In this paper, the conventional lion group algorithm is improved and used to solve Dynamic Multi-objective Optimization Problems (DMOPs). An environment change detection strategy is adopted to determine whether the en...
详细信息
ISBN:
(纸本)9798350385113;9798350385106
In this paper, the conventional lion group algorithm is improved and used to solve Dynamic Multi-objective Optimization Problems (DMOPs). An environment change detection strategy is adopted to determine whether the environment changes by calculating the average difference in fitness values of the hunting lions. Furthermore, the algorithm constructs a time series by recording the positions of the lion king at times t 1 and t to predict the lion king's position at time t + 1 to adapt to the changing environment. The performance of the proposed algorithm is studied by comparing it with four state-ofthe-art dynamic optimization algorithms. The experimental results show that the proposed algorithm outperforms other peer algorithms in most benchmark functions.
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