CAJ | 학술논문

针对混沌系统参数估计的多峰寻优问题，提出一种改进的多种群差分进化算法。改进差分进化算法的变异操作，使其前期更适合全局性搜索，利用α核心集对当前种群进行聚类，分别对聚类后的子群选用贪婪的差分变异算子完成深度搜索，比较所选取各子群的最优值，得到全局最优值作为是否结束搜索的判断依据，并将其应用到混沌系统参数估计中。实验结果表明，该算法对于多峰值、大空间的全局性参数估计在收敛速度、精度上优于混合量子进化算法、改进粒子群优化算法以及DE/best/2算法。
침대혼돈계통삼수고계적다봉심우문제，제출일충개진적다충군차분진화산법。개진차분진화산법적변이조작，사기전기경괄합전국성수색，이용α핵심집대당전충군진행취류，분별대취류후적자군선용탐람적차분변이산자완성심도수색，비교소선취각자군적최우치，득도전국최우치작위시부결속수색적판단의거，병장기응용도혼돈계통삼수고계중。실험결과표명，해산법대우다봉치、대공간적전국성삼수고계재수렴속도、정도상우우혼합양자진화산법、개진입자군우화산법이급DE/best/2산법。
In order to solve the multimodal optimization problem in chaotic systems parameter estimation,an improved multi-swarm Differential Evolution( DE) algorithm is proposed. The mutation operator of DE algorithm is improved, which is more suitable for the global search. By usingαcore set clustering the current swarm,the depth search with greed DE operator is completed on clustered swarms respectively. By comparing the optimal values of selected swarms, the global optimal value is obtained as the judgment of whether to end the search,and is applied to the parameter estimation of chaotic systems. Experimental results show that the proposed algorithm is better than the Hybrid Quantum Evolutionary Algorithm(HQEA),Improved Particle Swarm Optimization(IPSO) and original DE/best/2 algorithm in convergence rate and accuracy for multi peak,large space of global parameter estimation.