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.
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Acknowledgements
This work was supported by JSPS KAKENHI Grant Number JP15K00347, Grant-in-Aid for Scientific Research (C). We would like to thank them for supporting our work.
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Takahashi, R., Nakamura, Y., Ibaraki, T. (2018). Experimental Evaluation of ACO for Continuous Domains to Solve Function Optimization Problems. In: Dorigo, M., Birattari, M., Blum, C., Christensen, A., Reina, A., Trianni, V. (eds) Swarm Intelligence. ANTS 2018. Lecture Notes in Computer Science(), vol 11172. Springer, Cham. https://doi.org/10.1007/978-3-030-00533-7_30
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