scholarly journals Performance Analysis of Different Operators in Genetic Algorithm for Solving Continuous and Discrete Optimization Problems

2021 ◽  
Author(s):  
Shilun Song ◽  
Hu Jin ◽  
Qiang Yang
Keyword(s):  
Genetic Algorithm ◽  
Performance Analysis ◽  
Discrete Optimization ◽  
Optimization Problems ◽  
Discrete Optimization Problems

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.

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.

Discrete Social Spider Algorithm for Solving Traveling Salesman Problem

2021 ◽  
pp. 2150020
Author(s):  
Asieh Khosravanian ◽  
Mohammad Rahmanimanesh ◽  
Parviz Keshavarzi
Keyword(s):  
Genetic Algorithm ◽  
Discrete Optimization ◽  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Fitness Function ◽  
Traveling Salesman ◽  
The Other ◽  
Discrete Optimization Problem ◽  
Social Spider ◽  
Social Spider Algorithm

The Social Spider Algorithm (SSA) was introduced based on the information-sharing foraging strategy of spiders to solve the continuous optimization problems. SSA was shown to have better performance than the other state-of-the-art meta-heuristic algorithms in terms of best-achieved fitness values, scalability, reliability, and convergence speed. By preserving all strengths and outstanding performance of SSA, we propose a novel algorithm named Discrete Social Spider Algorithm (DSSA), for solving discrete optimization problems by making some modifications to the calculation of distance function, construction of follow position, the movement method, and the fitness function of the original SSA. DSSA is employed to solve the symmetric and asymmetric traveling salesman problems. To prove the effectiveness of DSSA, TSPLIB benchmarks are used, and the results have been compared to the results obtained by six different optimization methods: discrete bat algorithm (IBA), genetic algorithm (GA), an island-based distributed genetic algorithm (IDGA), evolutionary simulated annealing (ESA), discrete imperialist competitive algorithm (DICA) and a discrete firefly algorithm (DFA). The simulation results demonstrate that DSSA outperforms the other techniques. The experimental results show that our method is better than other evolutionary algorithms for solving the TSP problems. DSSA can also be used for any other discrete optimization problem, such as routing problems.

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