SCI论文是基础学科科研成果体现的一种形式，也是理科博士生科研量化指标的一项硬性规定，一般高校理科博士生毕业的条件是一篇SCI加两篇核心期刊，或者两篇SCI。许多博士生延期毕业基本上都是论文不够，所以SCI论文对于大部分理科博士研究生来说，是三年甚至四年中非常头疼的一件事。 那么，怎么写SCI论文，怎样投稿，本人就我近一年内的一些体会写一点感受。我分为基础准备、调研、研究和运算、整理、投稿等五个阶段分别做简要介绍。

一、基础准备：要完成一篇SCI论文，基础准备必不可少。首先，你要有该研究方向必备的理论知识，否则原版的文献你根本看不懂，人家做的报告你根本就听不懂。这方面我准备得比较充分，研一一学年和研二上学期我选修了大量的一些专业基础课程，并对一些自己感兴趣的文献做了相应的了解。对参考书上和文献上相应的公式，我都逐一认真地一一推演。当然，在看文献的过程中你还要去查找相关的文献，因为有些公式是从其它文献中引用而来，如果不查你根本就不可能推得出来。可以说，在研究生阶段，查阅文献和相关信息是一种最基本的能力。

二、调研：导师给了你一个课题后，一般会有一到两篇最核心的参考文献。首先你应该精读它们，如果做的是第一个课题，最好将人家的结果重现一下，用以验证一下你的基本，另外也可以增强你的自信心。当然，在精读这一到两篇文献的同时，你可能也要去查阅文中引用的相关文献，一般到你真正深入调研时，可能你阅读的相关文献已经有二十篇以上了。

在调研的同时，你还要明确你的这个工作到底是解决一个什么问题？

是一个新问题还是老问题，如果是老问题则必须用新方法或新模型，你期待的结果能够得到一个什么样的结论？

总之，你的工作必须是原创的（当然，review除外，但我估计还没有哪一个博士生牛到可以写review）。

三、研究和运算：研究目的明确后，下面就应该是研究的方法或模型了。我们专业主要是先建立物理模型，然后是大型计算，我们一般先建立基本的物理模型，然后用FORTRAN进行程序计算，因为有相当的偏微分方程是没有解析解的，只能通过数值计算获得数值解。所以这一阶段其实是最枯燥的。我们一般用SSH或XWIN32连到系里工作站上，在工作站进行计算，结果如果不满意再调整模型输入参数，我的第三个课题计算过程大约花了半年多的时间。数据出来后，用ORIGIN或IDL画图，再与观测对比，如果符合得很好，即能自圆其说的话，你就快OK了。当然，这一过程极其艰难，中间还要不时与老板讨论，对模型进行一个小的调整或修正。

其它专业我不知道，反正我们专业如果你的计算结果与观测符合得很好，那就说明你的模型至少可以解释这一现象，再牛的referee也不会拒你的稿子。

四、整理：一旦你的结果很好时，老板就会告诉你，把你的工作整理一下吧。成文时最好规范一些，最好不要用WORD，太土了，都是做科研究的人那能用平常人用的软件呢，呵呵。现在科技文献编辑一般都是LaTex软件，这个软件学起来并不难，最大的特点是生成的PDF文件极其漂亮。每一个SCI杂志都有LaTex模板，你只要将相应的地方换掉就

可以了。可能在插图和编辑公式表格时麻烦一点，一般大约两三天即可熟悉它。我们专业文献一般分abstract,introduction, model,results, discussion, summary and conclusion, acknowledge, references, etc.

