Hybrid of genetic algorithm and krill herd for software clustering problem

Akbari, Mohammad; Izadkhah, Habib

doi:10.1109/kbei.2019.8734939

Cited by 10 publications

(4 citation statements)

References 15 publications

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“…The major cause for presenting the change is that the GA approach does not provide optimal outcomes when compared with alternate approaches as KH lacks robust data flow and KH genetic operators are unfit in exhibiting the final outcomes. The major intension of this framework is to resolve the vulnerability of KH and GA and solve the clustering issues by KH [18]. The steps involved in newly presented approaches are given in the following.…”

Section: Hybridization Of Ga With Kh Algorithmmentioning

confidence: 99%

Hybridization of Metaheuristics Based Energy Efficient Scheduling Algorithm for Multi-Core Systems

Justus¹,

Sakthi²,

Priyadarshini³

et al. 2023

Computer Systems Science and Engineering

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The developments of multi-core systems (MCS) have considerably improved the existing technologies in the field of computer architecture. The MCS comprises several processors that are heterogeneous for resource capacities, working environments, topologies, and so on. The existing multi-core technology unlocks additional research opportunities for energy minimization by the use of effective task scheduling. At the same time, the task scheduling process is yet to be explored in the multi-core systems. This paper presents a new hybrid genetic algorithm (GA) with a krill herd (KH) based energy-efficient scheduling technique for multi-core systems (GAKH-SMCS). The goal of the GAKH-SMCS technique is to derive scheduling tasks in such a way to achieve faster completion time and minimum energy dissipation. The GAKH-SMCS model involves a multiobjective fitness function using four parameters such as makespan, processor utilization, speedup, and energy consumption to schedule tasks proficiently. The performance of the GAKH-SMCS model has been validated against two datasets namely random dataset and benchmark dataset. The experimental outcome ensured the effectiveness of the GAKH-SMCS model interms of makespan, processor utilization, speedup, and energy consumption. The overall simulation results depicted that the presented GAKH-SMCS model achieves energy efficiency by optimal task scheduling process in MCS.

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Section: Hybridization Of Ga With Kh Algorithmmentioning

confidence: 99%

Hybridization of Metaheuristics Based Energy Efficient Scheduling Algorithm for Multi-Core Systems

Justus¹,

Sakthi²,

Priyadarshini³

et al. 2023

Computer Systems Science and Engineering

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“…Table 5 shows the ranking of the active countries and organizations, including the name of country, organization, participating researchers, reference to the published papers, and the total number of papers. Jitender Kumar Chhabra, Amarjeet Prajapati, and Amit Rathee [27], [28], [29], [30], [31], [32], [33], [34], [35], [36] 10 Italy University of Basilicata Giuseppe Scanniello [37], [38], [39], [40], [41], [42], [43], [44] 8 Iran University of Tabriz Habib Izadkhah, Hamid Masoud, and Ayaz Isazadeh [31], [45], [46], [47], [], [48], [49] 7…”

Section: Active Researchers Organizations and Countries (Rq2)mentioning

confidence: 99%

“…[3], [7], [27], [32], [33], [34], [37], [47], [50], [55], [56], [57], [58], [66], [67], [68], [70], [72], [74], [110], [111], [112], [113], [114], [115], [116], [117], [118], [119], [120], [54], [121], [122], [123], [124], [125], [126], [127], [128], [129], [130], [131], [132], [133] A3…”

Section: B Factbase Source Selection (Rq6)mentioning

confidence: 99%

Software Module Clustering: An In-Depth Literature Analysis

Sarhan¹,

Ahmed²,

Bureš³

et al. 2020

Preprint

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Software module clustering is an unsupervised learning method used to cluster software entities (e.g., classes, modules, or files) with similar features. The obtained clusters may be used to study, analyze, and understand the software entities' structure and behavior. Implementing software module clustering with optimal results is challenging. Accordingly, researchers have addressed many aspects of software module clustering in the past decade. Thus, it is essential to present the research evidence that has been published in this area. In this study, 143 research papers from well-known literature databases that examined software module clustering were reviewed to extract useful data. The obtained data were then used to answer several research questions regarding state-of-the-art clustering approaches, applications of clustering in software engineering, clustering processes, clustering algorithms, and evaluation methods. Several research gaps and challenges in software module clustering are discussed in this paper to provide a useful reference for researchers in this field.

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“…Most search-based software clustering algorithms use BasicMQ and TurboMQ as objective function such as E-CDGM [6], EDA [7], Bunch [4,8], DAGC [9], SAHC [8], NAHC [8], HC+Bunch [5], modified firefly algorithm [14], MAEA-SMCP [15] and GAKH [16]. The limitations of using these objective functions are mentioned before.…”

Section: Related Workmentioning

confidence: 99%

Information Theoretic Objective Function for Genetic Software Clustering

Izadkhah

Tajgardan

2019

The 5th International Electronic Conference on Entropy and Its Applications

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Software clustering is usually used for program comprehension. Since it is considered to be the most crucial NP-complete problem, several genetic algorithms have been proposed to solve this problem. In the literature, there exist some objective functions (i.e., fitness functions) which are used by genetic algorithms for clustering. These objective functions determine the quality of each clustering obtained in the evolutionary process of the genetic algorithm in terms of cohesion and coupling. The major drawbacks of these objective functions are the inability to (1) consider utility artifacts, and (2) to apply to another software graph such as artifact feature dependency graph. To overcome the existing objective functions' limitations, this paper presents a new objective function. The new objective function is based on information theory, aiming to produce a clustering in which information loss is minimized. For applying the new proposed objective function, we have developed a genetic algorithm aiming to maximize the proposed objective function. The proposed genetic algorithm, named ILOF, has been compared to that of some other well-known genetic algorithms. The results obtained confirm the high performance of the proposed algorithm in solving nine software systems. The performance achieved is quite satisfactory and promising for the tested benchmarks.

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Hybrid of genetic algorithm and krill herd for software clustering problem

Cited by 10 publications

References 15 publications

Hybridization of Metaheuristics Based Energy Efficient Scheduling Algorithm for Multi-Core Systems

Hybridization of Metaheuristics Based Energy Efficient Scheduling Algorithm for Multi-Core Systems

Software Module Clustering: An In-Depth Literature Analysis

Information Theoretic Objective Function for Genetic Software Clustering

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