Classical evolutionary multi-objective optimization algorithms aim at finding an approximation of the entire set of Pareto optimal solutions. By considering the preferences of a decision maker within evolutionary multi-objective optimization algorithms, it is possible to focus the search only on those parts of the Pareto front that satisfy his/her preferences. In this paper, an extended prefere...

nowadays, the subject of vision metrology network design is local enhancement of the existing network. in the other words, it has changed from first to third order design concept. to improve the network, locally, some new camera stations should be added to the network in drawback areas. the accuracy of weak points is enhanced by the new images, if the related vision constraints are satisfied si...

In this paper, we have studied a multiobjective hybrid flowshop scheduling problem where n independent jobs should be executed in a hybrid assembly line. The aim of our work is to optimize the makespan and the total tardiness of the whole production. A simulation based optimization algorithm is proposed here to solve this problem. It is a combination of the simulation software ARENA and the FLC...

This paper presents an application of elitist non-dominated sorting genetic algorithm (NSGA-II) for solving a multi-objective reactive power market clearing (MO-RPMC) model. In this MO-RPMC model, two objective functions such as total payment function (TPF) for reactive power support from generators/synchronous condensers and voltage stability enhancement index (VSEI) are optimized simultaneous...

This paper considers the job scheduling problem in virtual manufacturing cells (VMCs) with the goal of minimizing two objectives namely, makespan and total travelling distance. To solve this problem two algorithms are proposed: traditional non-dominated sorting genetic algorithm (NSGA-II) and knowledge-based non-dominated sorting genetic algorithm (KBNSGA-II). The difference between these algor...

This paper presents a multi-objective resource-constrained project scheduling problem with positive and negative cash flows. The net present value (NPV) maximization and making span minimization are this study objectives. And since this problem is considered as complex optimization in NP-Hard context, we present a mathematical model for the given problem and solve three evolutionary algorithms;...

A major difficulty in the use of metaheuristics (i.e. evolutionary and particle swarm algorithms) to deal with multi-objective optimization problems is the choice of a convenient point at which to stop computation. Indeed, it is difficult to find the best compromise between the stopping criterion and the algorithm performance. This paper addresses this issue using the Non-dominated Sorting Gene...

Time-dependent Vehicle Routing Problem is one of the most applicable but least-studied variants of routing and scheduling problems. In this paper, a novel mathematical formulation of time-dependent vehicle routing problems with heterogeneous fleet, hard time widows and multiple depots, is proposed. To deal with the traffic congestions, we also considered that the vehicles are not forced to come...

with the increase in population and consequent increasing needs of society, land use planning is of particular importance. land use planning due to being involved with several conflicting aims, multi- objective evolutionary algorithm would be a useful tool to solve land use planning. but use of these algorithms should be examined according to the type of issues. in the study, addition to introd...

This research aims to predict PV output power by using different neuro-evolutionary methods. The proposed approach was evaluated by a data set, which was collected at 5-minute intervals in the photovoltaic laboratory of Niroo Research Institute of Iran (Tehran). The data has been divided into three intervals based on the amount of solar irradiation, and different neural networks were used for p...