Abstract
An improved quantum inspired immune clone optimization algorithm is proposed for optimization problem. It is proposed based on the immune clone algorithm and quantum computing theory. The algorithm adopts the quantum bit to express the chromosomes, and uses the quantum gate updating to implement evolutionary of population which can take advantage of the parallelism of quantum computing and the learning, memory capability of the immune system. Quantum observing entropy is introduced to evaluate the population evolutionary level, and relevant parameters are adjusted according to the entropy value. The proposed algorithm is tested on few benchmark optimization functions and the results are compared with other existing algorithms. The simulation results show that the proposed algorithm has better convergence, robustness and precision.
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Rao, A.C.S., Dara, S., Banka, H. (2016). An Improved Quantum Inspired Immune Clone Optimization Algorithm. In: Panigrahi, B., Suganthan, P., Das, S., Satapathy, S. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2015. Lecture Notes in Computer Science(), vol 9873. Springer, Cham. https://doi.org/10.1007/978-3-319-48959-9_8
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DOI: https://doi.org/10.1007/978-3-319-48959-9_8
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