Sustainable multi-objective process planning in reconfigurable manufacturing environment: adapted new dynamic NSGA-II vs New NSGA-III

Khettabi, Imen; Benyoucef, Lyès; Boutiche, Mohamed Amine

doi:10.1080/00207543.2022.2044537

Cited by 28 publications

(2 citation statements)

References 54 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Fortunately, the MPC algorithms have continued to be a highly effective control technique, maintaining their efficacy by accommodating complex dynamical systems and robust control objectives. Several methodologies have been investigated, including the Differential Evolution Algorithm (DEA) [28], [29], Sand Cat Swarm Optimization (SCSO) [30], Grey Wolf Optimizer (GWO) [31] and Non-dominated Sorting Genetic Algorithm III (NSGAIII) [32] for seeking objective optimums in complex landscapes. However, these methods are more suited for problems where a global or multi-objective optimum exists; they might not inherently focus on the worst-case scenarios.…”

Section: Introductionmentioning

confidence: 99%

Efficient Motion Planning With Minimax Objectives: Synergizing Interval Prediction and Tree-Based Planning

Vo,

Jung,

Kim

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

Section: Introductionmentioning

confidence: 99%

Efficient Motion Planning With Minimax Objectives: Synergizing Interval Prediction and Tree-Based Planning

Vo,

Jung,

Kim

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Khettabi et al first proposed a reconfigurable manufacturing system for cost, time efficiency, and environmental awareness. Adaptive dynamic NSGA-II and NSGA-III were developed for this system [12].…”

Section: Introductionmentioning

confidence: 99%

A Multi-Objective Local Optimization Method for Imaging Optical Systems

Zhao,

Wu,

et al. 2023

Photonics

View full text Add to dashboard Cite

Addressing the inherent limitations of conventional local optimization methodologies such as damping least squares (DLS) and adaptation algorithms, this study proposes a novel approach to multi-objective optimization for imaging optical systems. The proposed method entails the formulation of a multi-objective optimization mathematical framework, where the objectives are established upon lateral aberration and wave aberration criteria. Subsequently, enhancements are made to the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) by implementing a directional initial population strategy and parallel optimization with multiple-trajectory planning. The genesis of the initial population is rooted in the gradient direction information extracted from the starting positions. This strategic foundation precludes potential efficiency limitations in subsequent optimization stages arising from undesirable initial population quality. By employing mechanisms such as differentiation and mutation to sustain population diversity, the trajectory of evolution is guided by the first- and second-order derivatives of the optimal individual, thereby elevating the quality of the evolutionary offspring population. The fusion of the parent and offspring populations yields a composite population, which undergoes rapid and non-dominated sorting, crowding calculation, and elite strategies. Empirical results illustrate the validation of the proposed methodology. The proof-of-concept paradigm demonstrates high efficiency in multi-objective local optimization for imaging optical systems.

show abstract

Comparative Study of Multi‐objective Bayesian Optimization and NSGA‐III based Approaches for Injection Molding Process

Jung,

Park,

Lee

et al. 2024

Advcd Theory and Sims

View full text Add to dashboard Cite

Injection molding is a prevalent method for producing plastic components, yet determining the ideal process parameters has predominantly relied on heuristic approaches. In this research, a data‐driven injection molding process optimization framework is developed to simultaneously minimize warpage, cycle time, and clamping force. Employing multi‐objective Bayesian optimization (MBO), the framework is applied to a fan blade model for verification. Incorporating those objectives enables the selection of an injection molding machine with the proper maximum clamping force within acceptable tolerance and production time. Furthermore, non‐moldable regimes are considered in design space, which allows less prior knowledge for setting design space. The optimized results are compared with those obtained using NSGA‐III, a well‐established genetic algorithm‐based optimization technique, as a benchmark. The optimization frameworks produce a Pareto front for the three‐dimensional outputs, revealing distinct trade‐off relationships between cycle time and warpage, as well as clamping force and warpage. The MBO framework demonstrates a superior Pareto front compared to NSGA‐III when utilizing a limited data set, underscoring its benefits in scenarios where costly simulations or experiments are necessary. These findings are anticipated to contribute to the optimization of manufacturing processes, ultimately enhancing productivity in real‐world industries.

show abstract

Sustainable multi-objective process planning in reconfigurable manufacturing environment: adapted new dynamic NSGA-II vs New NSGA-III

Cited by 28 publications

References 54 publications

Efficient Motion Planning With Minimax Objectives: Synergizing Interval Prediction and Tree-Based Planning

Efficient Motion Planning With Minimax Objectives: Synergizing Interval Prediction and Tree-Based Planning

A Multi-Objective Local Optimization Method for Imaging Optical Systems

Comparative Study of Multi‐objective Bayesian Optimization and NSGA‐III based Approaches for Injection Molding Process

Contact Info

Product

Resources

About