特别是和小生境技术结合求解多峰函数质量明显提高。
Especially it joins niche technology to solve multi model function optimization, the solving quality is improved in evidence.
但在处理高维的具有太多局部最优前沿的多峰函数时极易陷入局部最优前沿。
But as to high-dimension functions with too many local Pareto optimal fronts, it traps in local Pareto optimal front easily.
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.
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.
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.
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.
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.
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.
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.
为了克服粒子群算法在求解多峰函数时极易陷入局部最优解的缺陷，提出一种基于自适应动态邻居广义学习的改进粒子群算法(ADPSO)。
As Particle Swarm Optimization (PSO) may easily get trapped in a local optimum, an improved PSO based on adaptive dynamic neighborhood and comprehensive learning named ADPSO was proposed.
由于其自相关函数的多峰性，将增加这种信号的捕获难度和误跟踪的可能性。
Because of the multimodality of its autocorrelation function, it makes acquisition and tracking more challenging.
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.
在基准函数的测试中，结果显示ADPSO算法比其他PSO算法有更好的运行效果，是求解多峰问题的一种有效算法。
The test results on benchmark functions show that ADPSO achieves better solutions than other improved PSO, and it is an effective algorithm to solve multi-objective problems.
在基准函数的测试中，结果显示ADPSO算法比其他PSO算法有更好的运行效果，是求解多峰问题的一种有效算法。
The test results on benchmark functions show that ADPSO achieves better solutions than other improved PSO, and it is an effective algorithm to solve multi-objective problems.
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