Design of the software system plays a crucial role in the effective and efficient maintenance of the software system. In the absence of original design structure it might be required to re-identify the design by using the source code of the concerned software. Software clustering is one of the powerful techniques which could be used to cluster large software systems into smaller manageable subsystems containing modules of similar features. This paper examines the use of novel evolutionary imperialist competitive algorithms, genetic algorithms and their combinations for software clustering. Apparently, recursive application of these algorithms result in the best performance in terms of quality of clusters, number of epochs required for convergence and standard deviation obtained by repeated application of these algorithms.

Nature-inspired algorithms are recently being appreciated for solving complex optimization and engineering problems. Black hole algorithm is one of the recent nature-inspired algorithms that have obtained inspiration from black hole theory of universe. In this paper, four formulations of multi-objective black hole algorithm have been developed by using combination of weighted objectives, use of secondary storage for managing possible solutions and use of Genetic Algorithm (GA). These formulations are further applied for scheduling jobs on parallel machines while optimizing bi-criteria namely maximum tardiness and weighted flow time. It has been empirically verified that GA based multi-objective Black Hole algorithms leads to better results as compared to their counterparts. Also the use of combination of secondary storage and GA further improves the resulting job sequence. The proposed algorithms are further compared to some of the existing algorithms, and empirically found to be better. The results have been validated by numerical illustrations and statistical tests.