Finding non dominated points for multiobjective integer convex programs with linear constraints

Zerfa, Lamia; Chergui, Mohamed

doi:10.1007/s10898-022-01132-4

Cited by 5 publications

(2 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…All simulation results show that the state trajectories of the model (12) converge to the optimal solution x * by choosing weights (w 1 , w 2 ) ⊤ . For example, Figure 4 describes that the trajectories of the neural network model (12) with eight different initial points and weight (w 1 , w 2 ) ⊤ = (0.8, 0.2) ⊤ converge to the optimal solution of the related single objective problem of (37). The generated Pareto fronts using the neural network (12) is presented in Figure 5.…”

Section: Weight Optimal Solutionmentioning

confidence: 99%

“…Luquea et al in Reference 36 described an interactive procedural algorithm for CQMOP based upon the Tchebycheff method, Wierzbicki's reference point approach, and the procedure of Michalowski and Szapiro. Zerfa and Chergui in Reference 37 presented a branch‐and‐bound based algorithm to generate all non dominated points for a multi‐objective integer programming problem with convex quadratic objective functions and linear constraints.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Utilizing a projection neural network to convex quadratic multi‐objective programming problems

Jahangiri

Nazemi

2023

Adaptive Control & Signal

View full text Add to dashboard Cite

Summary In this paper, we propose a projection neural network model for solving convex quadratic multi‐objective optimization problem (CQMOP). The CQMOP is first converted into an equivalent convex nonlinear programming problem by the means of the weighted sum method, where the Pareto optimal solutions are calculated via different values of weights. A neural network model is then constructed for solving the obtained convex problem. It is shown that the presented neural network is stable in the sense of Lyapunov and is globally convergent. Simulation results are given to illustrate the global convergence and performance of the suggested model. Both theoretical and numerical approaches are studied. Numerical results are in good agreement with the proved theoretical concepts.

show abstract