Multi-objective Pareto optimization of bone drilling process using NSGA II algorithm

Multi Objective Optimization of Drilling Process Variables Using Genetic Algorithm for Precision Drilling Operation

The aim of this paper is to utilise genetic algorithm approach to investigate the effect of CNC drilling process variables such as spindle speed, drill diameter, material thickness, and feed rate on thrust force and torque generated during the drilling of mild steel plate using H.S.S drill. To find out the relationship between drilling process variable on thrust force and torque generated to th...

A Fast Elitist Multi-Objective Genetic Algorithm: NSGA-II

Simultaneous high hydrogen content-synthesis gas production and in-situ CO2 removal via sorption-enhanced reaction process: modeling, sensitivity analysis and multi-objective optimization using NSGA-II algorithm

The main focus of this study is improvement of the steam-methane reforming (SMR) process by in-situ CO2 removal to produce high hydrogen content synthesis gas. Sorption-enhanced (SE) concept is applied to improve process performance. In the proposed structure, the solid phase CO2 adsorbents and pre-reformed gas stream are introduced to a gas-flowing solids-fixed bed reactor (GFSFBR). One dimens...

multi-objective optimization of vision metrology camera placement based on pareto front concept by nsga-ii method

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...

A Fast Elitist Non-Dominated Sorting Genetic Algorithm for Multi-Objective Optimization: NSGA-II

Abstract. Multi-objective evolutionary algorithms which use non-dominated sorting and sharing have been mainly criticized for their (i) computational complexity (where is the number of objectives and is the population size), (ii) non-elitism approach, and (iii) the need for specifying a sharing parameter. In this paper, we suggest a non-dominated sorting based multi-objective evolutionary algor...

Synchronous R-NSGA-II: An Extended Preference-Based Evolutionary Algorithm for Multi-Objective Optimization

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...