Software Module Clustering Using Bio-Inspired Algorithms

2021 ◽  
pp. 788-813
Author(s):  
Kawal Jeet ◽  
Renu Dhir
Keyword(s):  
Optimization Problems ◽  
Hill Climbing ◽  
Software Systems ◽  
Human Beings ◽  
Software Module ◽  
Bee Colony ◽  
Large Numbers ◽  
Software Modularization ◽  
Software Module Clustering ◽  
Software Engineers

Nature has always been a source of inspiration for human beings. Large numbers of complex optimization problems have been solved by the techniques inspired by nature. Software modularization is one of such complex problems that have been encountered by software engineers. It is the process of organizing modules of a software system into optimal clusters. In this chapter, some bio-inspired algorithms such as bat, artificial bee colony, black hole and firefly algorithm have been proposed for the cause of software modularization. The hybrid of these algorithms with crossover and mutation operators of the genetic algorithm has also been proposed. All the algorithms along with their hybrids are tested on seven benchmark open source software systems. It has been evaluated from the results thus obtained that the hybrid of these algorithms proved to optimize better than the existing genetic and hill-climbing approaches.

Software Module Clustering Using Bio-Inspired Algorithms

2016 ◽  
pp. 445-470 ◽  
Author(s):  
Kawal Jeet ◽  
Renu Dhir
Keyword(s):  
Optimization Problems ◽  
Hill Climbing ◽  
Software Systems ◽  
Human Beings ◽  
Software Module ◽  
Bee Colony ◽  
Large Numbers ◽  
Software Modularization ◽  
Software Module Clustering ◽  
Software Engineers

Nature has always been a source of inspiration for human beings. Large numbers of complex optimization problems have been solved by the techniques inspired by nature. Software modularization is one of such complex problems that have been encountered by software engineers. It is the process of organizing modules of a software system into optimal clusters. In this chapter, some bio-inspired algorithms such as bat, artificial bee colony, black hole and firefly algorithm have been proposed for the cause of software modularization. The hybrid of these algorithms with crossover and mutation operators of the genetic algorithm has also been proposed. All the algorithms along with their hybrids are tested on seven benchmark open source software systems. It has been evaluated from the results thus obtained that the hybrid of these algorithms proved to optimize better than the existing genetic and hill-climbing approaches.

scholarly journals Software Module Clustering Using Grid-Based Many-Objective Particle Swarm Optimization

2021 ◽  
Author(s):  
Amarjeet Prajapati
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Optimization Algorithms ◽  
Particle Swarm ◽  
Poor Performance ◽  
Software Module ◽  
Swarm Optimization ◽  
Software Module Clustering ◽  
The Many ◽  
Grid Based

Abstract The poor performance of traditional multi-objective optimization algorithms over the many-objective optimization problems has led to the development of a variety of many-objective optimization algorithms. Recently, several many-objective optimization algorithms have been proposed to address the different class of many-objective optimization problems. Most of the existing many-objective optimization algorithms were designed from the perspective of synthetic many-objective optimization problems. Despite the tremendous work made in the development of the many-objective optimization algorithms for solving the synthetic many-objective optimization problems, still real-world many-objective optimization problems gained little attention. In this work, we propose a grid-based many-objective particle swarm optimization (GrMaPSO) for the many-objective software optimization problem. In this contribution, the grid-based selection strategies along with other supportive strategies such as two-archive storing and crowding distance have been exploited in the framework of particle swarm optimization. The performance of the proposed approach is evaluated and compared to three existing approaches over five problem instances. The results demonstrate that the proposed approach is more effective and has significant advantages over existing many-objective approaches designed for the software module clustering problems.

