Preferred Strategy Based Self-adaptive Ant Lion Optimization Algorithm
LIU Jingsen1,2, HUO Yu3, LI Yu4
1. Institute of Intelligence Networks System, Henan University, Kaifeng 475004; 2. School of Software, Henan University, Kaifeng 475004; 3. School of Computer and Information Engineering, Henan University, Kaifeng 475004; 4. Institute of Management Science and Engineering, Henan University, Kaifeng 475004
Abstract：Ant lion optimization (ALO) algorithm produces low convergence speed and accuracy in high dimensional solution and it is inclined to fall into local extremum. Therefore, a preferred strategy based self-adaptive ant lion optimization algorithm (PSALO) is proposed. The adaptive boundary mechanism is introduced into the process of ant walking around the ant lion to increase the activity of ant population and prevent the algorithm from falling into the local extremum. The optimal roulette strategy is added in the ant lion selection by roulette to maintain the diversity of ant lion individuals and accelerate the convergence speed of the algorithm. The dynamic proportional factor is added into the ant position update formula to improve the exploration ability of the algorithm in the early stage and the development ability in the later stage. Theoretical analysis proves that the time complexity of the proposed algorithm is same as that of ALO. Optimized simulation experiment of 16 standard test functions with different features in multiple dimensions indicates good feasibility of the proposed algorithm. The optimization precision and convergence speed are improved significantly and they are less affected by the dimension variation. The ability in high dimensional solution is better and more stable.
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2020, Vol. 33 
