Abstract
To solve the problem that it is difficult to determine the initial location of the fruit fly in Fruit Fly Optimization Algorithm (FOA), an improved FOA, Adaptive Fruit Fly Optimization Algorithm (AFOA), is proposed in this paper. According to the ranges of variables to be optimized, AFOA can set the initial location of the fruit fly automatically and adjust the step value adaptively during iteration. Finally, the proposed algorithm is applied to Himmelblau’s non-linear optimization problem and time series prediction using Echo State Network (ESN). The experimental results imply that AFOA is effective and also show better ability in adaptation and optimization than traditional FOA, Particle Swarm Optimization (PSO) and Genetic Algorithm (GA).
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Project Supported by the Fundamental Research Funds for the Central Universities, China (No. CDJZR12170006).
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Zhu, Q., Cai, Z., Dai, W. (2016). An Adaptive Optimization Algorithm Based on FOA. In: Jia, Y., Du, J., Li, H., Zhang, W. (eds) Proceedings of the 2015 Chinese Intelligent Systems Conference. Lecture Notes in Electrical Engineering. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-48365-7_10
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DOI: https://doi.org/10.1007/978-3-662-48365-7_10
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