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首页> 外文期刊>International journal of computing science and mathematics >Chaotic particle swarm optimisation for fitting magnetic spin parameters of transition metal complexes
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Chaotic particle swarm optimisation for fitting magnetic spin parameters of transition metal complexes

机译:混沌粒子群算法优化过渡金属配合物的磁自旋参数

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摘要

Magnetic properties analysis of transition metal complexes is a challenging problem due to the high non-linearity of variable temperature magnetic susceptibility. This paper proposes a chaotic particle swarm optimisation (CPSO) to fit the optimal magnetic spin parameters. CPSO adopts adaptive chaotic search strategy guided by mean swarm velocity to maintain the swarm diversity and prompt the search efficiency of particles. Moreover, four different chaotic maps are separately introduced into CPSO, and the four kinds of CPSO are analysed to evaluate each chaotic distribution how to influence global convergence of the algorithm. The relative analysis and the simulation results on a magnetic spin of nickel atoms using various mixed anionic ligands verify the chaotic technique can improve the optimal capability of PSO validly, especially Bernouilli shift chaotic map, which can contribute to the swarm diversity of PSO greatly and get the best global search performance in four kinds of CPSO.
机译:由于可变温度磁化率的高度非线性,过渡金属络合物的磁性能分析是一个具有挑战性的问题。本文提出了一种混沌粒子群优化算法(CPSO)来拟合最优的磁自旋参数。 CPSO采用以平均群速度为指导的自适应混沌搜索策略,以保持群的多样性并提高粒子的搜索效率。此外,分别将四种不同的混沌映射图引入CPSO,并对四种CPSO进行了分析,以评估每种混沌分布如何影响算法的全局收敛性。各种混合阴离子配体对镍原子磁自旋的相关分析和仿真结果证明,该混沌技术可以有效地提高PSO的最佳性能,尤其是Bernouilli位移混沌图谱,可以极大地促进PSO的群多样性并得到四种CPSO中最佳的全局搜索性能。

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