Convex Multiobjective Programming Research Articles

Solving Convex Multi-Objective Optimization Problems via a Capable Neural Network Scheme

A neural network architecture is constructed to solve Convex Multi-objective Programming Problem (CMPP). By using the weighted sum method, a single-objective optimization problem related to the CMPP is formulated, in which the scalar objective is summation of weighted original objective functions. The Pareto Optimal Solution (POS) is given by using different values of weights. A neural network framework is then designed for solving the obtained convex problem. Based on employing Lyapunov theory, the proposed model is established to be stable in the sense of Lyapunov and it is globally convergent to an exact optimal solution of the convex programming problem with different values of weights. The computer simulations shows the feasibility and the efficiency of the suggested model.

A GEOMETRIC PROGRAMMING APPROACH TO CONVEX MULTI-OBJECTIVE PROGRAMMING PROBLEMS

Over the past few years, convex optimization has played a vital role in the study of complex engineering problems in different fields. Geometric programming is one of the available techniques particularly used for solving nonconvex programming problems. But in this article, a suitable attempt has been made to solve a real-life model on convex multi-objective using geometric programming technique with help of the E-constraint method and result is compared with the solutions obtained by fuzzy technique. Finally, a conclusion is presented by analyzing the solutions to a numerical problem.

A New Fuzzy Multiobjective Geometric Programming in Double Sampling in Presence of Non-Response

Non-linear programming problems can be solved efficiently by geometric programming in which non-linear expressions can be expressed by an exponential or power functions. In this paper, Double Sampling Stratification (DSS) strategy is used in multivariate stratified population with unidentified strata weights, for optimum sampling design (OSD) in the presence of non-response to estimate the unidentified population means. The double sampling problem in the presence of non-response is formulated as Fuzzy Multiobjective Convex Programming Problem (FMOCPP). Then, we convert FMOCPP into Fuzzy Single Objective Convex Problem (FSOCP) by using membership function. Dual solution is obtained using LINGO Software by solving FSOCP. The optimum allocations of sample sizes of respondents and non- respondents for both the phases are obtained by applying dual solutions and primal-dual relationship theorem. The given technique is illustrated by projecting a numerical problem.

Guaranteeing highly robust weakly efficient solutions for uncertain multi-objective convex programs

This paper deals with uncertain multi-objective convex programming problems, where the data of the objective function or the constraints or both are allowed to be uncertain within specified uncertainty sets. We present sufficient conditions for the existence of highly robust weakly efficient solutions, that is, robust feasible solutions which are weakly efficient for any possible instance of the objective function within a specified uncertainty set. This is done by way of estimating the radius of highly robust weak efficiency under linearly distributed uncertainty of the objective functions. In the particular case of robust quadratic multi-objective programs, we show that these sufficient conditions can be expressed in terms of the original data of the problem, extending and improving the corresponding results in the literature for robust multi-objective linear programs under ball uncertainty.

Optimality for E-[0,1] convex multi-objective programming

In this paper, we interest with deriving the sufficient and necessary conditions for optimal solution of special classes of Programming. These classes involve generalized E-[0,1] convex functions. Characterization of efficient solutions for E-[0,1] convex multi-objective Programming are obtained. Finally, sufficient and necessary conditions for a feasible solution to be an efficient or properly efficient solution are derived.

A branch-and-bound based heuristic algorithm for convex multi-objective MINLPs

We study convex multi-objective Mixed Integer Non-Linear Programming problems (MINLPs), which are characterized by multiple objective functions and non linearities, features that appear in real-world applications. To derive a good approximated set of non-dominated points for convex multi-objective MINLPs, we propose a heuristic based on a branch-and-bound algorithm. It starts with a set of feasible points, obtained, at the root node of the enumeration tree, by iteratively solving, with an ε-constraint method, a single objective model that incorporates the other objective functions as constraints. Lower bounds are derived by optimally solving Non-Linear Programming problems (NLPs). Each leaf node of the enumeration tree corresponds to a convex multi-objective NLP, which is solved heuristically by varying the weights in a weighted sum approach. In order to improve the obtained points and remove dominated ones, a tailored refinement procedure is designed. Although the proposed method makes no assumptions on the number of objective functions nor on the type of the variables, we test it on bi-objective mixed binary problems. In particular, as a proof-of-concept, we tested the proposed heuristic algorithm on instances of a real-world application concerning power generation, and instances of the convex biobjective Non-Linear Knapsack Problem. We compared the obtained results with those derived by well-known scalarization methods, showing the effectiveness of the proposed method.

Solving generalized convex multiobjective programming problems by a normal direction method

We use normal directions of the outcome set to develop a method of outer approximation for solving generalized convex multiobjective programming problems. We prove the convergence of the method and report some computational experiments. As an application, we obtain an algorithm to solve an associated multiplicative problem over a convex constraint set.

Norm–based approximation in E-[0,1] convex multi-objective programming

This paper addresses the problem of capturing nondominated points on non-convex pareto frontier, which are encountered in E-[0,1] convex multi-objective programming problems. We use E-[0,1] map to transfer non-convex pareto frontier to convex pareto frontier, then An algorithm to find a piecewise linear approximation of the nondominated set of convex pareto frontier are applied. Finally, the inverse map of E-[0,1] is used to get the nondominated set of non-convex pareto frontier.

Optimality for E - [ 0 , 1 ] convex multi-objective programming problems

In this paper, we are interested in deriving the sufficient and necessary conditions for an optimal solution of special classes of programming problems. These classes involve generalized $E\mbox{-}[0,1]$ convex functions. The characterization of efficient solutions for $E\mbox{-}[0,1]$ convex multi-objective programming problems is obtained. Finally, sufficient and necessary conditions for a feasible solution to be an efficient or properly efficient solution are derived.

