混合蛙跳算法王红亚

① 蛙跳算法的原理

蛙跳算法的思想是：在一片湿地中生活着一群青蛙。湿地内离散的分布着许多石头，青蛙通过寻找不同的石头进行跳跃去找到食物较多的地方。每只青蛙个体之间通过文化的交流实现信息的交换。每只青蛙都具有自己的文化。每只青蛙的文化被定义为问题的一个解。湿地的整个青蛙群体被分为不同的子群体，每个子群体有着自己的文化，执行局部搜索策略。在子群体中的每个个体有着自己的文化，并且影响着其他个体，也受其他个体的影响，并随着子群体的进化而进化。当子群体进化到一定阶段以后，各个子群体之间再进行思想的交流（全局信息交换）实现子群体间的混合运算，一直到所设置的条件满足为止。

② 蛙跳算法的过程

全局搜索过程
步骤l 初始化。确定蛙群的数量、种群以及每个种群的青蛙数。
步骤2 随机产生初始蛙群，计算各个蛙的适应值。
步骤3 按适应值大小进行降序排序并记录最好解Px，并且将蛙群分成族群。把F个蛙分配到m个族群Y，Y，Y…,Y中去，每个族群包含n个蛙，从而使得Yk=[X(j),f(j)|X(j)=X(k+m*(j-1), f(j)=f(k+m*(j-1),j=1,…,n,k=1,…,m].这里X（j）表示蛙群中的第j蛙，f(j)表示第j个蛙的目标函数值。
步骤4根据SFLA算法公式，在每个族群中进行元进化。
步骤5将各个族群进行混合。在每个族群都进行过一轮元进化之后，将各个族群中的蛙重新进行排序和族群划分并记录全局最好解Px。
步骤6检验计算停止条件。如果满足了算法收敛条件，则停止算法执行过程，否则转到步骤3。通常而言，如果算法在连续几个全局思想交流以后，最好解没有得到明显改进则停止算法。某些情况下，最大函数评价次数也可以作为算法的停止准则。
局部搜索过程
局部搜索过程是对上述步骤4的进一步展开，具体过程
如下：
步骤4—1设im=O，这里im是族群的计数器。用来与族群总数m进行比较。设iN=0，这里iN是局部进化的计数器，用来与Ls进行比较。
步骤4-2根据式(1)在第l，，1个族群中选择q个蛙进入子族群，确定Pb和Pw并设im=im+1。
步骤4-3设iN=iN+1。
步骤4—4根据式(2)和式(3)改进子族群中的最差蛙的位置。
步骤4—5如果步骤4—4改进了最差蛙的位置(解)，就用新产生的位置取代最差蛙的位置。否则就采用Px代替式(2)中的PB，重新更新最差蛙的位置。
步骤4—6如果步骤4-5没有改进最差蛙的位置，则随机产生一个处于湿地中任何位置的蛙来替代最差的蛙。
不管执行了以上三次跳跃中的任何一次，需重新计算本子群的最优个体Pb和最差个体Pw。
步骤4—7如果iN<LS，则转到步骤4-3。
步骤4—8如果im<m，则转到步骤4-2，否则转到全局搜索过程的步骤5。
算法停止条件
SFLA通常采用两种策略来控制算法的执行时间：
1)在最近的K次全局思想交流过程之后，全局最好解没有得到明显的改进；
2)算法预先定义的函数评价次数已经达到。
3)已有标准测试结果。
无论哪个停止条件得到满足，算法都要被强制退出整个循环搜索过程。

③ 什么是蛙跳算法

蛙跳算法（SFLA）是一种全新的后启发式群体进化算法，具有高效的计算性能和优良的全局搜索能力。对混合蛙跳算法的基本原理进行了阐述，针对算法局部更新策略引起的更新操作前后个体空间位置变化较大，降低收敛速度这一问题，提出了一种基于阈值选择策略的改进蛙跳算法。通过不满足阈值条件的个体分量不予更新的策略，减小了个体空间差异，从而改善了算法的性能。数值实验证明了该改进算法的有效性，并对改进算法的阈值参数进行了率定

