Abstract
Bio-inspired optimization algorithms have been widely used to solve various scientific and engineering problems. Inspired by biology life cycle, this paper presents a novel optimization algorithm called Lifecycle-based Swarm Optimization. LSO algorithm simulates biologic life cycle process through six optimization operators: chemotactic, assimilation, transposition, crossover, selection and mutation. Experiments were conducted on 7 unimodal functions. The results demonstrate remarkable performance of the LSO algorithm on those functions when compared to several successful optimization techniques.
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Shen, H., Niu, B., Zhu, Y., Chen, H. (2013). Optimization Algorithm Based on Biology Life Cycle Theory. In: Huang, DS., Jo, KH., Zhou, YQ., Han, K. (eds) Intelligent Computing Theories and Technology. ICIC 2013. Lecture Notes in Computer Science(), vol 7996. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39482-9_65
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DOI: https://doi.org/10.1007/978-3-642-39482-9_65
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