How to use PHP to write a simulated artificial immune algorithm

How to use PHP to write a simulated artificial immune algorithm

Introduction:
The simulated artificial immune algorithm (Artificial Immune System, referred to as AIS) is a heuristic optimization algorithm based on the immune system. By simulating the immune system Processes such as antibodies, immune loss, and immune selection are used to solve optimization problems. This article will introduce how to use PHP to write a simple simulated artificial immune algorithm and provide code examples.

1. Basic principles of immune algorithm
1.1 Antibody
In the simulated artificial immune algorithm, antibodies are the main objects to solve optimization problems. An antibody is a multidimensional vector that represents a possible solution to a problem. Search for optimal solutions through antibody operations (generation, mutation, selection).

1.2 Immune Loss
Immune loss means that during the search process, some solutions lose immunity and are discarded. By setting a certain immune loss mechanism, the diversity of the population can be maintained and prevent falling into the local optimal solution.

1.3 Immune Selection
Immune selection refers to selecting excellent solutions for further operations based on fitness during the search process. The quality of the solution is usually evaluated based on the fitness value.

2. Steps to implement the simulated artificial immune algorithm
2.1 Initialize the population
First, we need to randomly generate a set of antibodies as the initial population. These antibodies can be randomly generated or initialized based on the characteristics of the problem.

2.2 Set the fitness function
The fitness function is used to evaluate the quality of the antibody. Depending on the problem, the definition of the fitness function will be different.

2.3 Immunization operation
In the immunity operation, we can implement operations such as antibody cloning and mutation. The cloning operation will generate a new antibody that is similar to the original antibody, and the mutation operation will change the original antibody to a certain extent.

2.4 Immunity loss
According to the set immune loss mechanism, we can select some antibodies for elimination to maintain the diversity of the population.

2.5 Immunity selection
According to the fitness value of the antibody, select excellent antibodies as the objects for the next step. Antibodies with higher fitness are usually selected.

2.6 Termination condition
During the iterative process of the algorithm, the termination condition needs to be set. The algorithm stops when the condition is met. For example, reaching a certain number of iterations or reaching a certain fitness threshold.

3. PHP code example
The following is an example of a simple simulated artificial immune algorithm written in PHP:

// Initialize population
function initializePopulation ($populationSize) {

$population = array();
for ($i = 0; $i < $populationSize; $i++) {
    // 生成随机抗体
    $antibody = generateAntibody();
    $population[] = $antibody;
}
return $population;

}

// Generate random antibodies
function generateAntibody() {

// 根据问题的特性生成抗体
// ...
return $antibody;

}

// Settings Fitness function
function fitnessFunction($antibody) {

// 计算抗体的适应度值
// ...

}

//Immune operation (clone)
function cloneAntibody($antibody) {

// 克隆抗体
// ...
return $newAntibody;

}

// Immune operation (mutation)
function mutateAntibody($antibody) {

// 变异抗体
// ...
return $newAntibody;

}

// Immune loss
function immuneLoss($ population, $lossRate) {

// 按照免疫损失率进行淘汰
// ...
return $newPopulation;

}

// Immune selection
function immuneSelection($population) {

// 根据适应度值选择优秀抗体
// ...
return $newPopulation;

}

// Main function
function main() {

// 参数设置
$populationSize = 50;
$maxIterations = 100;
$lossRate = 0.1;

// 初始化种群
$population = initializePopulation($populationSize);

// 迭代循环
for ($i = 0; $i < $maxIterations; $i++) {
    // 计算适应度值
    foreach ($population as $antibody) {
        $fitness = fitnessFunction($antibody);
        // 更新抗体的适应度值
        // ...
    }
    
    // 免疫操作（克隆、变异）
    foreach ($population as $antibody) {
        $newAntibody = cloneAntibody($antibody);
        // 更新克隆抗体的适应度值
        // ...
        
        $mutatedAntibody = mutateAntibody($antibody);
        // 更新变异抗体的适应度值
        // ...
        
        // 将克隆抗体和变异抗体加入种群
        $population[] = $newAntibody;
        $population[] = $mutatedAntibody;
    }
    
    // 免疫损失
    $population = immuneLoss($population, $lossRate);
    
    // 免疫选择
    $population = immuneSelection($population);
}

// 输出结果
// ...

}

// Execute the main function
main();
?>

Conclusion:
Through the above example, we learned how to use PHP to write a simple simulated artificial immune algorithm. Please note that in practical applications, depending on the characteristics of the problem, the details of the immune algorithm need to be adjusted and improved accordingly. I hope this article will help you understand and apply simulated artificial immune algorithms.

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