Abstract
Genetic algorithm (GA) is widely applied to optimal path planning of mobile robots. In this work, an adaptive genetic algorithm (AGA) is proposed, which is expected to solve some difficulties that conventional GA inevitably faces, including local optimum in the early stage, too lower convergence speed, and large complicated computation process. Sine-AGA denotes that the cross probability and mutation probability could realize the adaptive adjustments by conforming to a set of sine functions, guaranteeing to achieve a preservation scheme for optimal individuals. The whole iterative process consists of path coding, choices of fitness function, design of reproduction, crossover and mutation operations, and the setting of initial parameters of AGA. Simulation under the same condition indicates that the convergence performances of average solution and optimal solution are highly enhanced, better than the ones obtained through the pure AGA scheme, and the convergence speed is proved to be increased as expected.
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