Scheduling Robotic Actions by Genetic Algorithms

1996 ◽  
Vol 5 (2) ◽  
pp. 191-204
Author(s):  
R. J. Abbott ◽  
M. L. Campbell ◽  
W. C. Krenz
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Real World ◽  
Hybrid Genetic Algorithm ◽  
Two Dimensional ◽  
The Real ◽  
Short Period

A hybrid genetic algorithm is used to schedule tasks for a satellite that can be modeled as a robot whose goal is to retrieve objects from a two-dimensional field. The objective is to find a schedule that maximizes the value of objects retrieved. Typical of the real-world tasks to which this corresponds is the scheduling of ground contacts for a communications satellite. An important feature of our application is that the amount of time available for running the scheduler is not necessarily known in advance. This requires that the scheduler produce reasonably good results after a short period, but that it also continue to improve its results if allowed to run for a longer period. We satisfy this requirement by developing what we call a sustainable genetic algorithm.

Two-dimensional nesting system based on hybrid genetic algorithm

2009 ◽  
Vol 14 (1) ◽  
pp. 60-64
Author(s):  
Qingming Wu ◽  
Wei Yang ◽  
Qiang Zhang ◽  
Junjie Zhou
Keyword(s):  
Genetic Algorithm ◽  
Hybrid Genetic Algorithm ◽  
Two Dimensional

A Hybrid Genetic Algorithm for Optimization of Two-Dimensional Cutting-Stock Problem

2012 ◽  
pp. 56-71
Author(s):  
Ahmed Mellouli ◽  
Faouzi Masmoudi ◽  
Imed Kacem ◽  
Mohamed Haddar
Keyword(s):  
Genetic Algorithm ◽  
Linear Programming ◽  
Production Cost ◽  
Programming Model ◽  
Hybrid Genetic Algorithm ◽  
Cutting Stock Problem ◽  
Cutting Stock ◽  
Two Dimensional ◽  
Genetic Approach ◽  
Cutting Pattern

In this paper, the authors present a hybrid genetic approach for the two-dimensional rectangular guillotine oriented cutting-stock problem. In this method, the genetic algorithm is used to select a set of cutting patterns while the linear programming model permits one to create the lengths to produce with each cutting pattern to fulfill the customer orders with minimal production cost. The effectiveness of the hybrid genetic approach has been evaluated through a set of instances which are both randomly generated and collected from the literature.

Genetic Algorithms and Multimodal Search

2011 ◽  
pp. 231-249
Author(s):  
Marcos Gestal ◽  
José Manuel Vázquez Naya ◽  
Norberto Ezquerra
Keyword(s):  
Genetic Algorithms ◽  
Evolutionary Computation ◽  
Unique Solution ◽  
Real World ◽  
Multiple Solutions ◽  
Global Solutions ◽  
Search Space ◽  
The Real ◽  
Local Solutions ◽  
Multimodal Problems

Traditionally, the Evolutionary Computation (EC) techniques, and more specifically the Genetic Algorithms (GAs), have proved to be efficient when solving various problems; however, as a possible lack, the GAs tend to provide a unique solution for the problem on which they are applied. Some non global solutions discarded during the search of the best one could be acceptable under certain circumstances. Most of the problems at the real world involve a search space with one or more global solutions and multiple local solutions; this means that they are multimodal problems and therefore, if it is desired to obtain multiple solutions by using GAs, it would be necessary to modify their classic functioning outline for adapting them correctly to the multimodality of such problems. The present chapter tries to establish, firstly, the characterisation of the multimodal problems will be attempted. A global view of some of the several approaches proposed for adapting the classic functioning of the GAs to the search of mu ltiple solutions will be also offered. Lastly, the contributions of the authors and a brief description of several practical cases of their performance at the real world will be also showed.

