This paper presents a simple but efficient algorithm for reducing the computation time of genetic algorithm (GA) and its variants. The proposed algorithm is motivated by the observation that genes common to all the individuals of a GA have a high probability of surviving the evolution and ending up being part of the final solution; as such, they can be saved away to eliminate the redundant computations at the later generations of a GA. To evaluate the performance of the proposed algorithm, we use it not only to solve the traveling salesman problem but also to provide an extensive analysis on the impact it may have on the quality of the end result. Our experimental results indicate that the proposed algorithm can significantly reduce the computation time of GA and GA-based algorithms while limiting the degradation of the quality of the end result to a very small percentage compared to traditional GA.

Abstract. This paper presents an efficient method for speeding up ant colony optimization (ACO) in solving the color image segmentation problem. The proposed method is inspired by the heuristics of image segmentation to reduce the computation time. To evaluate the performance of the proposed method, we applied the method on well-known test images. Our experimental results shows that the proposed method can significantly reduce the computation time about 19% to 45%.

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It is an important trend to apply the metaheuristics, such as ant colony optimization (ACO ), to data clustering. In general, the ACO for data clustering can accomplish better quality of cluster ing. In this paper, we proposed an improved ACO , to enhance the efficiency of ACO for data clustering. It is based on the assump tion that there are at least one or more neighbors belong to the same cluster in the L nearest neighbors of each instance. It mod ifies the operation of constructing solution to reduce the compu tation time of Euclidean distance. The experimental results show that the L-NNACO is faster than ACO about 38% to 54% • In addition, the L-NNACO is with greater or equal accuracy to the ACO for the various datasets of real world.