Abstract
A GPU-based Multigroup Genetic Algorithm was proposed, which parallelized the traditional genetic algorithm with a coarse-grained architecture island model. The original population is divided into several subpopulations to simulate different living environments, thus increasing species richness. For each subpopulation, different mutation rates were adopted, and the crossover results were optimized by combining the crossover method based on distance. The adaptive mutation strategy based on the number of generations was adopted to prevent the algorithm from falling into the local optimal solution. An elite strategy was adopted for outstanding individuals to retain their superior genes. The algorithm was implemented with CUDA/C, combined with the powerful parallel computing capabilities of GPUs, which greatly improved the computing efficiency. It provided a new solution to the TSP problem.
Highlights
Introduction e Traveling SalesmanProblem (TSP) is one of the essential problems in computer science. e mathematical description is as follows
Genetic Algorithm (GA) is a method to find the optimal solution by simulating the natural evolution process. e principle of GA is simple, operability is strong, and it is excellent for global searching, so it is widely used in solving Traveling SalesmanProblem (TSP)
E mutation operation improves the local search ability of the GA and promotes the result to converge to the optimal solution, while the adaptive mutation algorithm ensures the diversity of species in the later stage of evolution and prevents the algorithm from falling into the local optimal solution
Summary
Its key thought is natural selection: individuals with higher fitness in a population can survive and reproduce the generation. Evolution usually starts with a randomly generated population of individuals and is an iterative process. The fitness of each individual in the population is evaluated. Individuals with high fitness from the current population are selected, and the genome of each individual is modified (crossed and mutated) to form a new generation. Five major components in the GA initial population, fitness, selection, crossover, and mutation are explained as follows. Selection: select a pair of individuals with higher fitness from the population as parents of the generation. When the specified number of generations is reached, the population is screened to find the optimal solution. We developed an algorithm based on the island model; divide individuals into several subpopulations. After reaching the specified number of generations, we search and output the optimal solution in all groups
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