A hybird self-learning method based on particle swarm optimization and salp swarm algorithm
| Autor: | Kunquan Shi, Angus Wu, Zhenlun Yang, Xuewen Wei, Meiling Qiu |
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| Rok vydání: | 2019 |
| Předmět: |
Mathematical optimization
education.field_of_study Optimization algorithm Computer science Population MathematicsofComputing_NUMERICALANALYSIS Particle swarm optimization ComputingMethodologies_ARTIFICIALINTELLIGENCE Probability model TheoryofComputation_LOGICSANDMEANINGSOFPROGRAMS Benchmark (computing) Learning methods Salp swarm algorithm Software_PROGRAMMINGLANGUAGES education Function optimization problems |
| Zdroj: | 2019 Tenth International Conference on Intelligent Control and Information Processing (ICICIP). |
| DOI: | 10.1109/icicip47338.2019.9012195 |
| Popis: | This paper presents a novel self-learning hybrid optimization algorithm based on the particle swarm optimization (PSO) algorithm and the salp swarm algorithm (SSA) algorithm, namely HSL-PSO-SSA, for solving the function optimization problems. In HSL-PSO-SSA, three search strategies based on the ideas of PSO and SSA are adopted and a probability model is designed to determine the probability of a search strategy being used to update an individual in the search population. The performance of the HSL-PSO-SSA is investigated on solving the unimodal and multimodal benchmark functions. From the experimental results, it is observed that the proposed HSL-PSO-SSA outperforms the compared algorithms including the standard PSO and the original SSA. |
| Databáze: | OpenAIRE |
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