Granularity-based surrogate-assisted particle swarm optimization for high-dimensional expensive optimization

Tian, Jie; Sun, Chaoli; Tan, Ying; Zeng, Jianchao

doi:10.1016/j.knosys.2019.06.023

Cited by 37 publications

(7 citation statements)

References 40 publications

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“…e past few decades have witnessed the rapid development of PSO [33]-based metaheuristic optimization approaches due to wide application in solving the various optimization problems (e.g., [4,[34][35][36]). e easy implementation and effective optimization potential of the PSO algorithm make it more attractive in research and practice.…”

Section: Methodsmentioning

confidence: 99%

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

Prajapati

Parashar²,

Sunita

et al. 2021

Complexity

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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.

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Section: Methodsmentioning

confidence: 99%

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

Prajapati

Parashar²,

Sunita

et al. 2021

Complexity

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“…On the other hand, we note that these restart strategies could also be incorporated into population-based metaheuristics. For example, in Tian et al (2020), the authors proposed a reinitialization strategy based on the maximal focus distance, which can generate uniformly distributed initial particles (for a PSO). Simon et al (2014) proposed a reinitialization technique for another swarm intelligence algorithm.…”

Section: Intelligent and Adaptive Approachesmentioning

confidence: 99%

Initialization of metaheuristics: comprehensive review, critical analysis, and research directions

Sarhani

Voß

Jovanović

2022

Int Trans Operational Res

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Initialization of metaheuristics is a crucial topic that lacks a comprehensive and systematic review of the state of the art. Providing such a review requires in‐depth study and knowledge of the advances and challenges in the broader field of metaheuristics, especially with regard to diversification strategies, in order to assess the proposed methods and provide insights for initialization. Motivated by the aforementioned research gap, we provide a related review and begin by describing the main metaheuristic methods and their diversification mechanisms. Then, we review and analyze the existing initialization approaches while proposing a new categorization of them. Next, we focus on challenging optimization problems, namely constrained and discrete optimization. Lastly, we give insights on the initialization of local search approaches.

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“…PSO was introduced by Eberhart and Kennedy a few decades ago, which required short memory and was applied as an easy implementation process. PSO has been extensively applied as an optimization technique, like optical stuff based on multilayer thin films [57], electric daily peakload forecasting [58], high-dimensional clustering statistics [59], prediction differential models [60], parameter approximation of chaotic plots [61], optimization of nonlinear benchmark model [62] and parameter estimate models in electromagnetic waves of the plane [63].…”

Section: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

Swarming Computational Efficiency to Solve a Novel Third-Order Delay Differential Emden-Fowler System

Weera¹,

Sabir²,

Raja³

et al. 2022

Computers, Materials &Amp; Continua

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The purpose of this research is to construct an integrated neuro swarming scheme using the procedures of the artificial neural networks (ANNs) with the use of global search particle swarm optimization (PSO) along with the competent local search interior-point programming (IPP) called as ANN-PSOIPP. The proposed computational scheme is implemented for the numerical simulations of the third order nonlinear delay differential Emden-Fowler model (TON-DD-EFM). The TON-DD-EFM is based on two types along with the particulars of shape factor, delayed terms, and singular points. A merit function is performed using the optimization of PSOIPP to find the solutions to the TON-DD-EFM. The effectiveness of the ANN-PSOIPP is certified through the comparison with the exact results for solving four examples of the TON-DD-EFM. The scheme's efficiency is observed by performing the absolute error in suitable measures found around 10 −04 to 10 −07 . Furthermore, the statistical-based assessments for 100 trials are provided to compute the accuracy, stability, and constancy of the ANN-PSOIPP for solving the TON-DD-EFM.

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Granularity-based surrogate-assisted particle swarm optimization for high-dimensional expensive optimization

Cited by 37 publications

References 40 publications

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

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

Initialization of metaheuristics: comprehensive review, critical analysis, and research directions

Swarming Computational Efficiency to Solve a Novel Third-Order Delay Differential Emden-Fowler System

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