Abstract
Aiming at the shortcoming that the basic beetle antennae search algorithm fails to consider differences between individuals and the dynamic information in the searching process, this paper proposed a new beetle antennae search algorithm based on the elite selection mechanism and the neighbor mobility strategy. The elite selection mechanism will be used to weaken beetles having bad performances and generate new beetles to ensure diversities and abilities in the whole population. The neighbor mobility strategy will guide the algorithm to open up a wider searching area to ensure that individuals having good positions own a chance to infect individuals with poor performances. To verify the searching ability and the optimization speed of the proposed algorithm in this paper, different testing functions were selected for numerical testing experiments, and the iteration figures, box plots, and searching path figures were given. The experimental results showed that the proposed algorithm in this paper was superior to the original algorithm in the solving accuracy, the convergence speed, and the stability.
Highlights
In practical engineering fields, many optimization problems need to be solved under complex constraints and in a large searching range
Because the basic Beetle Antennae Search Algorithm (BAS) does not consider the individual difference and the dynamic information in the searching process, this paper proposed a new algorithm based on the elite selection mechanism and the neighbor mobility strategy
If the fitness value of the individual is lower than the threshold, the individual position will be replaced to dynamically increase the population diversity, and select elite individuals having good convergence and robustness in the whole population to guide other individuals to explore better positions
Summary
Many optimization problems need to be solved under complex constraints and in a large searching range. Traditional mathematical methods such as the steepest descent method and the variable scale method can only calculate simple and continuous functions [1,2,3]. Aghila Rajagopal et al proposed a new hybrid extreme learning machine with beetle antennae search algorithm, and used BAS to evaluate the optimal strategy in the low earth orbit satellite communication networks [32]. The parameters selection process must consider working environments, errors, interferences, and other factors, so the optimal initialization parameters of the algorithm can not be obtained by artificial experiences. This paper proposed an improved algorithm based on the elite search strategy and the nearest neighbor selection mechanism (ENBAS). Eight benchmark functions are used for function experiments, and this paper compared the proposed algorithm with other optimization algorithms to verify the ENBAS performance
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