A Genetic Algorithm for Solving Mixed Discrete Optimization Problems

1993 ◽  
Author(s):  
Zhihang Qian ◽  
Jun Yu ◽  
Ji Zhou
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Discrete Optimization ◽  
High Stability ◽  
Optimization Problems ◽  
Global Optimum ◽  
Optimal Method ◽  
Simple Genetic Algorithms ◽  
Discrete Optimization Problems

Abstract A new optimal method based on genetic algorithms (GAs) is proposed here towards the mixed discrete optimization problems. This method has not only the advantages of high stability and wide adaptability but also a better chance of locating the global optimum. Its efficiency is much higher than that of simple genetic algorithms.

A Genetic Algorithm with the Mixed Heuristics for Traveling Salesman Problem

2015 ◽  
Vol 14 (01) ◽  
pp. 1550003 ◽  
Author(s):  
Yong Wang
Keyword(s):  
Genetic Algorithm ◽  
Traveling Salesman Problem ◽  
Discrete Optimization ◽  
Time Complexity ◽  
Optimization Problems ◽  
Traveling Salesman ◽  
Optimization Process ◽  
Hamiltonian Cycles ◽  
Improved Genetic Algorithm ◽  
Discrete Optimization Problems

Traveling salesman problem (TSP) is one of well-known discrete optimization problems. The genetic algorithm is improved with the mixed heuristics to resolve TSP. The first heuristics is the four vertices and three lines inequality, which is applied to the 4-vertex paths to generate the shorter Hamiltonian cycles (HC). The second local heuristics is executed to reverse the i-vertex paths with more than two vertices, which also generates the shorter HCs. It is necessary that the two heuristics coordinate with each other in the optimization process. The time complexity of the first and second heuristics are O(n) and O(n3), respectively. The two heuristics are merged into the original genetic algorithm. The computation results show that the improved genetic algorithm with the mixed heuristics can find better solutions than the original GA does under the same conditions.

Genetic Algorithm for Panel Cutting Stock on CUDA Platform

2013 ◽  
Vol 712-715 ◽  
pp. 2569-2575
Author(s):  
Wen Wu Xie ◽  
Tao Ning
Keyword(s):  
Genetic Algorithm ◽  
Discrete Optimization ◽  
High Performance ◽  
Large Scale ◽  
Optimization Problems ◽  
Raw Material ◽  
Cutting Stock ◽  
Steel Bars ◽  
Material Utilization ◽  
Discrete Optimization Problems

The problem of placing a number of specific shapes on a raw material in order to maximize material utilization is commonly encountered in the production of steel bars and plates, papers, glasses, etc. In this paper, we presented a genetic algorithm for steel grating nesting design. For application in large-scale discrete optimization problems, we also implemented this algorithm with CUDA based on parallel computation. Experimental results show that under genetic algorithm invoking with CUDA scheme, we can obtain satisfied solutions to steel grating nesting problem with high performance.

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