Multi-stage Adaptive BA-ACO Hybrid Swarm Intelligence Algorithm

doi:10.3901/JME.2021.06.236

Abstract

Abstract: A multi-form force bat algorithm(MFBA) is proposed and searching process is divided into two stages based on periodical searching strategy drawing lessons from acting force rules in physics for dealing with the deficiency that interacting information among bats is not made full use of the bat algorithm(BA) in searching process, in addition, Benchmark functions are used to compare the performance of the proposed algorithm with standard BA, variation BA, standard particle swarm optimization(PSO) algorithm and two-stage force PSO(TFPSO) algorithm, the results show that better search ability of optimal solution can be obtained by the proposed algorithm. Aiming at the defection that pheromone updating mechanism is single and is easy to fall into premature convergence in the discrete space optimization of the standard ant colony optimization(ACO) algorithm, so a multi-stage adaptive pheromone ACO(MAPACO) algorithm combining with the actual ant social activities is proposed, when the proposed algorithm appears to be stagnant for a long time, the chaotic operator is introduced to help the algorithm to jump out of the premature convergence, and to give full play to the advantages of ACO algorithm, compared with the hybrid PSO based on cross variation mechanism of differential evolution algorithm, standard ACO and ACO based on dynamic local search, the proposed algorithm is proved to have higher search accuracy and better stability in traveling salesman problem. Aiming at the two methods' advantages that the MFBA algorithm have stronger global searching ability and faster convergence rate, while the MAPACO algorithm can deal with local problems more elaborately, a multi-stage adaptive MFBA-MAPACO hybrid swarm intelligence algorithm is proposed. Finally, the proposed hybrid swarm intelligence algorithm is applied in the reliability optimization of hydraulic system and reliability optimization of series parallel multi-state system, the effectiveness of the proposed MFBA-MAPACO hybrid swarm intelligence algorithm is further verified.

Key words: bat algorithm, ant colony optimization algorithm, hybrid swarm intelligence, reliability optimization

CLC Number:

TB114
TB18

CHEN Dongning, LIU Yidan, YAO Chengyu, YANG Xiaorong. Multi-stage Adaptive BA-ACO Hybrid Swarm Intelligence Algorithm[J]. Journal of Mechanical Engineering, 2021, 57(6): 236-248.

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