Particle Swarm Algorithm variants for the Quadratic Assignment Problems - A probabilistic learning approach

Hafiz, Faizal; Abdennour, Adel

doi:10.1016/j.eswa.2015.09.032

Cited by 45 publications

(32 citation statements)

References 49 publications

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“…It is, therefore, possible to adapt most of the existing PSO variants [46] in the continuous domain (x ∈ R) for the feature selection problems (x ∈ N) following the 2D-learning. However, the results of the study in [47] indicate that among popular PSO variants, adapted Unified Particle Swarm Optimization (UPSO) [48] performs comparatively better for the problems in the discrete domain. For this reason, in this work, UPSO has been adapted following the 2D-learning approach and referred throughout the manuscript as '2D-UPSO'.…”

Section: Two-dimensional (2d) Learning Framework For Particle Swarmsmentioning

confidence: 99%

Efficient feature selection of power quality events using two dimensional (2D) particle swarms

Hafiz

Swain

Naik

et al. 2019

Applied Soft Computing

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A novel two-dimensional (2D) learning framework has been proposed to address the feature selection problem in Power Quality (PQ) events. Unlike the existing feature selection approaches, the proposed 2D learning explicitly incorporates the information about the subset cardinality (i.e., the number of features) as an additional learning dimension to effectively guide the search process. The efficacy of this approach has been demonstrated considering fourteen distinct classes of PQ events which conform to the IEEE Standard 1159. The search performance of the 2D learning approach has been compared to the other six well-known feature selection wrappers by considering two induction algorithms: Naive Bayes (NB) and k-Nearest Neighbors (k-NN). Further, the robustness of the selected/reduced feature subsets has been investigated considering seven different levels of noise. The results of this investigation convincingly demonstrate that the proposed 2D learning can identify significantly better and robust feature subsets for PQ events.

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Section: Two-dimensional (2d) Learning Framework For Particle Swarmsmentioning

confidence: 99%

Efficient feature selection of power quality events using two dimensional (2D) particle swarms

Hafiz

Swain

Naik

et al. 2019

Applied Soft Computing

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“…It uses real-numbers randomness and global communication among the swarm particles rather than use the mutation, crossover or pheromone. Due to its simplicity and attractive search efficiency, PSO has been successfully applied to various domains such as: combinatorial optimization [15], data mining [29], clustering [37], neural networks [32], image processing [17], scheduling [47], and fuzzy logic [26].…”

Section: The Necessity Of Soft Computing For Qe: Apso As An Interestimentioning

confidence: 99%

An accelerated PSO for query expansion in web information retrieval: application to medical dataset

Khennak

Drias

2017

Appl Intell

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Swarm intelligence algorithms are now among the most widely used soft computing techniques for optimization and computational intelligence. One recent swarm intelligence algorithm that has begun to receive more attention is Accelerated Particle Swarm Optimization (APSO). It is an enhanced version of PSO with global optimization capability, sufficient simplicity and high flexibility. In this paper, we propose the application of the APSO technique to efficiently solve the problem of Query Expansion (QE) in Web Information Retrieval (IR). Unlike prior studies, we introduce a new modelling of QE that aims to find the suitable expanded query from among a set of expanded query candidates. Nevertheless, due to the large number of potential expanded query candidates, it is extremely complex to produce the best one through conventional hard computing methods. Therefore, we propose to consider the problem of QE as a combinatorial optimization problem and address it with APSO. We thoroughly evaluate the proposed APSO for QE using MEDLINE, the world Web's largest medical library. We first conduct a preliminary experiment to tune the APSO parameters. Then, we compare the results to a recent swarm intelligence algorithm called Firefly Algorithm (FA). We also compare the results with three recently published methods for QE that involved Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Bat Algorithm (BA). The experimental analysis demonstrates that the proposed APSO for QE is very competitive and yields substantial improvement over the other methods in terms of retrieval effectiveness and computational complexity.

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“…The 2D learning approach was developed as a generalized learning framework to adapt any continuous PSO variant (i.e., x ∈ R) for the model selection task (i.e., x ∈ N). However, the results of comparative analysis of various PSO variants [14] indicate that Unified Particle Swarm Optimization (UPSO) [15] is more suitable for the problem in the discrete domain (i.e., x ∈ N) and therefore it is selected in this study.…”

Section: B Velocity Updatementioning

confidence: 99%

“…Update the position of the i th particle following Algorithm-1 14 end 15 Store the old fitness of the swarm in 'F ' 16 Evaluate the swarm fitness To integrate the information about both the cardinality and the attributes/terms, the position update is carried out in two stages. In the first stage, the cardinality of the new position is determined.…”

Section: Position Updatementioning

confidence: 99%

Structure Selection of Polynomial NARX Models Using Two Dimensional (2D) Particle Swarms

Hafiz

Swain

Mendes

et al. 2018

2018 IEEE Congress on Evolutionary Computation (CEC)

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The present study applies a novel two-dimensional learning framework (2D-UPSO) based on particle swarms for structure selection of polynomial nonlinear auto-regressive with exogenous inputs (NARX) models. This learning approach explicitly incorporates the information about the cardinality (i.e., the number of terms) into the structure selection process. Initially, the effectiveness of the proposed approach was compared against the classical genetic algorithm (GA) based approach and it was demonstrated that the 2D-UPSO is superior. Further, since the performance of any meta-heuristic search algorithm is critically dependent on the choice of the fitness function, the efficacy of the proposed approach was investigated using two distinct information theoretic criteria such as Akaike and Bayesian information criterion. The robustness of this approach against various levels of measurement noise is also studied. Simulation results on various nonlinear systems demonstrate that the proposed algorithm could accurately determine the structure of the polynomial NARX model even under the influence of measurement noise.Index Terms-Nonlinear system identification, structure selection, NARX model, particle swarm † -'r' denotes the ratio of number of redundant terms to the total number of terms ‡ {T1, . . . T5} ∈ Tsystem denote the system term and are given in Appendix A

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Particle Swarm Algorithm variants for the Quadratic Assignment Problems - A probabilistic learning approach

Cited by 45 publications

References 49 publications

Efficient feature selection of power quality events using two dimensional (2D) particle swarms

Efficient feature selection of power quality events using two dimensional (2D) particle swarms

An accelerated PSO for query expansion in web information retrieval: application to medical dataset

Structure Selection of Polynomial NARX Models Using Two Dimensional (2D) Particle Swarms

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