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.
Highlights
Many real-world optimization problems usually require a large number of conflicting objectives to be optimized simultaneously to obtain solution
We compared it with eight variants of many-objective particle swarm optimization (MaOPSO), which are based on eight different global best selection strategies. e results indicate that the proposed EMPSO is competitive with respect to the existing global best selection strategies based on variants of MaOPSO approaches
Each of the variants is applied 31 times on each of the software projects. e performance of each variant is evaluated in terms of inverted generational distance (IGD) [52], modularization quality (MQ) [47], and hypervolume (HV) [53] quality metrics. e performance of each variant is compared with each other
Summary
Received 12 October 2021; Revised 18 November 2021; Accepted 22 November 2021; Published 7 December 2021. Industrial task scheduling [10], designing engineering models for different purposes [11], clustering of the software system to recover the software architecture [12], optimization of hybrid car controller [13], calibration optimization of the automotive [14], and improving existing software package structure [2] require more than three objectives to be optimized simultaneously to produce the solution Such real-world many-objective optimization problems (MaOPs) pose several challenges in the designing the metaheuristic optimization approach that can address them effectively and efficiently. E major limitation of the abovementioned PSO-based many-objective optimization algorithms is that most approaches use either convergence or divergence properties of the search space in designing the global best selection strategy. It mainly contains five major components: 1) initialization of position and velocity of particles in the swarm, 2) updation of the external archive, 3) updation of personal best position, 4) updation of global best position, and 5) updation of current velocity and position of the particles in the swarm. e details of each component are provided in the subsequent subsections
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