其中比较难写的是introduction 和discussion, summary and conclusion，中国人一般写这两部分都没有西方人写得那么简明。Introduction中先介绍与你的研究课题相关的其它人的工作，这一部分中一般要引用大量的参考文献。然后简要介绍你的工作要解决的问题及你paper的结构安排。Discussion中主要对你的结果做一些简单的讨论，比如你的结果可能预言一些什么样的现象，你的结果有些什么样的不确定性。summary and conclusion中一般会介绍你的工作的解决了什么样的问题？得到了什么结论？除非你很牛，一般结论不能太肯定，may多用一些会更好。当然，还可以把你的结论与一些大牛对比，如果差不多的话就会更加使得你的paper被accepted 的可能性更大。另外，如果你是第一次用LaTex，插图（请注意只能插ps or eps格式的图）或制表将会花费你大量的时间，不过这不冤枉，等你写第二篇文章时你就会轻车熟路了。

五、投稿：一般第一次投稿最好投母语非英语国家的杂志，我第一paper就投的日本天文学会杂志PASJ (impact factor 2.8)，05年九月底投的，十月底收到审稿意见。改了半个多月后于十一月上旬投出，第二稿审稿意见十二月一日左右收到，有一个小地方要求改动一下。第三稿投出去后于十二月二十日收到接收函。在投递修改稿时一定要注意，

除了LATEX文件和图表等附件外，还要附一封cover letter（说明函）,即对审稿人的意见要进行逐点回答。我的第二篇文章是背着老板做的，投到欧洲的天文学和天体物理学（A&A，impact factor 4.233），第一稿的水平实在不咋地，审稿人认真提出了一些看法（建议大家以后多投欧洲杂志，欧洲人真是很客气）和意见，我改了十天左右就投出去了，并要求编辑进行约一周的快速审稿，审稿人对我进行了批评，认为我这么急于发文章并没有对文章进行认真的修改，我把文章摆了半个月后再花了约半个月认真修改后，审稿人在第三稿审稿意见中很满意，只要我将结论中几句话稍稍修改一下就可以接收了，并表明只要编辑审阅可以了。第二篇文章大约花了四个月，这就是背着导师的代价。第三篇文章是今年3月9日投出的，6月2日接收，这个速度相当快了，投的是我们专业的头号杂志，美国的天体物理学杂志（ApJ, impact factor 6.3）。第四篇文章6月10号投出的，投的英国的皇家天文学会月报（MNRAS, impact factor 5.4）。9月1日接收，前后也是三个月左右。 一篇文章可能被接受的条件：第一：文章的内容要让审稿人surprising一下。第二，你的想法的新颖性也是文章被接收的重要条件，也就是说，你的文章如何从众多的投稿中脱颖而出，这就是一个法宝。也可以说成是想问题的角度不要墨守陈规！

目前我投的四篇文章都没有碰到拒稿的情况，可能我的运气较好。当然，拒稿也是很正常的现象。一般有两种可能：一是你的文章对该杂志并不适合。二是referee认为你的研究没有什么价值，或者没有什么新意。如果是前者，你选择一个合适的杂志重投即可。如果是后者，要么

重换选题，要么你确信你的研究有价值，可以向编辑提出更换referee, 我们系就有这种情况，并且文章后来还较快地被接收了。 至于文章的版面费的问题，大部分国外杂志都有，也有少数没有的，如果老板经费多，还是选本专业顶级期刊发，就不管钱的多少了。比如我发的ApJ，版面费一页120美金，还是挺多的。但是我的第二篇和第四篇文章都不需要版面费。另外，博士生期间也有科研经费申请，江苏省每年六月底就有研究生创新项目申请，时间是六月份，大约150人左右。今年我就申请到了3万元的科研基金，好像学校还有配套的3万元，这样的话经费够你用的了，买书，出差，笔记本电脑，发文章等可能都用不完

第二篇：SCI论文模板

Running title: Li et al. On….

An improved shuffled frog-leaping algorithm for knapsack problem

Authors’ name

Affiliation

Correspondence autuor(通讯作者：): tel/fax XXX; e-mail: XXX

Abstract

Shuffled frog-leaping algorithm (SFLA) has long been considered as new evolutionary algorithm of group evolution, and has a high computing performance and excellent ability for global search. Knapsack problem is a typical NP-complete problem. For the discrete search space, this paper presents the improved SFLA, and solves the knapsack problem by using the algorithm. Experimental results show the feasibility and effectiveness of this method.

