PSO
Evolution

Particle swarm optimisation (PSO) is an innovative and competitive optimisation 
                  technique for numerical optimisation with real-parameter representation. In this paper, we examine the working mechanism of PSO in a principled manner with forma analysis and investigate the applicability of PSO 
                  on the permutation problem domain. Particularly, our derived PSO schemes are empirically studied based on the quadratic assignment problem (QAP) benchmarks to justify its comparable performance, which in turn implies the benefits of our approach in applying PSO to the discrete problem domain.

Tao Gong

Andrew L. Tuson

Forma Analysis of Particle Swarm Optimisation for Permutation Problems

This paper presents a concept for the application of particle swarm optimization in geotechnical engineering. 
For the calculation of deformations in soil or rock, 
numerical simulations based on continuum methods are widely used. 
The material behavior is modeled using constitutive relations that require sets of material parameters to be specified. 
We present an inverse parameter identification technique, based on statistical analyses and a particle swarm optimization algorithm, 
to be used in the calibration process of geomechanical models. 
Its application is demonstrated with typical examples from the fields of soil mechanics and engineering geology. 
The results for two different laboratory tests and a natural slope clearly show that particle swarms are an efficient and fast tool for finding improved parameter sets to represent the measured reference data.

Joerg Meier

Winfried Schaedler

Lisa Borgatti

Alessandro Corsini

Tom Schanz

Inverse Parameter Identification Technique Using PSO Algorithm Applied to Geotechnical 
                        Modeling

The optimization of the nozzle selection, for sequencing component pick and place operations, is very important to the efficiency of multihead surface mounting machine (SMM). The nozzle change operation, that is, choosing the best nozzle head relative pair that is most effective for picking and placing components onto the printed circuit board (PCB), significantly adds to the overall assembly time. In this paper, as a practical application, we focus on a discrete particle swarm optimization (DPSO) algorithm for multihead SMM which is used to minimize the number of nozzle change operations and pick and place operations simultaneously. To evaluate the performance of the proposed algorithm, we test it on assembly tasks of PCBs through simulations. The results of computer experiments show that this DPSO algorithm was superior to the standard PSO algorithm.

Yee Ming Chen

Chun-Ta Lin

Optimizing the Operation Sequence of  
                        a Multihead Surface Mounting Machine Using  
                        a Discrete Particle Swarm Optimization Algorithm

The particle swarm optimization (PSO) algorithm is designed to find a single
optimal solution and needs some modifications to be able to locate multiple optima on a multimodal function. In parallel with evolutionary computation algorithms, these modifications can be grouped in the framework
of niching. In this work, we present a new approach to niching in PSO based on
clustering particles to identify niches. The neighborhood structure, on which particles rely
for communication, is exploited together with the niche information to locate multiple optima
in parallel. Our approach was implemented in the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>k</mml:mi></mml:math>-means-based PSO (<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>k</mml:mi></mml:math>PSO), which employs
the standard <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>k</mml:mi></mml:math>-means clustering algorithm, improved with a mechanism to adaptively
identify the number of clusters. <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>k</mml:mi></mml:math>PSO proved to be a competitive solution when compared with other existing algorithms, since it showed better performance on a benchmark set of multimodal functions.

Alessandro Passaro

Antonina Starita

Particle Swarm Optimization for Multimodal Functions:  A Clustering Approach

A discrete version of particle swarm optimization (DPSO) is employed to solve uncapacitated facility location (UFL) problem which is one of the most widely studied in combinatorial optimization. In addition, a hybrid version with a local search is defined to get more efficient results. The results are compared with a continuous particle swarm optimization (CPSO) algorithm and two other metaheuristics studies, namely, genetic algorithm (GA) and evolutionary simulated annealing (ESA). To make a reasonable comparison, we applied to same benchmark suites that are collected from OR-library. In conclusion, the results showed that DPSO algorithm is slightly better than CPSO algorithm and competitive with GA and ESA.

Ali R. Guner

Mehmet Sevkli

A Discrete Particle Swarm Optimization Algorithm for Uncapacitated Facility Location Problem

A multidisciplinary-multiobjective
optimization of aerocapture maneuvers is presented. The proposed approach
allows a detailed analysis of the coupling among vehicle's shape, trajectory
control, and thermal protection system design. A set of simplified models are
developed to address this analysis and a multiobjective particle swarm
optimizer is adopted to obtain the set of Pareto optimal solutions. In order to
deal with an unconstrained multiobjective optimization, a
two-point boundary value problem is formulated to
implicitly satisfy the constraints on the atmospheric exit conditions. The
trajectories of the most promising solutions are further optimized in a more
refined dynamical system by solving an optimal control problem using a direct
multiple shooting transcription method. Furthermore, a more complete vehicle
control is considered. All the simulations presented consider an aerocapture at
Mars with a polar orbit of 200 km of altitude as target orbit.

Roberto Armellin

Mich&#x000E8;le Lavagna

Multidisciplinary Optimization of Aerocapture Maneuvers

This study proposes an orthogonal momentum-type 
        particle swarm optimization (PSO) that finds good 
        solutions to global optimization problems using a delta 
        momentum rule to update the flying velocity of particles 
        and incorporating a fractional factorial design (FFD) via 
        several factorial experiments to determine the best 
        position of particles. The novel combination of the 
        momentum-type PSO and FFD is termed as the momentum-type PSO 
        with FFD herein. The momentum-type PSO modifies the 
        velocity-updating equation of the original Kennedy and 
        Eberhart PSO, and the FFD incorporates classical 
        orthogonal arrays into a velocity-updating equation for 
        analyzing the best factor associated with cognitive 
        learning and social learning terms. Twelve widely used 
        large parameter optimization problems were used to 
        evaluate the performance of the proposed PSO with the 
        original PSO, momentum-type PSO, and original PSO with 
        FFD. Experimental results reveal that the proposed 
        momentum-type PSO with an FFD algorithm efficiently solves 
        large parameter optimization problems.

Jenn-Long Liu

Chao-Chun Chang

Novel Orthogonal Momentum-Type Particle Swarm Optimization Applied to Solve Large  Parameter Optimization Problems

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Journal of Artificial Evolution and Applications

Particle Swarms: The Second Decade

Particle Swarms: The Second Decade

Articles

A Hybrid PSO/ACO Algorithm for Discovering Classification Rules in Data Mining

Particle Swarms: The Second Decade

The Generalized PSO: A New Door to PSO Evolution

Forma Analysis of Particle Swarm Optimisation for Permutation Problems

Inverse Parameter Identification Technique Using PSO Algorithm Applied to Geotechnical Modeling

Optimizing the Operation Sequence of a Multihead Surface Mounting Machine Using a Discrete Particle Swarm Optimization Algorithm

Particle Swarm Optimization for Multimodal Functions: A Clustering Approach

A Discrete Particle Swarm Optimization Algorithm for Uncapacitated Facility Location Problem

Multidisciplinary Optimization of Aerocapture Maneuvers

Novel Orthogonal Momentum-Type Particle Swarm Optimization Applied to Solve Large Parameter Optimization Problems