Entropy‐Driven Global Best Selection in Particle Swarm Optimization for Many‐Objective Software Package Restructuring

Abstract

Many real-world optimization problems usually require a large number of conflicting objectives to be optimized simultaneously to obtain solution. It has been observed that these kinds of many-objective optimization problems (MaOPs) often pose several performance challenges to the traditional multi-objective optimization algorithms. To address the performance issue caused by the different types of MaOPs, recently, a variety of many-objective particle swarm optimization (MaOPSO) has been proposed. However, external archive maintenance and selection of leaders for designing the MaOPSO to real-world MaOPs are still challenging issues. This work presents a MaOPSO based on entropy-driven global best selection strategy (called EMPSO) to solve the many-objective software package restructuring (MaOSPR) problem. EMPSO makes use of the entropy and quality indicator for the selection of global best particle. To evaluate the performance of the proposed approach, we applied it over the five MaOSPR problems. We compared it with eight variants of MaOPSO, which are based on eight different global best selection strategies. The results indicate that the proposed EMPSO is competitive with respect to the existing global best selection strategies based on variants of MaOPSO approaches.

DOOSRA—Distributed Object‐Oriented Software Restructuring Approach using DIM‐K‐means and MAD‐based ENRNN classifier

“…Nevertheless, for computing the class coupling strength, the indirect link CDs were not considered by the model Amarjeet Prajapati et al [23]…”

“…[20]Non‐dominated sorting genetic algorithm (NSGA‐II)The model could well be an effectual one whose quality was under suspicionA disability was comprised by the NSGA‐II for dealing with additional objectives along with optimisation problems were generatedAmit Rathee and Jitender Kumar Chhabra [21]Refactoring algorithmThe model's applicability was fortified by the outcome obtained in the software system restructuringHowever, to cover the hidden data in the software packages, the model was not capableAmarjeet Prajapati [22]Search‐centric software restructuring modelThe system could engender restructuring solutions better than the prevailing software package structureNevertheless, for computing the class coupling strength, the indirect link CDs were not considered by the modelAmarjeet Prajapati et al. [23]Entropy‐driven global best selection techniqueThe outcomes displayed the EMPSO was competitive regarding the prevailing global best selection models grounded on variants of many‐objective particle swarm optimisation systemsHowever, the model provided lower performance…”