The HPSO combines the particle swarm optimization with constraint optimization and direct search.
本文提出了一种新的求解约束优化问题的微粒群算法。
The HPSO employs local version constriction factor method and global version inertia weight method simultaneously to achieve relatively high performance.
HPSO同时采用局部模式的压缩因子方法和全局模式的惯性权重方法以获得相对较高的性能。
A hybrid particle swarm optimization (HPSO) approach is presented to solve the problem of optimizing the steady-state temperatures of the three zones of a billet reheating furnace.
针对一种蓄热推钢式加热炉三个加热区的炉温稳态优化问题，本文提出了一种混合粒子群优化(HPSO)方法。
A hybrid particle swarm optimization (HPSO) approach is presented to solve the problem of optimizing the steady-state temperatures of the three zones of a billet reheating furnace.
针对一种蓄热推钢式加热炉三个加热区的炉温稳态优化问题，本文提出了一种混合粒子群优化(HPSO)方法。
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