Designing Neural Networks Using Hybrid Particle Swarm Optimization
Designing Neural Networks Using Hybrid Particle Swarm Optimization
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DOI：
10.1007/11427391_62
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发表时间：
2005-05
期刊：
影响因子：
3.9
通讯作者：
Bo Liu;Ling Wang;Yihui Jin;Dexian Huang
中科院分区：
文献类型：
--
作者：
Bo Liu;Ling Wang;Yihui Jin;Dexian Huang
进化人工神经网络是进化计算和神经网络领域的一个重要研究课题。将差分进化和混沌引入经典粒子群优化算法，提出了一种混合粒子群优化算法。通过将DE操作与PSO算法相结合，可以很好地平衡种群的探索和开发能力，并合理地保持种群的多样性。同时，通过混沌局部搜索、DE算子和PSO算子的混合，丰富了搜索行为，增强了避免陷入局部最优的能力。然后，提出的混合粒子群算法（CPSODE）被应用于设计多层前馈神经网络。仿真结果和比较表明，所提出的混合粒子群算法的有效性和效率。
Evolving artificial neural network is an important issue in both evolutionary computation (EC) and neural networks (NN) fields. In this paper, a hybrid particle swarm optimization (PSO) is proposed by incorporating differential evolution (DE) and chaos into the classic PSO. By combining DE operation with PSO, the exploration and exploitation abilities can be well balanced, and the diversity of swarms can be reasonably maintained. Moreover, by hybridizing chaotic local search (CLS), DE operator and PSO operator, searching behavior can be enriched and the ability to avoid being trapped in local optima can be well enhanced. Then, the proposed hybrid PSO (named CPSODE) is applied to design multi-layer feed-forward neural network. Simulation results and comparisons demonstrate the effectiveness and efficiency of the proposed hybrid PSO.