To enhance optimization capabilities of beetle antennae search algorithm(BAS),self-adaptive and parallel beetle antennae optimization algorithm(APBAO)was proposed.APBAO evolved from a single iterative individual in BAS to multiple parallel iterative individuals which expanded the search scope of solution space.Elite system was employed to make algorithm self-adaptive.To verify the performance of the algorithm,test was conducted using multiple standard benchmark functions,compared APBAO with BAS,particle swarm optimization algorithm(PSO)and ant colony optimization algorithm(ACO).The experimental results showed that APBAO's optimization rate for the objective function was increased by 97.39%compared to BAS,and by 84.46%and 86.98%compared to the PSO and ACO,respectively.The proposed improvements effectively enhanced algorithm performance,and helped the algorithm escape from local optimal.
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