TA-ABC: Two-Archive Artificial Bee Colony for Multi-objective Software Module Clustering Problem

2018 ◽  
Vol 27 (4) ◽  
pp. 619-641 ◽  
Author(s):  
Amarjeet ◽  
Jitender Kumar Chhabra
Keyword(s):  
Artificial Bee Colony ◽  
Objective Functions ◽  
Cluster Approach ◽  
Pareto Dominance ◽  
Software Module ◽  
Nsga Ii ◽  
Multi Objective ◽  
Clustering Problem ◽  
Bee Colony ◽  
Software Module Clustering

Abstract Multi-objective software module clustering problem (M-SMCP) aims to automatically produce clustering solutions that optimize multiple conflicting clustering criteria simultaneously. Multi-objective evolutionary algorithms (MOEAs) have been a most appropriate alternate for solving M-SMCPs. Recently, it has been observed that the performance of MOEAs based on Pareto dominance selection technique degrades with multi-objective optimization problem having more than three objective functions. To alleviate this issue for M-SMCPs containing more than three objective functions, we propose a two-archive based artificial bee colony (TA-ABC) algorithm. For this contribution, a two-archive concept has been incorporated in the TA-ABC algorithm. Additionally, an improved indicator-based selection method is used instead of Pareto dominance selection technique. To validate the performance of TA-ABC, an empirical study is conducted with two well-known M-SMCPs, i.e. equal-size cluster approach and maximizing cluster approach, each containing five objective functions. The clustering result produced by TA-ABC is compared with existing genetic based two-archive algorithm (TAA) and non-dominated sorting genetic algorithm II (NSGA-II) over seven un-weighted and 10 weighted practical problems. The comparison results show that the proposed TA-ABC outperforms significantly TAA and NSGA-II in terms of modularization quality, coupling, cohesion, Pareto optimality, inverted generational distance, hypervolume, and spread performance metrics.

Comparison of Meta-heuristic Algorithms on Benchmark Functions

2019 ◽  
Vol 2 (3) ◽  
pp. 508-517
Author(s):  
FerdaNur Arıcı ◽  
Ersin Kaya
Keyword(s):  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Differential Evolution Algorithm ◽  
Population Based ◽  
Time Interval ◽  
Bee Colony ◽  
Different Characteristics

Optimization is a process to search the most suitable solution for a problem within an acceptable time interval. The algorithms that solve the optimization problems are called as optimization algorithms. In the literature, there are many optimization algorithms with different characteristics. The optimization algorithms can exhibit different behaviors depending on the size, characteristics and complexity of the optimization problem. In this study, six well-known population based optimization algorithms (artificial algae algorithm - AAA, artificial bee colony algorithm - ABC, differential evolution algorithm - DE, genetic algorithm - GA, gravitational search algorithm - GSA and particle swarm optimization - PSO) were used. These six algorithms were performed on the CEC’17 test functions. According to the experimental results, the algorithms were compared and performances of the algorithms were evaluated.

Multi-Start Jaya Algorithm for Software Module Clustering Problem

2018 ◽  
Vol 1 (1) ◽  
pp. 87-112 ◽  
Author(s):  
Kamal Z. Zamli ◽  
◽  
Abdulrahman Alsewari ◽  
Bestoun S. Ahmed ◽  
◽  
...  
Keyword(s):  
Jaya Algorithm ◽  
Software Module ◽  
Clustering Problem ◽  
Software Module Clustering

scholarly journals A Shuffle-Based Artificial Bee Colony Algorithm for Solving Integer Programming and Minimax Problems

Mathematics ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 1211
Author(s):  
Ivona Brajević
Keyword(s):  
Integer Programming ◽  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Minimax Problems ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Effective Performance ◽  
Complex Optimization ◽  
Overall Performance ◽  
Integer Programming Problems

The artificial bee colony (ABC) algorithm is a prominent swarm intelligence technique due to its simple structure and effective performance. However, the ABC algorithm has a slow convergence rate when it is used to solve complex optimization problems since its solution search equation is more of an exploration than exploitation operator. This paper presents an improved ABC algorithm for solving integer programming and minimax problems. The proposed approach employs a modified ABC search operator, which exploits the useful information of the current best solution in the onlooker phase with the intention of improving its exploitation tendency. Furthermore, the shuffle mutation operator is applied to the created solutions in both bee phases to help the search achieve a better balance between the global exploration and local exploitation abilities and to provide a valuable convergence speed. The experimental results, obtained by testing on seven integer programming problems and ten minimax problems, show that the overall performance of the proposed approach is superior to the ABC. Additionally, it obtains competitive results compared with other state-of-the-art algorithms.

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