Abstract: This paper proposes an adaptive particle swarm optimization algorithm and two benchmarks are used to test it. The results show that the algorithm can keep good balance between the exploration and the exploitation. When solving the problem of multi-modal function optimization, the algorithm has better capability of jumping out of local optimum than basic particle swarm optimization algorithm, and the executing efficiency does not reduce distinctly.

Key words: swarm intelligence, particle swarm optimization, population entropy, cellular

摘要： 提出了一种基于种群熵的自适应粒子群算法，采用2个基准函数对新算法进行了测试。测试结果表明，新算法有效地均衡了算法的探测和开采能力，在解决复杂多峰函数优化问题时，与基本粒子群算法相比，具有更强的摆脱局部极值点的能力，且执行效率降低不多。

关键词: 群智能, 粒子群优化, 种群熵, 元胞

CLC Number: 

• TP183