Abstract
Many bio-inspired algorithms have been proposed to solve optimization problems. However, there is still no conclusive evidence of superiority of particular algorithms in different problems, diverse experimental situations and varied testing scenarios. Here, eight methods are investigated through extensive experimentation in three problems: (1) benchmark functions optimization, (2) wind energy forecasting and (3) data clustering. Genetic algorithms, ant colony optimization, particle swarm optimization, artificial bee colony, firefly algorithm, cuckoo search algorithm, bat algorithm and self-adaptive cuckoo search algorithm are compared, concerning, the quality of solutions according to several performance metrics and convergence to best solution. A bio-inspired technique for automatic parameter tuning was developed to estimate the optimal values for each algorithm, allowing consistent performance comparison. Experiments with thousands of configurations, 12 performance metrics and Friedman and Nemenyi statistical tests consistently evidenced that cuckoo search works efficiently, robustly and superior to the other methods in the vast majority of experiments.
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Barbosa, C.E.M., Vasconcelos, G.C. (2018). Eight Bio-inspired Algorithms Evaluated for Solving Optimization Problems. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2018. Lecture Notes in Computer Science(), vol 10841. Springer, Cham. https://doi.org/10.1007/978-3-319-91253-0_28
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