電場 大學

Artificial Electric Field Algorithm in short ‘AEFA’ is an artificially intelligent optimization algorithm for mathematical optimization, it is inspired by the Coulomb’s law of electrostatic force. AEFA is also a population-based optimization algorithm. Like other population-based search techniques, it also generates a random population as an initial solution. Then the generated population moves in the influence of the governing forces calculated from Coulomb’s law of electrostatic forces. To understand this algorithm, suppose, we wish to solve:

人工電場算法的簡稱“ AEFA”是一種用于數學優化的人工智能優化算法，它受到庫侖 靜電力 定律的啟發。 AEFA還是基于人口的優化算法。 像其他基于人口的搜索技術一樣，它還會生成一個隨機人口作為初始解決方案。 然后，生成的種群在根據庫侖靜電力定律計算出的控制力的影響下移動。 假設要理解此算法，我們希望解決：

Where each variable has some lower and upper bound. (It may be extended to constrained optimization problems as well).

每個變量都有上限和下限。 (它也可以擴展到受約束的優化問題)。

The following mechanism is used to generate the initial population is used:

以下機制用于生成使用的初始種群：

Each generated solution is called here as a charged particle. Now the idea of AEFA is created from the following laws:

每個生成的溶液在此稱為帶電粒子。 現在，AEFA的概念是根據以下法律創建的：

This calculates the force of attraction or repulsion between two charged particles. Here K is the artificial designed Quolomb’s constant which is a function of the iter (current iteration)and the max iterations (maxiter)

這將計算兩個帶電粒子之間的吸引或排斥力。 這里K是人為設計的Quolomb常數，它是iter(當前迭代)和max迭代(maxiter)的函數

The generated electric field around any charge Q is calculated as

圍繞電荷Q生成的電場計算為

Finally, the acceleration can be calculated using Newton’s law of force (F=Ma)

最后，可以使用牛頓力定律(F = Ma)計算加速度

In this way, one may calculate the acceleration of all the charged particles initially generated. This acceleration is going to be the major influence for the update of all the particles in every iteration.

以此方式，可以計算最初產生的所有帶電粒子的加速度。 該加速度將成為每次迭代中所有粒子更新的主要影響力。

The Influence of charged particle on other particles帶電粒子對其他粒子的影響

This influence can easily be understood as: suppose we generated five-positions randomly and named them Q1…Q5. Now using the above three equations one may calculate the exerted force of one charged particle on the other particles, i.e. we have the value of F generated by each charge and then by using E=F/Q and a=F/m we can calculate the acceleration of every charged particle. Since this concept is artificially used to generate influence for the movement of the positions, therefore, the artificial value of the charge is defined here which is

這種影響很容易理解為：假設我們隨機生成五個位置，并將它們命名為Q1…Q5。 現在，使用以上三個方程式，可以計算一個帶電粒子對其他粒子的作用力，即，我們具有每次電荷生成的F值，然后使用E = F / Q和a = F / m可以計算出每個帶電粒子的加速度。 由于此概念是人為地用于對位置的移動產生影響，因此，此處定義了電荷的人為值，即

where qi is the charge of each particle best is the best and worst are the best fit and worst fit charged particle which are very simple to calculate, if the problem is of minimization then the particle which has the minimal value in the generated solution is the best, similar for the worst and fit_pi is the fitness value of the ith particle for which we are calculating the charge.

其中qi是每個粒子的電荷最好的是最好的，最差的是最佳擬合和最差擬合的帶電粒子，它們非常容易計算，如果問題是最小化，則在生成的溶液中值最小的粒子是最好，與最壞情況相似，fit_pi是我們要為其計算電荷的第i個粒子的適應度值。

Pseudo Code of the AEFAAEFA的偽代碼 The behavior of the Electric Field on the Various Benchmark functions各種基準功能下的電場行為

The code scripts are:

代碼腳本為：

The detailed code scripts are available at:

詳細的代碼腳本可在以下位置獲得：

https://in.mathworks.com/matlabcentral/fileexchange/71218-aefa-artificial-electric-field-algorithm

https://in.mathworks.com/matlabcentral/fileexchange/71218-aefa-artificial-electric-field-algorithm

More details can be found at:

可以在以下位置找到更多詳細信息：

Anita, and Anupam Yadav. “AEFA: Artificial Electric Field Algorithm for Global Optimization.” Swarm and Evolutionary Computation, vol. 48, Elsevier BV, Aug. 2019, pp. 93–108, doi:10.1016/j.swevo.2019.03.013.

Anita和Anupam Yadav。 “ AEFA：用于全局優化的人工電場算法。” 群與進化計算，第一卷。 48，Elsevier BV，2019年8月，第93-108頁，doi：10.1016 / j.swevo.2019.03.013。

-Anupam

-阿努帕姆

翻譯自: https://medium.com/artifical-mind/artificial-electric-field-algorithm-for-optimization-fb6f57f413b4

電場 大學

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