An objective space cut and bound algorithm for convex multiplicative programmes

Multiplicative programming problems are global optimisation problems known to be NP-hard. In this paper we propose an objective space cut and bound algorithm for approximately solving convex multiplicative programming problems. This method is based on an objective space approximation algorithm for convex multi-objective programming problems. We show that this multi-objective optimisation algorithm can be changed into a cut and bound algorithm to solve convex multiplicative programming problems. We use an illustrative example to demonstrate the working of the algorithm. Computational experiments illustrate the superior performance of our algorithm compared to other methods from the literature.

The vector exact l1 penalty method for nondifferentiable convex multiobjective programming problems

In this paper, the vector exact l1 penalty function method for nondifferentiable convex multiobjective programming problems is analyzed. The vector penalized optimization problem with the vector exact l1 penalty function is defined. Conditions are given guaranteeing the equivalence of the sets of (weak) Pareto optimal solutions of the considered nondifferentiable multiobjective programming problem and of the associated vector penealized optimization problem with the vector exact l1 penalty function. This equivalence is established for nondifferentiable vector optimization problems with convex functions.

A constraint shifting homotopy method for convex multi-objective programming

In this paper, a constraint shifting combined homotopy method for solving multi-objective programming problems with both equality and inequality constraints is presented. It does not need the starting point to be an interior point or a feasible point and hence is convenient to use. Under some assumptions, the existence and convergence of a smooth path to an efficient solution are proven. Simple numerical results are given.

Multi-objective convex programming problem arising in multivariate sampling

In this paper, we formulate the multivariate allocation problem as a multi-objective convex programming problem. The objective functions are convex and there is a single linear constraint with some upper and lower bounds. We also consider a two dimensional multivariate problem when the cost is minimized. A numerical example is given to illustrate the solution procedure. International Journal of Engineering, Science and Technology, Vol. 2, No. 6, 2010, pp. 291-296

Generating Efficient Outcome Points for Convex MultiobjectiveProgramming Problems and Its Application to Convex MultiplicativeProgramming

Convex multiobjective programming problems and multiplicative programming problems have important applications in areas such as finance, economics, bond portfolio optimization, engineering, and other fields. This paper presents a quite easy algorithm for generating a number of efficient outcome solutions for convex multiobjective programming problems. As an application, we propose an outer approximation algorithm in the outcome space for solving the multiplicative convex program. The computational results are provided on several test problems.

Chebyshev Approximate Solution to Allocation Problem in Multiple Objective Surveys with Random Costs

In this paper, we consider an allocation problem in multivariate surveys as a convex programming problem with non-linear objective functions and a single stochastic cost constraint. The stochastic constraint is converted into an equivalent deterministic one by using chance constrained programming. The resulting multi-objective convex programming problem is then solved by Chebyshev approximation technique. A numerical example is presented to illustrate the computational procedure.

An approximation algorithm for convex multi-objective programming problems

Multiplicative programming problems are difficult global optimization problems known to be NP-hard. In this paper we propose a method for approximately solving convex multiplicative programming problems. This work is based on our previous work “An approximation algorithm for convex multiobjective programming problems”. We show, by slightly changing the algorithm, that our method can be used to solve convex multiplicative programming problems. We provide an example that shows that our method has computational advantage compared with Benson's outcome space branch and bound outer approximation algorithm.

Modified interactive Chebyshev algorithm (MICA) for convex multiobjective programming

In this paper, we describe an interactive procedural algorithm for convex multiobjective programming based upon the Tchebycheff method, Wierzbicki’s reference point approach, and the procedure of Michalowski and Szapiro. At each iteration, the decision maker (DM) has the option of expressing his or her objective-function aspirations in the form of a reference criterion vector. Also, the DM has the option of expressing minimally acceptable values for each of the objectives in the form of a reservation vector. Based upon this information, a certain region is defined for examination. In addition, a special set of weights is constructed. Then with the weights, the algorithm of this paper is able to generate a group of efficient solutions that provides for an overall view of the current iteration’s certain region. By modification of the reference and reservation vectors, one can “steer” the algorithm at each iteration. From a theoretical point of view, we prove that none of the efficient solutions obtained using this scheme impair any reservation value for convex problems. The behavior of the algorithm is illustrated by means of graphical representations and an illustrative numerical example.

The envelope theorem for multiobjective convex programming via contingent derivatives

The aim of this paper is to formulate an envelope theorem for vector convex programs. The obtained result allows to quantify the changes of a certain set of optimal values according to changes of any of the parameters which appear in the constraints. We show that the sensitivity depends on a set of associated Lagrange multipliers and its sensitivity.

Characterizing strict efficiency for convex multiobjective programming problems

The article pertains to characterize strict local efficient solution (s.l.e.s.) of higher order for the multiobjective programming problem (MOP) with inequality constraints. To create the necessary framework, we partition the index set of objectives of MOP to give rise to subproblems. The s.l.e.s. of order m for MOP is related to the local efficient solution of a subproblem. This relationship inspires us to adopt the D.C. optimization approach, the convex subdifferential sum rule, and the notion of ?-subdifferential to derive the necessary and sufficient optimality conditions for s.l.e.s. of order $${m \geqq 1}$$ for the convex MOP. Further, the saddle point criteria of higher order are also presented.

On the set of weakly efficient minimizers for convex multiobjective programming

In this paper by employing an asymptotic approach we develop an existence and stability theory for convex multiobjective programming. We deal with the set of weakly efficient minimizers. To this end we employ a notion of convergence for vector-valued functions close to that due to Lemaire.