In this paper, a Goal Programming (GP) model is converted into a multi-objective optimization problem (MOO) of minimizing deviations from fixed goals. To solve the resulting MOO problem, a hybrid metaheuristic with two steps is proposed to find the Pareto set’s solutions. First, a Record-to-Record Travel with an adaptive memory is used to find first non-dominated Pareto frontier solutions preem...

In this paper relationships between Pareto points and saddle points in multiple objective programming are investigated. Convex and nonconvex problems are considered and the equivalence between Pareto points and saddle points is proved in both cases. The results are based on scalarizations of multiple objective programs and related linear and augmented Lagrangian functions. Partitions of the ind...

i This paper describes a solution technique mr Linear Programming problems with multiple objective functions. In this type of problem it is often necessary to replace the concept of "optimgm" with that of "best compromise". In contrast with methods dealing with a priori weighted sums of the objective functions, the method described here involves a sequential exploration of solutions. This explo...

It is a common characteristic of many multiple objective programming problems that the efficient solution set can only be identified in approximation: since this set often contains an infinite number of points, only a discrete representation can be computed, and due to numerical difficulties, each of these points itself might in general be only approximate to some efficient point. From among th...

Personal financial planning involves managing all the money activities during a planner’s lifetime. Traditional personal financial planning procedures begin with the planner’s financial status, goals, and expectations for the future before future cash flows of different time periods under various scenarios can be determined. If the planning results fail to meet the planner’s expectation, the pl...

A multi-objective optimization problem (MOP) is a simultaneous of more than one real-valued conflicting objective function subject to some constraints. Most MOP algorithms try provide set Pareto optimal solutions which are equally good in terms the functions. The can be infinite, and hence, analysis choice task or several among hard for decision maker (DM). In this paper, new scalarization appr...

Meta-programming has shown much promise for improving the quality of software by offering programming language techniques to address issues of modularity, reusability, maintainability, and extensibility. A system that supports metaprogramming is able to generate or manipulate other programs to extend their behavior. This paper describes OpenFortran, a Meta-Object Protocol (MOP) that is able to ...

The free-form deformation model can represent a wide range of non-rigid deformations by manipulating control point lattice over the image. However, due to large number parameters, it is challenging fit directly deformed image for estimation because complexity fitness landscape. In this paper, we cast registration task as multi-objective optimization problem (MOP) according fact that regions aff...

AspectS is an approach to general-purpose aspect-oriented programming in the Squeak/Smalltalk environment. Based on the language model of AspectJ, it extends the Smalltalk MOP to accommodate the aspect modularity mechanism. In contrast to AspectJ, weaving in AspectS happens dynamically at runtime.

We consider multiple objective programming (MOP) problems with additive interdependences, i.e. when the states of some chosen objective are attained through supportive or inhibitory feed-backs from several other objectives. We show how the concept of interdependency can be applied to an n-party single-issue negotiation problem in which the negotiators cooperatively face a common problem or in w...