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• W2894951879 abstract "A new Ant Colony Optimization (text {ACO}_text {B}) to solve function optimization problems (FOP) is evaluated experimentally by using ten standard multimodal test functions such as Michaelwicz’s function. In (text {ACO}_text {B}), ants search for solutions in binary search space and can improve the accuracy of solutions by the stepwise localization of search space. Experiments show that (text {ACO}_text {B}) can keep the balance between accuracy and efficiency to search for optimum solutions, and that it can reduce the population size of (text {ACO}_text {R}), which is a preceding ACO based on real search space. It is also shown that Covariance Matrix Adaptation-Evolution Strategy (CMA-ES) is superior in computational time but lacks the accuracy of solutions, and that Genetic Algorithm (GA) is superior in the ratio of getting the optimum solutions but weak in the performance." @default.
• W2894951879 created "2018-10-12" @default.
• W2894951879 creator A5031909273 @default.
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• W2894951879 date "2018-01-01" @default.
• W2894951879 modified "2023-09-27" @default.
• W2894951879 title "Experimental Evaluation of ACO for Continuous Domains to Solve Function Optimization Problems" @default.
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• W2894951879 doi "https://doi.org/10.1007/978-3-030-00533-7_30" @default.
• W2894951879 hasPublicationYear "2018" @default.
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