scholarly journals Linear bilevel multiobjective optimization problem: Penalty approach

2017 ◽  
Vol 13 (5) ◽  
pp. 1-11
Author(s):  
Yibing Lv ◽  
◽  
Zhongping Wan ◽  
Keyword(s):  
Multiobjective Optimization ◽  
Optimization Problem ◽  
Multiobjective Optimization Problem ◽  
Penalty Approach

scholarly journals Bus Timetabling as a Fuzzy Multiobjective Optimization Problem Using Preference-based Genetic Algorithm

1970 ◽  
Vol 24 (3) ◽  
pp. 183-191 ◽  
Author(s):  
Surafel Luleseged Tilahun ◽  
Hong Choon Ong
Keyword(s):  
Genetic Algorithm ◽  
Multiobjective Optimization ◽  
Waiting Time ◽  
Third World ◽  
Optimization Problem ◽  
Vital Role ◽  
Single Frequency ◽  
Multiobjective Optimization Problem ◽  
Fuzzy Preference

Transportation plays a vital role in the development of a country and the car is the most commonly used means. However, in third world countries long waiting time for public buses is a common problem, especially when people need to switch buses. The problem becomes critical when one considers buses joining different villages and cities. Theoretically this problem can be solved by assigning more buses on the route, which is not possible due to economical problem. Another option is to schedule the buses so that customers who want to switch buses at junction cities need not have to wait long. This paper discusses how to model single frequency routes bus timetabling as a fuzzy multiobjective optimization problem and how to solve it using preference-based genetic algorithm by assigning appropriate fuzzy preference to the need of the customers. The idea will be elaborated with an example.

Multiobjective optimization of parallel kinematic mechanisms by the genetic algorithms

Robotica ◽  
2011 ◽  
Vol 30 (5) ◽  
pp. 783-797 ◽  
Author(s):  
Ridha Kelaiaia ◽  
Olivier Company ◽  
Abdelouahab Zaatri
Keyword(s):  
Genetic Algorithms ◽  
Multiobjective Optimization ◽  
High Speed ◽  
Optimization Problem ◽  
Multiobjective Optimization Problem ◽  
Dimensional Synthesis ◽  
Nsga Ii ◽  
Parallel Kinematic ◽  
Parallel Kinematic Mechanisms ◽  
Kinematic Mechanisms

SUMMARYIt is well known that Parallel Kinematic Mechanisms (PKMs) have an intrinsic dynamic potential (very high speed and acceleration) with high precision and high stiffness. Nevertheless, the choice of optimal dimensions that provide the best performances remains a difficult task, since performances strongly depend on dimensions. On the other hand, there are many criteria of performance that must be taken into account for dimensional synthesis, and which are sometimes antagonist. This paper presents an approach of multiobjective optimization for PKMs that takes into account several criteria of performance simultaneously that have a direct impact on the dimensional synthesis of PKMs. We first present some criteria of performance such as the workspace, transmission speeds, stiffness, dexterity, precision, as well as dynamic dexterity. Secondly, we present the problem of dimensional synthesis, which will be defined as a multiobjective optimization problem. The method of genetic algorithms is used to solve this type of multiobjective optimization problem by means of NSGA-II and SPEA-II algorithms. Finally, based on a linear Delta architecture, we present an illustrative application of this methodology to a 3-axis machine tool in the context of manufacturing of automotive parts.

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