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首页> 外文期刊>Neural Networks, IEEE Transactions on >Self-Organizing Potential Field Network: A New Optimization Algorithm
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Self-Organizing Potential Field Network: A New Optimization Algorithm

机译：自组织势场网络：一种新的优化算法

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摘要

This paper presents a novel optimization algorithm called self-organizing potential field network (SOPFN). The SOPFN algorithm is derived from the idea of the vector potential field. In the proposed network, the neuron with the best weight is considered as the target with the attractive force, while the neuron with the worst weight is considered as the obstacle with the repulsive force. The competitive and cooperative behaviors of SOPFN provide a remarkable ability to escape from the local optimum. Simulations were performed, compared, and analyzed on eight benchmark functions. The results presented illustrate that the SOPFN algorithm achieves a significant performance improvement on multimodal problems compared with other evolutionary optimization algorithms.
机译：本文提出了一种称为自组织势场网络（SOPFN）的新型优化算法。 SOPFN算法源自矢量势场的思想。在提出的网络中，权重最大的神经元被视为具有吸引力的目标，而权重最差的神经元被视为具有排斥力的障碍。 SOPFN的竞争与合作行为提供了显着的能力来摆脱局部最优。对八个基准功能进行了仿真，比较和分析。提出的结果表明，与其他进化优化算法相比，SOPFN算法在多峰问题上实现了显着的性能提升。

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