Abstract
Many complex self-adaptive phenomena in the nature often give us inspirations. Some scholars are inspired from these natural bio-based phenomena and proposed many nature-inspired optimization algorithms. When solving some complex problems which cannot be solved by the traditional optimization algorithms easily, the nature-inspired optimization algorithms have their unique advantages. Inspired by the transmission mode of seeds, a novel evolutionary algorithm named Bean Optimization Algorithm (BOA) is proposed, which can be used to solve complex optimization problems by simulating the adaptive phenomenon of plants in the nature. BOA is the combination of nature evolutionary tactic and limited random search. It has stable robust behavior on explored tests and stands out as a promising alternative to existing optimization methods for engineering designs or applications. Through research and study on the relevant research results of biostatistics, a novel distribution model of population evolution for BOA is built. This model is based on the negative binomial distribution. Then a kind of novel BOA algorithm is presented based on the distribution models. In order to verify the validity of the Bean Optimization Algorithm based on negative binomial distribution model (NBOA), function optimization experiments are carried out, which include four typical benchmark functions. The results of the experiments are made a comparative analysis with that of particle swarm optimization (PSO) and BOA. From the results analysis, we can see that the performance of NBOA is better than that of PSO and BOA. We also conduct a research on the characters of NBOA. A contrast analysis is carried out to verify the research conclusions about the relations between the algorithm parameters and its performance.
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Feng, T., Xie, Q., Hu, H., Song, L., Cui, C., Zhang, X. (2015). Bean Optimization Algorithm Based on Negative Binomial Distribution. In: Tan, Y., Shi, Y., Buarque, F., Gelbukh, A., Das, S., Engelbrecht, A. (eds) Advances in Swarm and Computational Intelligence. ICSI 2015. Lecture Notes in Computer Science(), vol 9140. Springer, Cham. https://doi.org/10.1007/978-3-319-20466-6_9
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DOI: https://doi.org/10.1007/978-3-319-20466-6_9
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