Keywords: shuffled frog-leaping algorithm; knapsack problem; optimization problem

0 Introduction

Knapsack problem(KP) is a very typical NP-hard problem in computer science, which was first proposed and studied by Dantzing in the 1950s. There are many algorithms for solving the knapsack problem. Classical algorithms for KP are the branch and bound method (BABM), dynamic programming method （分支界定法和动态规划法）, etc. However, most of such algorithms are over-reliance on the features of problem itself, the computational volume of the algorithm increases by exponentially, and the algorithm needs more searching time with the expansion of the problem. Intelligent optimization problem for solving NP are the ant colony algorithm, greedy algorithm, etc. Such algorithms do not depend on the characteristics of the problem itself, and have the strong global search ability. Related studies have shown that it can effectively improve the ability to search for the optimal solution by combining the intelligent optimization algorithm with the local heuristic searching algorithm.

Shuffled frog-leaping algorithm is a new intelligent optimization algorithm, it combines the advantages of meme algorithm based on genetic evolution and particle swarm algorithm based on group behavior. It has the following characteristics: simple in concept, few parameters, the calculation speed, global optimization ability, easy to implement, etc. and has been effectively used in practical engineering problems, such as resource allocation, job shop process arrangements, traveling salesman problem, 0/1 knapsack problem, etc. However, the basic leapfrog algorithm is easy to blend into local optimum, and thus this paper improved the shuffled frog-leaping algorithm to solve combinatorial optimization problems such as knapsack problem. Experimental results show that the algorithm is

effective in solving such problems.

1 The mathematical model of knapsack problem

Knapsack problem is a NP-complete problem about combinatorial optimization, which is usually divided into 0/1 knapsack problem, complete knapsack problem, multiple knapsack problem, mixed knapsack problem, the latter three kinds can be transformed into the first, therefore, the paper only discussed the 0/1 knapsack problem. The mathematical model of 0/1 knapsack problem can be described as:

n?

?max?xivi?i?0 ?n??xiwi?C(xi?1or0,i?1,2,...,n)??i?0

where: n is the number of objects; wi is the weight of the ith object(I = 1, 2…n); vi is the value of the ith object; xi is the choice status of the ith object; when the ith object is selected into knapsack, defining variable xi = 1,otherwise xi = 0; C is the maximum capacity of knapsack.

2 The basic shuffled frog-leaping algorithm

It generates P frogs randomly, each frog represents a solution of the problem, denoted by Ui, which is seen as the initial population. Calculating the fitness of all the frogs in the population, and arranging the frog according to the descending of fitness. Then dividing the frogs of the entire population into m sub-group of, each sub-group contains n frogs, so P =m*n. Allocation method: in accordance with the principle of equal remainder. That is, by order of the scheduled, the 1, 2, ..., n frogs were assigned to the 1,2, ...., N sub-groups

separately, the n+1 frog was assigned to the first sub-group, and so on, until all the frogs were allocated.

For each sub-group, setting UB is the solution having the best fitness, UW is the solution having the worst fitness, Ug is the solution having the best fitness in the global groups. Then, searching according to the local depth within each sub-group, and updating the local optimal solution, updating strategy is:

??min?int(rand(UB?UW)),Smax?,UB?UW?0S??

??max?int(rand(UB?UW)),?Smax?,UB?UW?0

Uq?Uw?S

where, S is the adjustment vector of individual frog, Smax is the largest step size that is allowed to change by the frog individual. Rand is a random number between 0 and 1.