④ 最常见的人工智能算法都有哪些

神经网络算法、蚁群算法、混合蛙跳算法、蜂群算法。

⑤ 仿生智能优化算法(如何用英语翻译下）谢谢

Biological modelling intelligence optimization algorithm <dnt> </dnt> each indivial has the experience and the wisdom intelligent body in the biological modelling intelligence optimization algorithm, between the indivial has the interaction mechanism, forms the formidable community wisdom through the interaction to solve the complex problem. The biological modelling intelligence optimization optimization algorithm is one kind of probability searching algorithm essentially, it does not need the question the gradient information, has following is different with the traditional optimization algorithm characteristic:①In the community interaction's indivial is distributional, does not have the direct central main body, not because the indivial indivial will present the breakdown to affect the community to the question solution, will have the strong robustness;②Each indivial can only the sensation partial information, indivial ability or follows the rule to be simple, therefore the community intelligence realizes is simple, is convenient;③The system uses in the expenses which corresponds being few, easy to expand;④From the organization sense, namely the community displays the complex behavior is displays the high intelligence alternately through the simple indivial. Biological modelling intelligence optimization algorithm's these characteristics to overcome difficulties which the optimization design domain faced to provide the powerful support.
<dnt> </dnt>Second, a biological modelling intelligence optimization algorithm common ground analyzes [6] <dnt> the </dnt> several kind of biological modelling intelligence optimization algorithm is simulates the nature living system, total dependence organism own instinct, to optimize its survival condition through unconsciousness optimization behavior to adapt an environment kind of new optimized method, thus has many similar characteristics in the structure and the behavior: 1) is a kind of indefinite algorithm, this kind of uncertainty has manifested the nature physiological mechanism, is follows its randomness to come, when solves certain specific questions must surpass the definite method; 2) is a kind of probability algorithm, its main step includes the random factors, can have more opportunities to gain the globally optimal solution; 3) does not rely on the optimal process the optimized question's strict mathematics nature as well as the objective function and the constraints precise mathematics description; 4) is one kind based on community's intelligent optimization algorithm; 5) has the concealment parallelism, can obtain the great income by the few computations; 6) has appears suddenly the nature, its general objective's completion is in the indivial evolution process appears suddenly in the community; 7) has the evolution, its indivial in complex, stochastic, time-variable environment, through enhances its compatibility unceasingly from the study; 8) has robustness, under the different condition and the environment, manifests the formidable compatibility and the validity. Certainly, because in the nature living system's multiplicity and the complexity, these algorithms also displayed the huge difference. But difference existence, also happen to discuss these biological modelling intelligence optimization algorithm the essential attribute, then obtains the biological modelling intelligence algorithm the unified frame pattern, designed a performance better algorithm to provide the rich material.Second, two biological modelling intelligence optimization algorithm unification frame pattern [7] <dnt> the </dnt> biological modelling intelligence optimization algorithm in aspects and so on structure, research content and method and movement pattern manifested the big similarity, has provided the possibility for the establishment biological modelling intelligence optimization algorithm's unified frame pattern.
<dnt> </dnt> forms the community of the indivial, rests on the specific evolution rule, the iteration proces the renewal community (for example genetic algorithm, ant group algorithm) or the indivial position (for example grain of subgroup algorithm, artificial school of fish algorithm, mix leapfrog algorithm), the optimal solution evolves unceasingly along with the community or the migration appears suddenly, this frame pattern may describe is:
<dnt> </dnt>1) establishes various parameters, proces the initial community and calculates the adaptation value;
<dnt> </dnt>2) acts according to the hypothesis rule, the renewal community or its position, has group of solutions, the computation indivial adaptation value;
<dnt> </dnt>3) obtains the community by the indivial adaptation value comparison the optimal-adaptive value and makes the record;
<dnt> </dnt>4) judges the terminal condition whether to satisfy, if satisfies, conclusion iteration; Otherwise, transfers 2).
<dnt> </dnt> in this frame pattern, the one who decides the algorithm performance is community's renewal rule, these hypothesis rule had decided the indivial behavior standards, have the direct biology foundation, constituted the algorithm to be different with other similar unique essences and the bright characteristic.
<dnt> the </dnt> biological modelling intelligence optimization algorithm sets up together the call-board generally, with records the most superior indivial the historical condition. In algorithm execution each iteration, each indivial comparison own condition and call-board condition, and when own condition is superior with it replacement, causes the call-board to record the historical most superior condition throughout. After algorithm iteration conclusion, may read out the optimal solution from the call-board condition and gain the related information

