The Integration of Genetic and Ant Colony Algorithm in a Hybrid Approach
Keywords:genetic algorithm, ant colony algorithm, hybrid approach, optimization
The genetic algorithm has many difficulties in solving path plan optimization. Problems like the lack of appropriate setting and settings for different applications. This research work proposes an improved genetic algorithm that combines ant colony algorithm for path optimization. The goal is to eliminate the parameterization uncertainty of the traditional genetic algorithm by introducing the ant colony optimizer. Through the hybrid algorithm, optimization is achieved in moving from point to point, reducing the total distance and improving the travel time. With the help of the global search features of the ant colony optimizer and the stepwise search features, the optimal parameters of the genetic are improved, and the finding of the optimal solution in the global application of the hybrid algorithm is accelerated. The experimental results show that the proposed algorithm can automatically obtain better parameters, especially in its initial values, having good solution accuracy, robustness and significantly better efficiency. The hybrid algorithm was tested on a TSP problem but has applications in spatial mechanics systems such as CNC machining, robotic systems and Coordinate Measuring Machines (CMM). A CMM application is also presented in the results of this paper. Experimental measurements show that up to 40% path planning optimization can be achieved compared to a simple genetic algorithm.
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