In this paper, a kind of Partheno Genetic Algorithm (PGA) based on Path Representation scheme is proposed for solving Traveling Salesman Problem (TSP). This algorithm employs only mutation and selection operators to p...In this paper, a kind of Partheno Genetic Algorithm (PGA) based on Path Representation scheme is proposed for solving Traveling Salesman Problem (TSP). This algorithm employs only mutation and selection operators to produce the offspring,instead of traditional crossover operator. A specfiic mutation operator is designed combining the insertion operator with reversion operator,which ensures its strong searching capability. This algorithm simulates the recurrence of nature evolution process, while providing fewer control parameters. Experiments based on Ciunese 144 cities(CHN144)and 7 instances selected from TSPLIB are used to test the performance of this algorithm. They prove that it can reach the satisfying optimization at a faster speed. Especially,for the CHN144,the best path it finds is better than any other available one.展开更多
For overcoming the weakness of the population climbing evolutionary algorithm,we design a new algorithmthat randomly chooses many parents from the population to recombine and the worse individuals to mutate so as to d...For overcoming the weakness of the population climbing evolutionary algorithm,we design a new algorithmthat randomly chooses many parents from the population to recombine and the worse individuals to mutate so as to de-crease the size of population, accelerate the convergence rate and improve the performance. The results of numericalexperiments including seven non-linear optimization problems show that the new algorithm is characteristic of robustand high efficiency,and can quickly find the global solutions which are better than those got by MATLAB and othermethods.展开更多
文摘In this paper, a kind of Partheno Genetic Algorithm (PGA) based on Path Representation scheme is proposed for solving Traveling Salesman Problem (TSP). This algorithm employs only mutation and selection operators to produce the offspring,instead of traditional crossover operator. A specfiic mutation operator is designed combining the insertion operator with reversion operator,which ensures its strong searching capability. This algorithm simulates the recurrence of nature evolution process, while providing fewer control parameters. Experiments based on Ciunese 144 cities(CHN144)and 7 instances selected from TSPLIB are used to test the performance of this algorithm. They prove that it can reach the satisfying optimization at a faster speed. Especially,for the CHN144,the best path it finds is better than any other available one.
文摘For overcoming the weakness of the population climbing evolutionary algorithm,we design a new algorithmthat randomly chooses many parents from the population to recombine and the worse individuals to mutate so as to de-crease the size of population, accelerate the convergence rate and improve the performance. The results of numericalexperiments including seven non-linear optimization problems show that the new algorithm is characteristic of robustand high efficiency,and can quickly find the global solutions which are better than those got by MATLAB and othermethods.
