An Improved Beetle Antennae Search Algorithm Based on the Elite Selection Mechanism and the Neighbor Mobility Strategy for Global Optimization Problems

Abstract

Aiming at the shortcoming that the basic beetle antennae search algorithm fails to consider the differences between individuals and dynamic information in the searching process, this paper proposed a new search algorithm based on the elite selection mechanism and the neighbor mobility strategy. The elitist selection mechanism is used to weaken beetles having bad performance and generate new beetles to ensure diversity and adaptability 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 owns 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 to conduct a comprehensive statistical test and analysis. The experimental results show that the proposed algorithm in this paper is superior to the original algorithm and other comparative algorithms in solving accuracy, convergence speed, and stability.INDEX TERMS Beetle antennae search algorithm, numerical problems, global optimization.

A comprehensive survey of convergence analysis of beetle antennae search algorithm and its applications

“…Then, the position is updated based on the evaluation of the objective function. eBAS was compared with nine other metaheuristic algorithms, including BA, Biogeography-Based Optimization (BBO) (Qian et al 2022), ABC, PSO, Cuckoo Search (CS) (Shao and Fan 2021), Harmony Search (HS) (Wu et al 2020), (Mirjalili et al 2014), Teaching-Learning-Based Optimization (TLBO) (Neshat et al 2014), and FA. eBAS demonstrated exceptional performance in multiple optimization examples with continuous and/or discrete variables, especially in solving practical-sized problems with numerous design variables and complex constraints.…”

“…Rigorous evaluations through standard function tests revealed that BAS-ABC achieves a more rapid convergence and superior accuracy in comparison to the conventional ABC and PSO, albeit with a sensitivity to parameter configurations. Furthering this domain, Zhang et al introduced an iteration of the BAS-ABC algorithm (Shao and Fan 2021),, which, upon deriving optimal adaptive values and positions via BAS, employs these positions as food source coordinates within the ABC framework. The algorithm executes BAS iterations commensurate with the number of food sources identified in ABC.…”