文件名称:code
- 所属分类：
- matlab例程
- 资源属性：
- [Matlab] [源码]
- 上传时间：
- 2013-06-21
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- 2kb
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- sina v*******
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Convergence analysis and performance of the extended artificial
physics optimization algorithm.artificial physics optimization (EAPO), a population-based,
stochastic, evolutionary algorithm (EA) for multidimensional search and optimization.
EAPO extends the physicomimetics-based Artificial Physics Optimization (APO) algorithm
by including each individual’s best fitness history. Including the history improves EAPO’s
search capability compared to APO. EAPO and APO invoke a gravitational metaphor in
which the force of gravity may be attractive or repulsive, the aggregate effect of which is
to move individuals toward local and global optima. A proof of convergence is presented
that reveals the conditions under which EAPO is guaranteed to converge
physics optimization algorithm.artificial physics optimization (EAPO), a population-based,
stochastic, evolutionary algorithm (EA) for multidimensional search and optimization.
EAPO extends the physicomimetics-based Artificial Physics Optimization (APO) algorithm
by including each individual’s best fitness history. Including the history improves EAPO’s
search capability compared to APO. EAPO and APO invoke a gravitational metaphor in
which the force of gravity may be attractive or repulsive, the aggregate effect of which is
to move individuals toward local and global optima. A proof of convergence is presented
that reveals the conditions under which EAPO is guaranteed to converge
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下载文件列表
code
....\APO.m
....\my_cost_function.m