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

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.

Generating the structural graph‐based model from a program source‐code using chaotic forrest optimization algorithm

“…In each iteration, chromosomes are evolved, and attempts are made to construct chromosomes (clusters) with high cohesion and low coupling using a goal function. Then, in order to extend the search, the selected solutions are modified using crossover and mutation operators (Barros, 2012;Chen, 1995). The results of conducted experiments show that the GA is a good alternative for SMC problems for small and middle size software clustering.…”

“…The Savalan technique enhances all clustering criteria at the same time, according to the results. Savalan creates higher-quality clusters than comparable methods such as Bunch (Mancoridis et al, 1999), CIA (Chen, 1995), and Chava (Korn et al, 1999), and has much greater performance (MQ) in big software systems. Savalan was provided as a free software tool for academics and developers in (Chen, 1995).…”

“…Savalan creates higher-quality clusters than comparable methods such as Bunch (Mancoridis et al, 1999), CIA (Chen, 1995), and Chava (Korn et al, 1999), and has much greater performance (MQ) in big software systems. Savalan was provided as a free software tool for academics and developers in (Chen, 1995). As a result, this research bridges the gap between theory and practice and has a theoretical and practical influence on computer society.…”

“…The clustering methods will use the generated graph as their input. Source code analysing tools are used to automate MDG production (http://savalan-smct.com/, n.d.; Chen, 1995;Korn et al, 1999). The proposed method's process is depicted in Figure 1.…”

MaDHS: Many‐objective discrete harmony search to improve existing package design

“…9,15,16 In real-world software remodularization problem such as improving the package design of an existing object-oriented software system, there are many conflicting objectives, which need to be optimized simultaneously. 4,[17][18][19][20][21][22] The previous researchers have adapted the different metaheuristic algorithms (eg, nondominated sorting genetic algorithm II (NSGA-II), 3 harmony search (HS), 23 and artificial bee colony (ABC) 24 ) to design the SBSR techniques to address the various forms of remodularization problems. However, these algorithms experiences degradation in their performance when the number of objective functions to be optimized increase by more than three.…”