Sparrow search algorithm based on cosine similarity and random multi-chaotic perturbation

Guangyang Li; Qian Qian; Yong Feng; Yunfa Fu

doi:10.1117/12.2644679

10 November 2022 Sparrow search algorithm based on cosine similarity and random multi-chaotic perturbation

Guangyang Li, Qian Qian, Yong Feng, Yunfa Fu

Proceedings Volume 12301, 6th International Conference on Mechatronics and Intelligent Robotics (ICMIR2022); 1230110 (2022) https://doi.org/10.1117/12.2644679
Event: 6th International Conference on Mechatronics and Intelligent Robotics, 2022, Kunming, China

Abstract

Aiming at the problems of sparrow algorithm (SSA), such as easy to fall into local extremum, uneven initial population distribution and slow convergence in late iteration, a sparrow search algorithm (CMSSA) was proposed, which combined cosine similarity and random multi-chaotic disturbance. The algorithm firstly integrates the reverse learning strategy and initializes the population by using the population uniform adjustment strategy of cosine similarity to ensure the uniformity and richness of the population, so that the algorithm can better search for the global optimal solution. Secondly, a random selection mechanism of multi-chaos local search strategy is used to take advantages from different disturbance states of multiple chaos models. Randomly selected chaos maps are used in each iteration to perturb individuals and help SSA get rid of local extremums. Simulation results show that compared with other intelligent algorithms, CMSSA achieves better results in robustness and optimization accuracy.

Citation Download Citation

Guangyang Li, Qian Qian, Yong Feng, and Yunfa Fu "Sparrow search algorithm based on cosine similarity and random multi-chaotic perturbation", Proc. SPIE 12301, 6th International Conference on Mechatronics and Intelligent Robotics (ICMIR2022), 1230110 (10 November 2022); https://doi.org/10.1117/12.2644679

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KEYWORDS

Optimization (mathematics)

Chaos

Computer simulations

Particle swarm optimization

Complex systems

Lithium

Statistical analysis

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