this paper uses integrated data envelopment analysis (dea) models to rank all extreme and non-extreme efficient decision making units (dmus) and then applies integrated dea ranking method as a criterion to modify genetic algorithm (ga) for finding pareto optimal solutions of a multi objective programming (mop) problem. the researchers have used ranking method as a shortcut way to modify ga to d...

numerous problems encountered in real life cannot be actually formulated as a single objective problem; hence the requirement of multi-objective optimization (moo) had arisen several years ago. due to the complexities in such type of problems powerful heuristic techniques were needed, which has been strongly satisfied by swarm intelligence (si) techniques. particle swarm optimization (pso) has ...

This paper uses integrated Data Envelopment Analysis (DEA) models to rank all extreme and non-extreme efficient Decision Making Units (DMUs) and then applies integrated DEA ranking method as a criterion to modify Genetic Algorithm (GA) for finding Pareto optimal solutions of a Multi Objective Programming (MOP) problem. The researchers have used ranking method as a shortcut way to modify GA to d...

majority of models in location literature are based on assumptions such as point demand, absence of competitors, as well as monopoly in location, products, and services. however in real-world applications, these assumptions are not well-matched with reality. in this study, a new mixed integer nonlinear programming model based on weighted goal programming approach is proposed to maximize the cap...

in this paper, a multi-objective reconfiguration problem has been solved simultaneously by a modified ant colony optimization algorithm. two objective functions, real power loss and energy not supplied index (ens), were utilized. multi-objective modified ant colony optimization algorithm has been generated by adding non-dominated sorting technique and changing the pheromone updating rule of ori...

a wind turbine transformer (wtt) is designed using a 3d wound core while the transformer’s total owning cost (toc) and its inrush current performance realized as the two objective functions in a multi-objective optimization process. multi-objective genetic algorithm is utilized to derive pareto optimal solutions. the effects of inrush current improvement on other operating and design parameters...

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 introduced‎. ‎in this approach‎, ‎first a discretized form of the time-control space is considered and then‎, ‎a piecewise linear control and a piecewise linear trajectory are obtained from the discretized time-control space using ...

Evolutionary optimization algorithms have been used to solve multiple objective problems. However, most of these methods have focused on search a sufficient Pareto front, and no efforts are made to explore the diverse Pareto optimal solutions corresponding to a Pareto front. Note that in semi-obnoxious facility location problems, diversifying Pareto optimal solutions is important. The paper the...

Pareto-optimal solutions are one of the most important and well-studied solution concepts in multi-objective optimization. Often the enumeration of all Pareto-optimal solutions is used to filter out unreasonable trade-offs between different criteria. While in practice, often only few Pareto-optimal solutions are observed, for almost every problem with at least two objectives there exist instanc...