A comparative study of many-objective optimizers on large-scale many-objective software clustering problems

Over the past 2 decades, several multi-objective optimizers (MOOs) have been proposed to address the different aspects of multi-objective optimization problems (MOPs). Unfortunately, it has been observed that many of MOOs experiences performance degradation when applied over MOPs having a large number of decision variables and objective functions. Specially, the performance of MOOs rapidly decreases when the number of decision variables and objective functions increases by more than a hundred and three, respectively. To address the challenges caused by such special case of MOPs, some large-scale multi-objective optimization optimizers (L-MuOOs) and large-scale many-objective optimization optimizers (L-MaOOs) have been developed in the literature. Even after vast development in the direction of L-MuOOs and L-MaOOs, the supremacy of these optimizers has not been tested on real-world optimization problems containing a large number of decision variables and objectives such as large-scale many-objective software clustering problems (L-MaSCPs). In this study, the performance of nine L-MuOOs and L-MaOOs (i.e., S3-CMA-ES, LMOSCO, LSMOF, LMEA, IDMOPSO, ADC-MaOO, NSGA-III, H-RVEA, and DREA) is evaluated and compared over five L-MaSCPs in terms of IGD, Hypervolume, and MQ metrics. The experimentation results show that the S3-CMA-ES and LMOSCO perform better compared to the LSMOF, LMEA, IDMOPSO, ADC-MaOO, NSGA-III, H-RVEA, and DREA in most of the cases. The LSMOF, LMEA, IDMOPSO, ADC-MaOO, NSGA-III, and DREA, are the average performer, and H-RVEA is the worst performer.

Information Theoretic Objective Function for Genetic Software Clustering

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.

Geometric Morphometrics Analysis of Chelae and Carapace of the Freshwater Prawn Macrobrachium Bate, 1868

The shape of chelae and carapace can be used to distinguish between species of prawn. This study aims to determine the variations in the shape of chelae and carapace in several species belonging to the genus Macrobrachium using analysis of geometric morphometric. This study uses photos of specimens that have been processed with several TPS software. Data analyzed statistically by PCA using the MorphoJ software. Clustering analysis using UPGMA method using PAST software. The results showed the carapace shape grid deformation varied at the tip of the rostrum, the tip of the ocular spine and the lower curvature of the front of the carapace, and the base spines of rostrum. Grid deformation in the shape of chelae varies at the tip of the pollex, the junction between the pollex and the manus on the inferior margin of the propodus, the upper and lower points marking the junction of the dactylus with the propodus. PCA shows the total variation of the carapace shape is 82.66% which is divided into PC1: 75.11% and PC2: 7.55%. While the total variation of the shape of chelae is 87.56% which is divided into PC1: 55.49% and PC2: 32.07%. Clustering analysis shows the grouping of populations of Macrobrachium, the first group is M. latidactylus and M. sintangense, the second group includes M. horstii and M. latimanus. M. lar is a species that shows the similarity of the shape of the carapace and chelae with the two groups. M. rosenbergii and M. pilimanus are on different lines.