融合多策略改进的自适应狮群优化算法

Improved Adaptive Lion Swarm Optimization Algorithm Based on Multi-Strategy

针对狮群优化算法种群差异性低、收敛速度慢、易陷入局部极值的问题,提出融合多策略改进的自适应狮群算法。引入自适应参数改进Tent混沌映射用于种群初始化,保证随机分布的同时提高多样性和均匀遍历性;基于差分进化机制引入母狮位置更新扰动因子,增强算法跳出局部最优的能力;融合二阶范数与信息熵形成步长扰动因子,通过自适应参数动态调整幼狮不同行为方式的选择概率,从而抑制算法早熟收敛;基于自适应Tent混沌搜索策略,通过局部最优解的多个邻域点改善适应度较差的个体,进一步提升算法的寻优速度和精度。在16个多类型的标准测试函数上,与多种智能算法的对比验证了所提算法良好的优化性能。为进一步评估所提算法的有效性,将其用于反向传播神经网络初始权重和阈值的优化,2个标准数据集上的实验结果表明,相比于其他算法,所提算法具有更高的分类精度。

To solve the problems of low diversity,slow convergence speed and easy to fall into local extremum of the lion swarm optimization algorithm,an improved adaptive algorithm based on multi-strategy is proposed.Specifically,the adaptive parameters are introduced to improve Tent chaotic map for population initialization,which ensures random distribution and improves diversity and uniform ergodicity.Then,based on differential evolution mechanism,the disturbance factor of lioness position update is introduced to enhance the ability of the algorithm to jump out of the local optimum.Finally,the second order norm and information entropy are combined to form a step size disturbance factor,which adaptively adjusts the selection probability of different behavior modes of the cub,therefore inhibits the premature convergence of the algorithm.Based on adaptive Tent chaotic search,individuals with poor fitness are improved through multiple neighborhood points of local optimal solution to further enhance the optimization speed and accuracy.Comparing with various intelligence algorithms,the better performance of the proposed algorithm is verified through 16 multi-type test functions.To further evaluate the effectiveness of the proposed algorithm,it is used to optimize the initial weights and thresholds of back propagation neural networks.Experimental results on the two datasets show the proposed algorithm has higher classification accuracy compared with the other three algorithm.