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A behavior-selection based Rao algorithm and its applications to power system economic load dispatch problems
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AI
DOI：10.1007/s10489-021-02849-7.png)
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Abstract
En 中文
To improve the search efficiency of Rao algorithm, a behavior-selection based Rao Algorithm is proposed in this paper. Our proposed algorithm is a parameter-less and metaphor-less algorithm, which has three major improvements. (i) Three perturbation operators for offspring population are proposed to effectively balance algorithm exploitation and exploration capability. (ii) A behavior selection strategy is proposed for evaluating the three new perturbation operators and the original operators of Rao Algorithms. The upper confidence bound algorithm is modified and used for calculating the future value of the operators. (iii) A new mapping strategy is designed to increase the diversity of the solutions. The behavior-selection based Rao algorithm is tested based on some benchmark functions and the power system economic load dispatch problems, compared to the other well-known algorithms, the proposed algorithm has a competitive superiority in terms of convergence performance and global search capability.
Keywords:
Rao algorithm
Metaphor-less
Dynamic behavior selection
Upper confidence bound
Economic load dispatch
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