Towards a Hybrid Solver: Integration of a Genetic Algorithm Within “DSIDES”

2002 ◽  
Author(s):  
Shuiwei Xie ◽  
Warren F. Smith
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Linear Programming ◽  
Decision Support ◽  
Hybrid Genetic Algorithm ◽  
Systems Design ◽  
Nonlinear Problems ◽  
The Body ◽  
Hybrid Solver ◽  
Decision Based Design

In contributing to the body of knowledge for decision-based design, the work reported in this paper has involved steps towards building a hybrid genetic algorithm to address systems design. Highlighted is a work in progress at the Australian Defence Force Academy (ADFA). A genetic algorithm (GA) is proposed to deal with discrete aspects of a design model (e.g., allocation of space to function) and a sequential linear programming (SLP) method for the continuous aspects (e.g., sizing). Our historical Decision Based Design (DBD) tool has been the code DSIDES (Decision Support In the Design of Engineering Systems). The original functionality of DSIDES was to solve linear and non-linear goal programming styled problems using linear programming (LP) and sequential (adaptive) linear programming (SLP/ALP). We seek to enhance DSIDES’s solver capability by the addition of genetic algorithms. We will also develop the appropriate tools to deal with the decomposition and synthesis implied. The foundational paradigm for DSIDES, which remains unchanged, is the Decision Support Problem Technique (DSPT). Through introducing genetic algorithms as solvers in DSIDES, the intention is to improve the likelihood of finding the global minimum (for the formulated model) as well as the ability of dealing more effectively with nonlinear problems which have discrete variables, undifferentiable objective functions or undifferentiable constraints. Using some numerical examples and a practical ship design case study, the proposed GA based method is demonstrated to be better in maintaining diversity of populations, preventing premature convergence, compared with other similar GAs. It also has similar effectiveness in finding the solutions as the original ALP DSIDES solver.

Optimal Design of 6-UPU Parallel Mechanism

2011 ◽  
Vol 480-481 ◽  
pp. 1055-1060
Author(s):  
Guang Hua Wu ◽  
Lie Hang Gong ◽  
Xin Wei Ji ◽  
Zhong Jun Wu ◽  
Yong Jun Gai
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Optimal Design ◽  
Objective Function ◽  
Parallel Mechanism ◽  
Design Space ◽  
Jacobian Matrix ◽  
Optimal Model ◽  
The Real ◽  
Universal Joints

The methodology of the optimal design for the 6-UPU parallel mechanism (PM) is presented based on genetic algorithms. The optimal index which expressed by Jacobian matrix of the PM is first deduced. An optimal model is established, in which the kinematic dexterity of a parallel mechanism is considered as the objective function. The design space, the limiting length of the electric actuators and the limit angles of universal joints are taken as constraints. The real-encoding genetic algorithm is applied to the optimal design of a parallel mechanism, which is proved the validity and advantage for the optimal design of a similar mechanism.

scholarly journals A Two-Dimensional Genetic Algorithm and Its Application to Aircraft Scheduling Problem

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Ming-Wen Tsai ◽  
Tzung-Pei Hong ◽  
Woo-Tsong Lin
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Scheduling Problem ◽  
Two Dimensional ◽  
Dimensional Representation ◽  
One Dimensional ◽  
Time Table ◽  
Feasible Solutions ◽  
Crossover And Mutation ◽  
Airline Company

Genetic algorithms have become increasingly important for researchers in resolving difficult problems because they can provide feasible solutions in limited time. Using genetic algorithms to solve a problem involves first defining a representation that describes the problem states. Most previous studies have adopted one-dimensional representation. Some real problems are, however, naturally suitable to two-dimensional representation. Therefore, a two-dimensional encoding representation is designed and the traditional genetic algorithm is modified to fit the representation. Particularly, appropriate two-dimensional crossover and mutation operations are proposed to generate candidate chromosomes in the next generations. A two-dimensional repairing mechanism is also developed to adjust infeasible chromosomes to feasible ones. Finally, the proposed approach is used to solve the scheduling problem of assigning aircrafts to a time table in an airline company for demonstrating the effectiveness of the proposed genetic algorithm.

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