Memory Tunicate Swarm Algorithm with Information Sharing
QU Chiwen1,2, PENG Xiaoning1,3
1. School of Mathematics and Statistics, Hunan Normal University, Changsha 410081; 2. Key Laboratory of Computing and Stochastic Mathematics, Ministry of Education, Hunan Normal University, Changsha 410081; 3. School of Medicine, Hunan Normal University, Changsha 410081
Abstract Aiming at the problems of low accuracy, slow convergence speed and easily falling into local optimum of the tunicate swarm algorithm(TSA), a memory tunicate swarm algorithm with information sharing is proposed. Firstly, a dynamic self-adaptive adjustment strategy is adopted to divide the population into two sub-groups dynamically, including information sharing search and jet propulsion search, to balance the global development capability and local development capability of TSA. Then, some tunicate individuals are selected randomly to acquire information from the peers to realize the sufficient information exchange and sharing among tunicate individuals in the information sharing search mode. For another group of individuals, historical optimal locations are introduced to guide learning and thus the effectiveness of the algorithm search is enhanced. Experimental results on 20 benchmark functions show that the proposed algorithm is evidently superior in convergence rate, solution accuracy and robustness.
Fund:National Natural Science Foundation of China(No.81472860), Key Research and Development Project of Hunan Province(No. 2020DK2002)
Corresponding Authors:
PENG Xiaoning, Ph.D., professor. His research interests include development and application of genetic algorithm and data mining of tumor genome.
About author:: QU Chiwen, Ph.D. candidate, associate professor. His research interests include intelligent computing and statistics.
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2021, Vol. 34 
