A novel hybrid multi-objective algorithm to solve the generalized cubic cell formation problem

Bouaziz, Hamida; Bardou, Dalal; Berghida, Meryem; Chouali, Samir; Lemouari, Ali

doi:10.1016/j.cor.2022.106069

Cited by 5 publications

(3 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The optimality tolerances were set to 0.001%. Based on a comparison between test problems solved with AUGMECON-R 20,29 and the presented biofuel supply chain problem, it is demonstrated that using AUGMECON-R increases computation efficiency by almost 70%.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Optimal design of a biofuel supply chain using an augmented multi-objective and TOPSIS method

et al. 2023

View full text Add to dashboard Cite

show abstract

Section: Resultsmentioning

confidence: 99%

“…Eqn (28) states the distribution of biofuel from the refinery to demand and biofuel storage. Eqn (29) imposes the mass balance for biofuel storage, similar to eqn (22). The inventory…”

Section: Constraintsmentioning

confidence: 99%

Optimal design of a biofuel supply chain using an augmented multi-objective and TOPSIS method

et al. 2023

View full text Add to dashboard Cite

show abstract

“…Research indicates that incorporating additional information into the process of evolution, such as gradient search techniques and special combination operators, can greatly improve the search efficiency and solution accuracy of MOEAs [13,[44][45][46].…”

Section: Hybrid Multi-objective Algorithm Frameworkmentioning

confidence: 99%

An Efficient Hybrid Multi-Objective Optimization Method Coupling Global Evolutionary and Local Gradient Searches for Solving Aerodynamic Optimization Problems

Cao,

Tang,

Zhu

et al. 2023

Mathematics

View full text Add to dashboard Cite

Aerodynamic shape optimization is frequently complicated and challenging due to the involvement of multiple objectives, large-scale decision variables, and expensive cost function evaluation. This paper presents a bilayer parallel hybrid algorithm framework coupling multi-objective local search and global evolution mechanism to improve the optimization efficiency and convergence accuracy in high-dimensional design space. Specifically, an efficient multi-objective hybrid algorithm (MOHA) and a gradient-based surrogate-assisted multi-objective hybrid algorithm (GS-MOHA) are developed under this framework. In MOHA, a novel multi-objective gradient operator is proposed to accelerate the exploration of the Pareto front, and it introduces new individuals to enhance the diversity of the population. Afterward, MOHA achieves a trade-off between exploitation and exploration by selecting elite individuals in the local search space during the evolutionary process. Furthermore, a surrogate-assisted hybrid algorithm based on the gradient-enhanced Kriging with the partial least squares(GEKPLS) approach is established to improve the engineering applicability of MOHA. The optimization results of benchmark functions demonstrate that MOHA is less constrained by dimensionality and can solve multi-objective optimization problems (MOPs) with up to 1000 decision variables. Compared to existing MOEAs, MOHA demonstrates notable enhancements in optimization efficiency and convergence accuracy, specifically achieving a remarkable 5–10 times increase in efficiency. In addition, the optimization efficiency of GS-MOHA is approximately five times that of MOEA/D-EGO and twice that of K-RVEA in the 30-dimensional test functions. Finally, the multi-objective optimization results of the airfoil shape design validate the effectiveness of the proposed algorithms and their potential for engineering applications.

show abstract

Multi-objective cell configuration considering part quality and machine compatibility

Heydari,

Paydar,

Mahdavi

et al. 2024

Neural Comput & Applic

View full text Add to dashboard Cite

A novel hybrid multi-objective algorithm to solve the generalized cubic cell formation problem

Cited by 5 publications

References 33 publications

Optimal design of a biofuel supply chain using an augmented multi-objective and TOPSIS method

Optimal design of a biofuel supply chain using an augmented multi-objective and TOPSIS method

An Efficient Hybrid Multi-Objective Optimization Method Coupling Global Evolutionary and Local Gradient Searches for Solving Aerodynamic Optimization Problems

Multi-objective cell configuration considering part quality and machine compatibility

Contact Info

Product

Resources

About