A hierarchical heterogeneous ant colony optimization based approach for efficient action rule mining

Sreeja, N. K.; Ashok, S.

doi:10.1016/j.swevo.2016.02.004

Cited by 13 publications

(5 citation statements)

References 9 publications

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“…Because of its diversity, ACO has been applied in a wide variety of studies [10]- [11], [103]- [105] including supervised learning models, such as classification rules [12]- [17]. In this paper, we focus on the ACO algorithm with SVM [18]- [20], and they both together have been applied to several optimization problems [21]- [22]. This research aims at the development of a new hybrid algorithm ACOFTF which considers important QoS metrics such as SLA violations, migration time, throughput time, overhead time, and optimization time.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Model for Load Balancing in Cloud Using File Type Formatting

et al. 2020

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Maintaining accuracy in load balancing using metaheuristics is a difficult task even with the help of recent hybrid approaches. In the existing literature, various optimized metaheuristic approaches are being used to achieve their combined benefits for proper load balancing in the cloud. These approaches often adopt multi-objective QoS metrics, such as reduced SLA violations, reduced makespan, high throughput, low overload, low energy consumption, high optimization, minimum migrations, and higher response time. The cloud applications are generally computation-intensive and can grow exponentially in memory with the increase in size if no proper effective and efficient load balancing technique is adopted resulting in poor quality solutions. To provide a better load balancing solution in cloud computing, with extensive data, a new hybrid model is being proposed that performs classification on the number of files present in the cloud using file type formatting. The classification is performed using Support Vector Machine (SVM) considering various file formats such as audio, video, text maps, and images in the cloud. The resultant data class provides high classification accuracy which is further fed into a metaheuristic algorithm namely Ant Colony Optimization (ACO) using File Type Formatting FTF for better load balancing in the cloud. Frequently used QoS metrics, such as SLA violations, migration time, throughput time, overhead time, and optimization time are evaluated in the cloud environment and comparative analysis is performed with recent metaheuristics, such as Ant Colony Optimization-Particle Swarm Optimization (ACOPS), Chaotic Particle Swarm Optimization (CPSO), Q-learning Modified Particle Swarm Optimization (QMPSO), Cat Swarm Optimization (CSO) and D-ACOELB. The proposed algorithm outperforms them and provides good performance with scalability and robustness.

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Section: Introductionmentioning

confidence: 99%

A Hybrid Model for Load Balancing in Cloud Using File Type Formatting

et al. 2020

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“…ACO has been applied to a wide variety of domains [41]- [44], including supervised learning using various learning models, such as classification rules [29], [45]- [50], decision trees [51], [52], and various types of Bayesian network classifiers [53]- [57].…”

Section: Review Of the Aco R Algorithmmentioning

confidence: 99%

Parameter Self-Adaptation in an Ant Colony Algorithm for Continuous Optimization

Abdelbar

Salama

2019

IEEE Access

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ACO R is a well-established ant colony optimization algorithm for continuous-domain optimization. We present an approach for the dynamic adaptation of the ACO R algorithm's controlling parameters, focusing on the search width parameter, based on using several pre-specified parameter configurations, which we call personalities. Before an ant starts to generate a candidate solution, it stochastically adopts a personality based on the relative past success of different personalities. The success of a personality is measured, in turn, by the survival rate of the previous solutions generated by the ants adopting that personality. The premise of our approach is that some personalities will be more appropriate than others for different phases of the search. In addition, our adaptive approach can accommodate solution recombination, the use of which within ACO R was recently explored in the previous work. It allows the frequency of applying recombination and the type of recombination operator to be dynamically adapted, by having one or more recombination personalities among the competing personalities. We evaluate these proposals experimentally on two applications: 1) training feedforward neural networks for classification using 65 benchmark datasets from the University of California Irvine repository and 2) optimizing several popular synthetic benchmark continuous-domain functions. Our experimental results indicate that our proposals perform better than the standard ACO R on both applications, to a statistically significant extent.

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“…Meanwhile, some attempts have been made to strengthen the performance of ACO algorithms by the collaborative work between multiple ant colonies. Sreeja and Sankar [40] proposed a hierarchical heterogeneous ant colony optimization with different ant agents and the minimal cost action rules to reduce time cost. Zhang et al [41] proposed a dynamic multi-role adaptive collaborative ant colony optimization (MRCACO) based on heterogeneous multi-colony and multi-role adaptive cooperation mechanism.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Improved Ant Colony System Based on Population Information Entropy for Path Planning of Mobile Robot

Zhang

2021

IEEE Access

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In this paper, an adaptive improved ant colony algorithm based on population information entropy(AIACSE) is proposed to improve the optimization ability of the algorithm. The diversity of the population in the iterative process is described by the information entropy. The non-uniform distribution initial pheromone is constructed to reduce the blindness of the search at the starting phase. The pheromone diffusion model is used to enhance the exploration and collaboration capacity between ants. The adaptive parameter adjusting strategy and the novel pheromone updating mechanism based on the evolutionary characteristics of the population are designed to achieve a better balance between exploration of the search space and exploitation of the knowledge during the optimization progress. The performance of AIACSE is evaluated on the path planning of mobile robots. Friedman's test is further conducted to check the significant difference in performance between AIACSE and the other selected algorithms. The experimental results and statistical tests demonstrate that the presented approach significantly improves the performance of the ant colony system (ACS) and outperforms the other algorithms used in the experiments.INDEX TERMS Ant colony optimization, path planning, mobile robot, grid map, pheromone diffusion model, parameter adjusting strategy, pheromone updating strategy, population information entropy.

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A hierarchical heterogeneous ant colony optimization based approach for efficient action rule mining

Cited by 13 publications

References 9 publications

A Hybrid Model for Load Balancing in Cloud Using File Type Formatting

A Hybrid Model for Load Balancing in Cloud Using File Type Formatting

Parameter Self-Adaptation in an Ant Colony Algorithm for Continuous Optimization

An Adaptive Improved Ant Colony System Based on Population Information Entropy for Path Planning of Mobile Robot

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