Journal ArticleDOI
Decomposition of parametric space for bi-objective optimization problem using neural network approach
TLDR
The proposed gradient-based neural network approach is affirmed to be stable in the sense of Lyapunov and it is capable for obtaining the optimal solution in solving both NLPPs and BOOPs tasks.Abstract:
A new gradient-based neural network approach is proposed for solving nonlinear programming problems (NLPPs) and bi-objective optimization problems (BOOPs). The most prominent feature of the proposed approach is that it can converge rapidly to the equilibrium point (optimal solution), for an arbitrary initial point. The proposed approach is affirmed to be stable in the sense of Lyapunov and it is capable for obtaining the optimal solution in solving both NLPPs and BOOPs tasks. Further, BOOP is converted into an equivalent optimization problem by the mean of the weighted sum method, where the Pareto optimal solutions are obtained by using different weights. Also the decomposition of parametric space for BOOP is analyzed in details based on the stability set of the first kind. The experiments results also affirmed that the proposed approach is a promising approach and has an effective performance.read more
Citations
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Journal ArticleDOI
Hybridization of Grey Wolf Optimizer and Crow Search Algorithm Based on Dynamic Fuzzy Learning Strategy for Large-Scale Optimization
TL;DR: The proposed GWO-CSA algorithm combines the strong points of both grey wolf optimizer and crow search algorithm with the aim to escape from local optima with faster convergence than the standard GWO and CSA.
Journal ArticleDOI
Integrating reference point, Kuhn–Tucker conditions and neural network approach for multi-objective and multi-level programming problems
TL;DR: The main idea is to convert the MOPP and the MLPP into an equivalent convex optimization problem and a neural network approach is then constructed for solving the obtained convex programming problem.
Journal ArticleDOI
Feedback neural network for constrained bi-objective convex optimization
TL;DR: In this article , a novel feedback neural network is constructed to find an optimal solution of the low-priority optimization problem in the optimal solution set of the high priority optimization problem, which is proved to be a Pareto optimal solution to the considered bi-objective optimization problem.
Solving biobjective network flow problem associated with minimum cost-time loading
Omid Baghani,Saideh Ghafoori +1 more
TL;DR: A primal-dual simplex algorithm is applied for solving the biobjective min imum cost-time network flow problem such that the total shipping cost and the totalShipping fixed time are considered as the first and second objective functions, respectively.
Journal ArticleDOI
Utilizing a projection neural network to convex quadratic multi‐objective programming problems
Mohammad Jahangiri,Ali Nazemi +1 more
TL;DR: In this paper , a projection neural network model was proposed for solving convex quadratic multi-objective optimization problem (CQMOP), where the Pareto optimal solutions are calculated via different values of weights.
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