2019-01-22T08:30:18Z
http://mathco.journals.pnu.ac.ir/?_action=export&rf=summon&issue=375
Biquarterly Research Journal of Control and Optimization in applied Mathematics
COAM
2383-3130
2383-3130
2016
1
1
Approximate Pareto Optimal Solutions of Multi objective Optimal Control Problems by Evolutionary Algorithms
Akbar
Hashemi Borzabadi
Manije
Hasanabadi
Navid
Sadjadi
In this paper an approach based on evolutionary algorithms to find Pareto optimal pair of state and control for multi-objective optimal control problems (MOOCP)'s is introduced. In this approach, first a discretized form of the time-control space is considered and then, a piecewise linear control and a piecewise linear trajectory are obtained from the discretized time-control space using a numerical method. To do that, a modified version of two famous evolutionary genetic algorithm (GA) and particle swarm optimization (PSO) to obtain Pareto optimal solutions of the problem is employed. Numerical examples are presented to show the efficiency of the given approach.
Multi-objective optimal control problem
Pareto solution
Evolutionary algorithm
Discretization
Approximation
2016
08
01
1
19
http://mathco.journals.pnu.ac.ir/article_2033_6025868ddbdd4dc87ad0fcbab8275cf9.pdf
Biquarterly Research Journal of Control and Optimization in applied Mathematics
COAM
2383-3130
2383-3130
2016
1
1
Regularity Conditions for Non-Differentiable Infinite Programming Problems using Michel-Penot Subdifferential
Nader
Kanzi
In this paper we study optimization problems with infinite many inequality constraints on a Banach space where the objective function and the binding constraints are locally Lipschitz. Necessary optimality conditions and regularity conditions are given. Our approach are based on the Michel-Penot subdifferential.
Programming problem
Regularity conditions
Optimality condition
Michel-Penot subdifferential
2016
08
01
21
30
http://mathco.journals.pnu.ac.ir/article_2036_b4c59900a48266b0ad2b6385eac8fc8b.pdf
Biquarterly Research Journal of Control and Optimization in applied Mathematics
COAM
2383-3130
2383-3130
2016
1
1
On Efficiency of Non-Monotone Adaptive Trust Region and Scaled Trust Region Methods in Solving Nonlinear Systems of Equations
Rasoul
Hekmati
In this paper we run two important methods for solving some well-known problems and make a comparison on their performance and efficiency in solving nonlinear systems of equations. One of these methods is a non-monotone adaptive trust region strategy and another one is a scaled trust region approach. Each of methods showed fast convergence in special problems and slow convergence in other ones; we try to categorize these problems and find out that which method has better numerical behavior. The robustness of methods is demonstrated by numerical experiments.
Non-monotone adaptive
Scaled trust region
Nonlinear systems of equations
Numerical comparison
2016
08
01
31
40
http://mathco.journals.pnu.ac.ir/article_2035_11095e0649e2c164939bca8bed4acb50.pdf
Biquarterly Research Journal of Control and Optimization in applied Mathematics
COAM
2383-3130
2383-3130
2016
1
1
A New Measure for Evaluating the Efficiency of Human's Resources in University
Aghile
Heydari
Hamid Reza
Yousefzadeh
In this paper we try to introduce a new approach and study the notion of efficiency under a multi objectives linear programming problem in the university by using analysis of hierarchy process (AHP). To this end, we first extract some effective parameters due to efficiency offices in university and then prioritized these parameters by the AHP method. Hence, we could classify the most important factors of people's dissatisfaction in the offices and could underlie further studies in related offices to evaluate the efficiency and also effective factors for increasing the efficiency. More clearly, a mathematical model is suggested to calculate the amount of efficiency under a multi objectives linear programming problem and then it is solved by using the existing methods. Note that in order to examine the approach's performance, the Payame Noor University of Mashhad (PNUM) is selected as a case study. Numerical experiments are included to illustrate the effectiveness of the proposed approach.
Efficiency
AHP Method
Multi Objectives Linear Programming Problem
2016
08
01
41
53
http://mathco.journals.pnu.ac.ir/article_2031_67e30f9b118bc22227c768a31ee37fdb.pdf
Biquarterly Research Journal of Control and Optimization in applied Mathematics
COAM
2383-3130
2383-3130
2016
1
1
Solving Linear Semi-Inﬁnite Programming Problems Using Recurrent Neural Networks
Alaeddin
Malek
Ghasem
Ahmadi
Seyyed Mehdi
Mirhoseini Alizamini
Linear semi-inﬁnite programming problem is an important class of optimization problems which deals with infinite constraints. In this paper, to solve this problem, we combine a discretization method and a neural network method. By a simple discretization of the infinite constraints,we convert the linear semi-infinite programming problem into linear programming problem. Then, we use a recurrent neural network model, with a simple structure based on a dynamical system to solve this problem. The portfolio selection problem and some other numerical examples are solved to evaluate the effectiveness of the presented model.
Linear semi-infinite programming
Recurrent neural network
Dynamical system
Discretization
Linear programming
2016
08
01
55
67
http://mathco.journals.pnu.ac.ir/article_2034_0faff11932ef505dc435167738955b71.pdf
Biquarterly Research Journal of Control and Optimization in applied Mathematics
COAM
2383-3130
2383-3130
2016
1
1
Solving Fully Fuzzy Linear Programming Problems with Zero-One Variables by Ranking Function
Aminalah
Alba
Jahanshahloo has suggested a method for the solving linear programming problems with zero-one variables. In this paper we formulate fully fuzzy linear programming problems with zero-one variables and a method for solving these problems is presented using the ranking function and also the branch and bound method along with an example is presented.
Fuzzy set
Fuzzy number
Ranking function
Triangular fuzzy number
Zero-one triangular fuzzy number
2016
08
01
69
78
http://mathco.journals.pnu.ac.ir/article_2032_bddde463bc52ea73351058285d33fa3e.pdf