A Modified Artificial Rabbit Optimization with Adaptive Diversity Golden Sine Search
This paper proposes an improved artificial rabbit optimization algorithm based on adaptive diversi-ty golden sine search to address the problems of low initial population diversity and susceptibility to local optima in solving complex optimization problems.Firstly,this paper introduces the number of Halton sequences of the quasi Monte Carlo method to increase the diversity of the initial population.Subsequently,in order to improve the search ability of the artificial rabbit optimization algorithm in the later stage of iteration and avoid the algo-rithm falling into local optima,this paper proposes an operator for the adaptive diversity golden sine search strat-egy.The improved artificial rabbit optimization algorithm is compared with four other algorithms on 8 benchmark functions and 2 engineering application benchmark functions,and the results show that the improved algorithm in this paper achieves significant improvement in computational performance.
artificial rabbits optimizationadaptive diversitygolden sine searchfunction optimization
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