3 The improved shuffled frog-leaping algorithm for KP

A frog is on behalf of a solution, which is expressed by the choice status vector of object, then frog U = ( x1, x2, …, xn ), where, xi is the choice status of the i-th object; when the i-th object is selected into knapsack, defining variable xi = 1,otherwise xi = 0; f (i), the fitness

function of individual frog can be defined as:

3.1 The local update strategy of frog

The purpose of implementing the local search in the frog sub-group is to search the local optimal solution in different search directions, after searching and iterating a certain number

of iterations, making the local optimum in sub-group gradually tend to the global optimum individual.

Definition 1 Giving a frog’s status vector U, the switching sequence C(i,j) is defined:

where, Ui said the state of object i becomes from the selected to the cancel state, or in turn; Ui= Uj, object i and object j exchange places, that object i and object j are selected or deselected at the same time. Ui≠Uj, object i is selected or canceled, or in turn. Then the new vector of switching operation is:

Definition 2 Selecting any two vectors Ui and Uj of frog from the group, D, the distance from Ui to Uj is all exchange sequences that Ui is adjusted to Uj.

where, m is the number of adjusting.

Based on the above definition, the update strategy of the individual frog is defined as follows:

where, l is the number of switching sequence D(UB,UW) for updating UW; lmax is the maximum number of switching sequence allowed to be selected; s is the switching sequence required for updating UW.

3.2 The global information exchange strategy

During the execution of the basic shuffled frog-leaping algorithm, the operation of updating the feasible solution was is executed repeatedly, it is usually to meet the situation that updating fail, the basic shuffled frog-leaping algorithm updates the feasible solution randomly, but the random method often falls into local optimum or reduces the rate of convergence of the algorithm.

Obviously, the key that overcoming the shortcomings of basic SFLA in evolution is: it is necessary to keep the impact of local and global best information on the frog jump, but also pay attention to the exchange of information between individual frogs. In this paper, first two jumping methods in basic SFLA are improved as follows:

Pn= PX + r1*(Pg－Xp1 (t)) +r2*(PW－Xp2 (t))

Pn= Pb + r3*(Pg－Xp3 (t)) ( 5) ( 6)

Where, Xp1(t),Xp2(t),Xp3(t) are any three different individuals which are different from X. Meanwhile, removing the sorting operation according to the fitness value of frog individual from basic SFLA, and appropriately limiting the third frog jump. Thus, we get an

efficient modified SFLA basing on the improvements of above. In the modified algorithm, the frog individual in the subgroup generates a new individual ( the first jump)by using formula (5),if the new individual is better than its parent entity then replacing the parent individual. otherwise re-generating a new individual (the frog jump again)by using (6).If better than the parent ,then replacing it. or when r4 ≤ FS (the pre-vector, its components are 0.2≤ FSi≤ 0.4),generating a new individual (the third frog jump ) randomly and replacing parent entity.

The new update strategy will enhance the diversity of population and the search through of the worst individual in the iterative process, which can ensure communities’ evolving continually, help improving the convergence speed and avoid falling into local optimum, and then expect algorithm both can converge to the nearby of optimal solution quickly and can approximate accuracy, improved the performance of the shuffled frog-leaping algorithm.

4 Simulation experiment

Two classical 0/1 knapsack problem instances were used in the paper, example 1 was taken from the literature [11], example 2 was taken from the literature [12]. The comparison algorithm used in the paper was branch and bound method for 0/1 knapsack problem. Under the same experimental conditions, two instances of simulation experiments were conducted 20 times, the average statistical results were shown in Table 1 and Table 2.

5 Conclusion

The shuffled frog-leaping algorithm is a kind of search algorithm with random intelligence and global search capability, this paper improved shuffled frog-leaping algorithm and solved the 0/1 knapsack problem by using the algorithm. Experiments show that the improved algorithm has better feasibility and effectiveness in solving 0/1 knapsack problem. Acknowledgements

This work was supported by XXX(基金号). Our special thanks are due to Prof. XXX (name), XXX (affiliation), for his helpful discussion with preparing the manuscript.

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