Control and Optimization in Applied Mathematics
Volume:1 Issue: 1, Spring-Summer 2016

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 introducedý. ýIn this approachý, ýfirst a discretized form of the time-control space is considered and thený, ýa piecewise linear control and a piecewise linear trajectory are obtained from the discretized time-control space using a numerical methodý. ýTo do thatý, ýa modified version of two famous evolutionary genetic algorithm (GA) and particle swarm optimization (PSO) to obtain Pareto optimal solutions of the problem is employedý. ýNumerical examples are presented to show the efficiency of the given approach.

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 Lipschitzý. ýNecessary optimality conditions and regularity conditions are givený. ýOur approach are based on the Michel-Penot subdifferential.

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)ý. ýTo this endý, ýwe first extract some effective parameters due to efficiency offices in university and then prioritized these parameters by the AHP methodý. ýHenceý, ýwe 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 efficiencyý. ýMore clearlyý, ýa 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 methodsý. ýNote that in order to examine the approach's performanceý, ýthe Payame Noor University of Mashhad (PNUM) is selected as a case studyý. ýNumerical experiments are included to illustrate the effectiveness of the proposed approach.

ýLinear semi-infinite programming problem is an important class of optimization problems which deals with infinite constraintsý. ýIn this paperý, ýto solve this problemý, ýwe combine a discretization method and a neural network methodý. ýBy a simple discretization of the infinite constraints,we convert the linear semi-infinite programming problem into linear programming problemý. ýThený, ýwe use a recurrent neural network modelý, ýwith a simple structure based on a dynamical system to solve this problemý. ýThe portfolio selection problem and some other numerical examples are solved to evaluate the effectiveness of the presented model.

Jahanshahloo has suggested a method for the solving linear programming problems with zero-one variablesý. ýIn 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.