abstract atrak basin is an agricultural area in north khorasan province that the most farmers are using from groundwater resources for irrigation their crops. decreasing rainfall in recent years in this area, competition between urban, agriculture and industrial consumers has increased for water acquisition. in present study, game theory was applied for groundwater resources management of atrak...

In Data Envelopment Analysis (DEA), identification of the Pareto-efficient frontier of an empirical production possibility set is a prerequisite step toward determining rates of change of outputs with changes in inputs along its piecewise linear facets. These rates of change, which will be different on different facets, have important economic and managerial implications in trade-off analysis, ...

The burgeoning area of security games has focused on real-world domains where security agencies protect critical infrastructure from a diverse set of adaptive adversaries. There are security domains where the payoffs for preventing the different types of adversaries may take different forms (seized money, reduced crime, saved lives, etc) which are not readily comparable. Thus, it can be difficu...

Minimum Spanning Tree (MST) problem is of high importance in network optimization. The multi-criteria MST (mc-MST) is a more realistic representation of the practical problem in the real-world, but it is diicult for the traditional network optimization technique to deal with. In this paper, a genetic algorithm (GA) approach is developed to deal with this problem. Without neglecting its network ...

This paper investigates the use of a multi-objective approach for evolving artificial neural networks that act as controllers for the legged locomotion of a quadrupedal robot simulated in a 3-dimensional, physics-based environment. The Pareto-frontier Differential Evolution (PDE) algorithm is used to generate a Pareto optimal set of artificial neural networks that optimize the conflicting objec...

Performance guarantees for online learning algorithms typically take the form of regret bounds, which express that the cumulative loss overhead compared to the best expert in hindsight is small. In the common case of large but structured expert sets we typically wish to keep the regret especially small compared to simple experts, at the cost of modest additional overhead compared to more comple...

We describe techniques for visualizing the Pareto optimal set that can be used if the multiobjective optimization problem considered has more than two objective functions. The techniques discussed can be applied in the framework of both MCDM and EMO approaches. First, lessons learned from methods developed for biobjective problems are considered. Then, visualization techniques for convex multio...

An effective approach to decision support in multicriteria decision making (MCDM) problems characterized by three to eight decision criteria is described. The approach is based on approximating the feasible set in the criterion space (or a broader criterion set, which has the same Pareto frontier) and visualization of the Pareto frontier by interactive displaying bi-criterion slices of this set...

Heuristic optimization provides a robust and efficient approach for extracting approximate solutions of multi-objective problems because of their capability to evolve a set of non-dominated solutions distributed along the Pareto frontier. The convergence rate and suitable diversity of solutions are of great importance for multi-objective evolutionary algorithms. The focu...

This paper reports an evolutionary approach called Scatter Search, that uses the concept of Pareto optimality to obtain a good approximate Pareto frontier. In order to designate a subset of strategies to be a reference solutions a choice function called Kramer Selection is used. A variant of measure of Kemen-Snell may be used, in our case study, in order to find a diverse set to complement the ...