Many decision making problems can be formulated as multi-objective optimization problems (MOP). There hardly exists the solution that optimizes all objective functions in MOP, and then the concept of Pareto optimal solution (or efficient solution) is introduced. Usually, there exist a lot of Pareto optimal solutions, which are considered as candidates of final decision making solution. It is an...

A multi-objective chance-constrained programming integrated with Genetic Algorithm and robustness evaluation methods was proposed to weigh the conflict between system investment against risk for watershed load reduction, which was firstly applied to nutrient load reduction in the Lake Qilu watershed of the Yunnan Plateau, China. Eight sets of Pareto solutions were acceptable for both system inv...

A multiobjective programming algorithm may find multiple nondominated solutions. If these solutions are scattered more uniformly over the Pareto frontier in the objective space, they are more different choices and hence their quality is better. In this paper, we propose a quality measure called U-measure to measure the uniformity of a given set of nondominated solutions over the Pareto frontier...

[1] The operation of large-scale water resources systems often involves several conflicting and noncommensurable objectives. The full characterization of tradeoffs among them is a necessary step to inform and support decisions in the absence of a unique optimal solution. In this context, the common approach is to consider many single objective problems, resulting from different combinations of ...

This paper deals with three particular models of the bi-criteria {0,1}-knapsack problem: equal weighted items, constant sum of the criteria coe¢cients, and the combination of the two previous models. The con...guration of the Pareto frontier is presented and studied. Several properties on the number and the composition of the e¢cient solutions are devised. The connectedness of the e¢cient solut...

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...

One aspect that is often disregarded in evolutionary multiobjective research is the fact that the solution of a problem involves not only search but decision making. Most of approaches concentrate on adapting an evolutionary algorithm to generate the Pareto frontier. In this work we present a new idea to incorporate preferences in MOEA. We introduce a binary fuzzy preference relation that expre...

In many practical investment situations the amount of available memory on stock data is extremely huge. Thus many investors are attracted to base their decisions on the information "currently available in their minds" (see [1, 2]). In the present paper various risk measurement models having application in the investment management are discussed. First we explain the concept of mean variance eff...

This work presents a genetic algorithm (GA)-based optimization technique, called GA-ParFnt, to find the Pareto frontier for optimizing data transfer versus job execution time in grids. As the performance of a generic GA is not suitable to find such Pareto relationship, major modifications are applied to it so that it can efficiently discover such relationship. The frontier curve representing th...

In this work, authors discuss the way of selecting level supplier by using concept binary coded genetic algorithm. For best solution due to involvement multi objective functions, process Tournament selection is widely discussed. addition this, involve fuzzy parameters aspiration levels Decision maker in analysis part for more clarity towards optimality. As a case study pareto optimality, theory...