A new hybrid GA−ACO−PSO algorithm for solving various engineering design problems

Tam, Jun Hui; Ong, Zhi Chao; Ismail, Zubaidah; Ang, Bee Chin; Khoo, Shin Yee

doi:10.1080/00207160.2018.1463438

Cited by 48 publications

(12 citation statements)

References 47 publications

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“…The performance comparison has been done with three sate of art algorithms i.e. SVM classifier (without any optimization technique), Firefly (SVM with Firefly optimization [14]) [1], ACO-PSO-GA (SVM with hybrid ACO-PSO-GA optimization) [9] using sensitivity, specificity, and classification accuracy as the parameters. The analysis parameters i.e.…”

Section: Resultsmentioning

confidence: 99%

Implementation of Hybrid ACO-PSO-GA-DE Algorithm for Mammogram Classification

Bala¹,

Priti²

2019

IJRTE

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Breast Cancer is one of the fastest growing cancer that causes women to death in the world. The early detection of breast cancer improves the chances of its cure. The malignant tumor that is the sign of breast cancer can be detected by mammography. This paper develops a technique to classify the mammogram images as normal, benign or malignant. This paper applies HAPGD (Hybrid ACO (Ant Colony Optimization), PSO (Particle Swarm Optimization), GA (Genetic Algorithm), and DE (Differential Evolution)) classification algorithm to texture features extracted from the mammogram image. The analysis has been done on the DDSM and MIAS dataset by using classification accuracy, specificity, and sensitivity as the parameter with three state of art algorithms i.e. SVM classifier (without any optimization technique), Firefly (SVM with Firefly optimization), ACO-PSO-GA (SVM with hybrid ACO-PSO-GA optimization). The improvement in the performance measures against three state of art techniques shows the significance of the algorithm.

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Section: Resultsmentioning

confidence: 99%

Implementation of Hybrid ACO-PSO-GA-DE Algorithm for Mammogram Classification

Bala¹,

Priti²

2019

IJRTE

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“…As a swarm intelligence optimization algorithm, PSO has many advantages, such as stability, a short time convergence, few parameters to adjust and ease of implementation. PSO has been successfully applied to the combination optimization of various engineering problem areas, such as data mining [47], artificial neural network training [48], vehicle path planning [49], medical diagnosis [50,51] and system and engineering design [52].…”

Section: Pso Algorithmmentioning

confidence: 99%

A Weighted Ensemble Learning Algorithm Based on Diversity Using a Novel Particle Swarm Optimization Approach

et al. 2020

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In ensemble learning, accuracy and diversity are the main factors affecting its performance. In previous studies, diversity was regarded only as a regularization term, which does not sufficiently indicate that diversity should implicitly be treated as an accuracy factor. In this study, a two-stage weighted ensemble learning method using the particle swarm optimization (PSO) algorithm is proposed to balance the diversity and accuracy in ensemble learning. The first stage is to enhance the diversity of the individual learner, which can be achieved by manipulating the datasets and the input features via a mixed-binary PSO algorithm to search for a set of individual learners with appropriate diversity. The purpose of the second stage is to improve the accuracy of the ensemble classifier using a weighted ensemble method that considers both diversity and accuracy. The set of weighted classifier ensembles is obtained by optimization via the PSO algorithm. The experimental results on 30 UCI datasets demonstrate that the proposed algorithm outperforms other state-of-the-art baselines.

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“…The main motive of this hybrid algorithm is to remove the limitation of individual algorithms by combining the strengths of each algorithm. The exploration strength of ACO is combined with exploitation property of PSO which are being balanced by the GA [10]. The crossover phase of GA is replaced DE perturbation phase to improve the convergence of the overall algorithm to produce better results in less iteration.…”

Section: Hybrid Aco-pso-ga-de Algorithmmentioning

confidence: 99%

“…The performance of HAPGD algorithm is compared with hybrid meta-heuristic algorithms i.e. ACO-PSO [12], ACO-GA [13], PSO-GA [14], GA-DE [15] and ACO-PSO-GA [10] using the parameters described above on 7 datasets given in table 1 has been done in this section. The results are evaluated by executing the algorithms 20 times and taking the average of results.…”

Section: Performance Analysismentioning

confidence: 99%

Hybrid ACO-PSO-GA-DE Algorithm for Big Data Classification

2019

ijrte

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This paper designs a technique to classify big data efficiently. This work considers the processing of big data as an optimization problem due to the trade-off between accuracy and time and solves this optimization problem by using a meta-heuristic approach. The HAPGD (Hybrid ACO (Ant Colony Optimization), PSO (Particle Swarm Optimization), GA (Genetic Algorithm), and DE (Differential Evolution)) classification algorithm is designed by using the support vector machine (SVM) along with hybrid ACO-PSO-GA-DE algorithm that hybrids exploration capability of ACO with exploitation capability of PSO whose balance is maintained using modified GA. The GA has been modified by using the DE algorithm. The presented technique performs classification efficiently as shown in results on seven datasets using different analysis parameters due to balanced exploration and exploitation search with fast convergence

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A new hybrid GA−ACO−PSO algorithm for solving various engineering design problems

Cited by 48 publications

References 47 publications

Implementation of Hybrid ACO-PSO-GA-DE Algorithm for Mammogram Classification

Implementation of Hybrid ACO-PSO-GA-DE Algorithm for Mammogram Classification

A Weighted Ensemble Learning Algorithm Based on Diversity Using a Novel Particle Swarm Optimization Approach

Hybrid ACO-PSO-GA-DE Algorithm for Big Data Classification

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