Multiple parameter control for ant colony optimization applied to feature selection problem

Gang, Wang; Chu, Hai-Cheng; Zhang, Yuxuan; Chen, Huiling; Wei-tong, HU; Li, Ying; Peng, Xinhua

doi:10.1007/s00521-015-1829-8

Cited by 23 publications

(6 citation statements)

References 33 publications

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“…The ACO algorithm is widely applied to solve the problem of high-dimensional feature selection (Ma et al, 2021). For instance, a fuzzy adaptive ACO was proposed by Wang et al (2015). Could achieve better classification results under specific conditions.…”

Section: Introductionmentioning

confidence: 99%

A developed ant colony algorithm for cancer molecular subtype classification to reveal the predictive biomarker in the renal cell carcinoma

XIN¹,

MA²,

SONG³

et al. 2023

BIOCELL

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Background: Recently, researchers have been attracted in identifying the crucial genes related to cancer, which plays important role in cancer diagnosis and treatment. However, in performing the cancer molecular subtype classification task from cancer gene expression data, it is challenging to obtain those significant genes due to the high dimensionality and high noise of data. Moreover, the existing methods always suffer from some issues such as premature convergence. Methods: To address those problems, we propose a new ant colony optimization (ACO) algorithm called DACO to classify the cancer gene expression datasets, identifying the essential genes of different diseases. In DACO, first, we propose the initial pheromone concentration based on the weight ranking vector to accelerate the convergence speed; then, a dynamic pheromone volatility factor is designed to prevent the algorithm from getting stuck in the local optimal solution; finally, the pheromone update rule in the Ant Colony System is employed to update the pheromone globally and locally. To demonstrate the performance of the proposed algorithm in classification, different existing approaches are compared with the proposed algorithm on eight high-dimensional cancer gene expression datasets. Results: The experiment results show that the proposed algorithm performs better than other effective methods in terms of classification accuracy and the number of feature sets. It can be used to address the classification problem effectively. Moreover, a renal cell carcinoma dataset is employed to reveal the biological significance of the proposed algorithm from a number of biological analyses. Conclusion: The results demonstrate that CAPS may play a crucial role in the occurrence and development of renal clear cell carcinoma.

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Section: Introductionmentioning

confidence: 99%

A developed ant colony algorithm for cancer molecular subtype classification to reveal the predictive biomarker in the renal cell carcinoma

XIN¹,

MA²,

SONG³

et al. 2023

BIOCELL

View full text Add to dashboard Cite

show abstract

“…where ρ is the evaporation rate, m is the number of ants and ΔT i j k is pheromone quality laid on edge (i,j) by kth ant [50,51]. The main reason for choosing ACO in our work is that it facilitates the building of candidate solutions as proposed to only facilitating the solution space exploration.…”

Section: Ant Colony Optimisation Algorithmmentioning

confidence: 99%

“…After building the solution the pheromone values in the arcs are updated according to the following equation:

T_{i j} = ()(, 1 - ρ) T_{i j} + normalΔ T_{i j}

where T ij is the amount of pheromone on a given edge ij , ρ is the rate of pheromone evaporation and Δ T ij is the amount of pheromone deposited typically given by

normalΔ T_{i j}^{k} = \frac{1}{L_{k}} normalif normalant k normaltravel normalon normaledge i, j

where

L_{k}

is the cost of the k th ant's tour. Pheromone values are updated by ants that have completed the tour

T_{i j} = ()(, 1 - ρ) T_{i j} + \sum_{k = 1}^{m} normalΔ T_{i j}^{k}

where ρ is the evaporation rate, m is the number of ants and

normalΔ T_{i j}^{k}

is pheromone quality laid on edge ( i , j ) by k th ant [50, 51]. The main reason for choosing ACO in our work is that it facilitates the building of candidate solutions as proposed to only facilitating the solution space exploration.…”

Section: Ant Colony Optimisation Algorithmmentioning

confidence: 99%

Efficient improved ant colony optimisation algorithm for dynamic software rejuvenation in web services

2020

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Software rejuvenation is an effective technique to counteract software ageing in continuously-running applications such as web-service-based systems. In a client-server application, where the server is intended to run perpetually, rejuvenation of the server process periodically during the server idle times increases the availability of that service. In these systems, web services are allocated based on the receiver's requirements and server's facilities. Since the selection of a server among candidates while maintaining the optimal quality of service is an NP-hard problem, meta-heuristics seems to be suitable. In this study, the proposed dynamic software rejuvenation as a proactive fault-tolerance technique based on a combination of ant colony optimisation (ACO) and gravitational emulation local search (GELS) so as to determine the optimal times when rejuvenation can be performed and failure rate can be minimised. The newly proposed method combined the public search capabilities of ACO with local search of GELS algorithm in an effort to create a stable algorithm, which can make reaching the global optimum largely possible in the proposed work. The simulation results revealed that the proposed strategy can decrease the failure rate of web services averagely by 28% in comparison with genetic algorithm and decision-tree strategies.

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“…The accuracy of SVM classifier obtained on validation samples is used as a fitness value and it is evaluated on the Indian Pines hyperspectral data set. Gang Wang et al, in [21] have explored the use of ACO by adaptively adjusting its parameters (such as pheromone evaporation rate, number of ants and exploration probability factor) for feature selection. The results are evaluated on 10 different datasets and performance is compared with GA, PSO, ACO, fuzzy adaptive ant system.…”

Section: The Current State-of-the-art In Metaheuristic Algorithmsmentioning

confidence: 99%

Binary Multi-Verse Optimization (BMVO) Approaches for Feature Selection

Hans¹,

Kaur²

2020

IJIMAI

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Multi-Verse Optimization (MVO) is one of the newest meta-heuristic optimization algorithms which imitates the theory of Multi-Verse in Physics and resembles the interaction among the various universes. In problem domains like feature selection, the solutions are often constrained to the binary values viz. 0 and 1. With regard to this, in this paper, binary versions of MVO algorithm have been proposed with two prime aims: firstly, to remove redundant and irrelevant features from the dataset and secondly, to achieve better classification accuracy. The proposed binary versions use the concept of transformation functions for the mapping of a continuous version of the MVO algorithm to its binary versions. For carrying out the experiments, 21 diverse datasets have been used to compare the Binary MVO (BMVO) with some binary versions of existing metaheuristic algorithms. It has been observed that the proposed BMVO approaches have outperformed in terms of a number of features selected and the accuracy of the classification process.

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Multiple parameter control for ant colony optimization applied to feature selection problem

Cited by 23 publications

References 33 publications

A developed ant colony algorithm for cancer molecular subtype classification to reveal the predictive biomarker in the renal cell carcinoma

A developed ant colony algorithm for cancer molecular subtype classification to reveal the predictive biomarker in the renal cell carcinoma

Efficient improved ant colony optimisation algorithm for dynamic software rejuvenation in web services

Binary Multi-Verse Optimization (BMVO) Approaches for Feature Selection

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