求解带约束的多峰函数优化问题在科学研究和工程应用中具有重要的现实意义。
Solving multi -modal function optimization problems with constraints has an important practical meaning in scientific re-searches and engineering applications.
该算法比上述两种算法具有更好的性能，特别是对多峰函数优化等问题计算效果更好。
It is proved that this proposed algorithm outperforms the two algorithms proviously referenced and has better results for multi-modal functions in particular.
针对多峰函数优化问题，借鉴粒子群优化特性和免疫网络理论，提出一种免疫粒子群网络算法。
Referred to the character of particle swarm optimization and immune network theory, an immune particle swarm network algorithm for multimodal function optimization is proposed.
以局部演化的方式，实现了种群间分离与种群内自聚集，使多峰函数优化问题转化为单峰函数优化问题。
The separation among populations and the adaptive-gathering in a population are achieved by local evolution, so the multi-model function optimization is transformed to unimodal function optimization.
最后，对典型的多峰函数优化试验表明：作者开发的粒子群优化算法结构简单，运行快，是一个通用有效的优化工具。
Finally, the experimental results show that this algorithm which developed by us is an universal and effective optimization tool with simple structure and little running time.
针对边界约束函数全局最优化和多峰寻优问题，提出一种直接搜索算法。
This paper proposes a direct search algorithm for global optimization and multi-peak searching of functions with boundary constraints.
为了测试该算法的性能，选择了几个目标函数进行优化，包括单峰函数和多峰函数。
In order to test the performance of the algorithm, several object functions are optimized including single-modal and multi-modal function.
仿真实验的结果也表明该算法在平均运行时间减少了56%的情况下多峰函数的优化效果得到了显著改善。
The simulative experimental results show that it has obvious improvement in multimodal function optimization problems with the case of the average run time reduced to 56% of the former.
基于混沌序列的多峰函数微粒群寻优算法的目标就是找到多峰函数的所有局部优化峰值。
It makes a searching for all local optimization of the multimodal function that a PSO algorithm based on chaos sequence for multi-modal function optimization.
基于混沌序列的多峰函数微粒群寻优算法的目标就是找到多峰函数的所有局部优化峰值。
It makes a searching for all local optimization of the multimodal function that a PSO algorithm based on chaos sequence for multi-modal function optimization.
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