⑥ 邹采荣的学术成果

一、发表论文(代表作10篇,部分检索结果：1997开始至今被收录SCI 33篇、EI 96篇、CPCI 29篇)：
1.Zou-CR, Plotkin-EI, Swamy-MNS, 2-D Fast Kalman Algorithms for Adaptive Parameter-Estimation of Nonhomogeneous Gaussian Markov Random-Field Model,IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-ANALOG AND DIGITAL SIGNAL PROCESSING , Vol.41, Iss. 10,pp 678-692,1994;
2. Zou-CR, Plotkin-EI, Swamy-MNS, He-ZY. Recursive-in- Order Least-Squares Parameter-Estimation Algorithm for 2-D Noncausal Gaussian Markov Random-Field Model, CIRCUITS SYSTEMS AND SIGNAL PROCESSING Vol.14 Iss.1,pp 87-110,1995;
3.Luo LJ,Lu Y,Zou CR, Image Sequence Macroblock Classification Using Neural Networks, SIGNAL PROCESSING,Vol.69, Iss. 2,pp.191-198,1998;
4.Wang ZH, He ZY, Zou CR, A Generalized Fast Algorithm for N-d Discrete Cosine Transform and Its Application to Motion Picture Coding, IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-ANALOG AND DIGITAL SIGNAL PROCESSING Vol.46, Iss.5, pp.617-627 ,1999;
5. Gao-XQ, Duanmu-CJ, Zou-CR, A Multilevel Successive Elimination Algorithm for Block Matching Motion Estimation, IEEE TRANSACTIONS ON IMAGE PROCESSING,Vol.9, Iss.3, pp.501-504, 2000;
6.Zheng WM, Zhou XY, Zou CR, Facial expression recognition using kernel canonical correlation analysis (KCCA),IEEE TRANSACTIONS ON NEURAL NETWORKS ,Vol.17,pp.233-238,2005;
7.He, Yunhui, Zhao, Li, Zou, Cairong, Face recognition using common faces method,PATTERN RECOGNITION, Vol.39, Iss.11, pp.2218-2222, 2006;
8.Wei Xin, Zhao Li, Zou Cairong,Blind Multiple Access Interference Suppression Algorithm Based on Relaxed Subgradient Projection for DS/CDMA Systems, CIRCUITS SYSTEMS AND SIGNAL PROCESSING Vol.29, Iss.4 pp.769-780,2010;
9.Sun Ning, Ji Zhen-hai, Zou Cai-rong , Two-dimensional Canonical Correlation Analysis and Its Application in Small Sample Size Face Recognition, NEURAL COMPUTING & APPLICATIONS,Vol.19,Iss.3,pp.377-382,2010;
10.Cairong Zou, Chengwei Huang, Dong Han, Li Zhao. Detecting Practical Speech Emotion in a Cognitive Task, Computer Communications and Networks (ICCCN), 2011 Proceedings of 20th International Conference on, Maui, HI, USA, 2011。
二、授权发明专利：
1.一种基于二维偏最小二乘法的面部表情识别方法;
2.一种基于加权主动形状建模的人脸特征定位方法;
3.一种人脸身份和表情的同步识别方法;
4.一种雷达脉内调制信号的特征提取方法;
5.一种基于改进Fukunage-koontz变换的语音情感识别系统;
6.浅海水声通信系统的间接自适应均衡方法;
7.水声网络中的节能的媒质访问控制方法;
8.基于JND和AR模型的感知视频压缩方法;
9.基于改进的VLS的立体视频编码方法;
10.一种基于支持矢量基的语音情感识别方法;
11.一种适用于中国数字电视地面广播国家标准的同步方法;
12.基于分数傅里叶变换的二维维纳滤波的取证语音增强方法;
13.一种加权次梯度投影的数字助听器回声路径估计方法;
14.基于改进BP算法的中间视合成方法;
15.一种基于CDMA水声网络的媒质访问控制方法。
三、已授权实用新型专利3项：
1.超高精度压力计量校准仪;
2.新型滴眼装置;
3.带有信息检索的智能电视终端。
四、已授权外观设计专利1项：
1.活页式电子乐谱。
五、已登记软件着作权1项：
1.MusicPro电子乐谱系统软件V1.0(登记号：2008SR38814)。
六、正在申请并受理发明专利15项：
1.基于乐符知识及双投影法的乐符基元分割方法;
2.一种基于心电信号与语音信号的双模态情感识别方法;
3.一种针对烦躁情绪的可据判的自动语音情感识别方法;
4.基于情感对特征优化的语音情感分类方法;
5.一种分数傅里叶变换上的时频域掩蔽信息隐藏方法;
6.一种基于分数傅里叶变换域的隐秘信号同步方法;
7.基于分段投影与乐符结构的谱线检测及删除方法;
8.基于行游程邻接表的乐谱快速连通域分析方法;
9.一种基于多变量统计的助听器声源定位方法;
10.一种基于压缩传感的助听器声源定位方法;
11.一种认知无线电功率控制方法;
12.一种基于云理论与分子动力学模拟的混合蛙跳算法;
13.基于特征空间自适应投影的语音情感识别方法;
14.一种跨语言的语音情感识别方法;
15.负面情绪检测中的基于上下文修正的语音情感识别方法。
七、科技获奖：
1.“基于面部表情和情感语音的儿童情绪能力分析与分类的研究”获2009年度江苏省科学技术进步二等奖.排名 第一;
2.“情感特征分析与识别的理论与应用”获2008年教育部自然科学二等奖 排名 第一;
3.“多维数字信号处理的理论与应用研究” 获1998年国家教育部科技进步(基础类)二等奖 排名第三;
4.“盲信号模型参数估计的方法研究”获2000年中国高校科学技术奖励委员会二等奖 排名第四;
5.“小波与滤波器组的理论及其应用研究”获2006年教育部自然科学二等奖 排名 第三;
6.“神经网络理论及其智能信息处理应用基础”获1998年国家教育部科技进步(基础类)一等奖 排名第二十二。
八、参编着作：
1.《多维数字信号处理》，何振亚主编，国防工业出版社 1995。获江苏省优秀教材一等奖、教育部2001年优秀教材一等奖。

⑦ 仿生算法有那些

仿生算法———遗传算法、蚁群算法和混合蛙跳算法