An efficient heuristic algorithm for software module clustering optimization

Yuste, Javier; Duarte, Abraham; Pardo, Eduardo G.

doi:10.1016/j.jss.2022.111349

Cited by 8 publications

(4 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A reverse engineering technique known as software source code clustering organises software modules with similar characteristics into groups. Te quantity of connections within and across clusters, known as cohesion, is employed to evaluate the modularization quality (MQ) [3,4]. Te efciency of structural models generated by module clustering algorithms is evaluated using the MQ criterion, which posits improved clustering results from greater cohesion (internal links within a cluster) and reduced coupling.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Modified Horse Herd Optimization Algorithm and Its Application in the Program Source Code Clustering

Arasteh,

Gunes,

Bouyer

et al. 2023

Complexity

View full text Add to dashboard Cite

Maintenance is one of the costliest phases in the software development process. If architectural design models are accessible, software maintenance can be made more straightforward. When the software’s source code is the only available resource, comprehending the program profoundly impacts the costs associated with software maintenance. The primary objective of comprehending the source code is extracting information used during the software maintenance phase. Generating a structural model based on the program source code is an effective way of reducing overall software maintenance costs. Software module clustering is considered a tremendous reverse engineering technique for constructing structural design models from the program source code. The main objectives of clustering modules are to reduce the quantity of connections between clusters, increase connections within clusters, and improve the quality of clustering. Finding the perfect clustering model is considered an NP-complete problem, and many previous approaches had significant issues in addressing this problem, such as low success rates, instability, and poor modularization quality. This paper applied the horse herd optimization algorithm, a distinctive population-based and discrete metaheuristic technique, in clustering software modules. The proposed method’s effectiveness in addressing the module clustering problem was examined by ten real-world standard software test benchmarks. Based on the experimental data, the quality of the clustered models produced is approximately 3.219, with a standard deviation of 0.0718 across the ten benchmarks. The proposed method surpasses former methods in convergence, modularization quality, and result stability. Furthermore, the experimental results demonstrate the versatility of this approach in effectively addressing various real-world discrete optimization challenges.

show abstract

Section: Introductionmentioning

confidence: 99%

“…Notably, the clusters do not intersect and the combination of these m clusters encompasses the entire source code (S). Consequently, the problem of SMC is technically categorised as NP-complete [3][4][5]. Terefore, the application of metaheuristic methods to select the appropriate grouping is required.…”

Section: Introductionmentioning

confidence: 99%

A Modified Horse Herd Optimization Algorithm and Its Application in the Program Source Code Clustering

Arasteh,

Gunes,

Bouyer

et al. 2023

Complexity

View full text Add to dashboard Cite

show abstract

“…Moreover, the aim of heuristic algorithms is to utilize various methods in local examinations and techniques of randomization [ 11 ]. Further analysis and advancements were made in heuristic algorithms and converted to metaheuristic algorithms [ 12 , 13 ]. The novel collections of algorithms have better performance than heuristic algorithms; accordingly, the affix of “meta” that means “far off” was linked with these algorithms.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improved Fitness-Dependent Optimizer for Solving Economic Load Dispatch Problem

Tahir

Rashid

Rauf

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Economic load dispatch depicts a fundamental role in the operation of power systems, as it decreases the environmental load, minimizes the operating cost, and preserves energy resources. The optimal solution to economic load dispatch problems and various constraints can be obtained by evolving several evolutionary and swarm-based algorithms. The major drawback to swarm-based algorithms is premature convergence towards an optimal solution. Fitness-dependent optimizer is a novel optimization algorithm stimulated by the decision-making and reproductive process of bee swarming. Fitness-dependent optimizer (FDO) examines the search spaces based on the searching approach of particle swarm optimization. To calculate the pace, the fitness function is utilized to generate weights that direct the search agents in the phases of exploitation and exploration. In this research, the authors have used a fitness-dependent optimizer to solve the economic load dispatch problem by reducing fuel cost, emission allocation, and transmission loss. Moreover, the authors have enhanced a novel variant of the fitness-dependent optimizer, which incorporates novel population initialization techniques and dynamically employed sine maps to select the weight factor for the fitness-dependent optimizer. The enhanced population initialization approach incorporates a quasi-random Sabol sequence to generate the initial solution in the multidimensional search space. A standard 24-unit system is employed for experimental evaluation with different power demands. The empirical results obtained using the enhanced variant of the fitness-dependent optimizer demonstrate superior performance in terms of low transmission loss, low fuel cost, and low emission allocation compared to the conventional fitness-dependent optimizer. The experimental study obtained 7.94E−12, the lowest transmission loss using the enhanced fitness-dependent optimizer. Correspondingly, various standard estimations are used to prove the stability of the fitness-dependent optimizer in phases of exploitation and exploration.

show abstract