Optimization of Allocation and Launch Conditions of Multiple Missiles for Three-Dimensional Collaborative Interception of Ballistic Targets

Algorithm 3

Covariance Matrix Adaptation with Mixed Variables (CMA-MV).

  Input: Objective Function ,

     Number of Continuous Samples per Iteration ,

     Number of Discrete Samples per Iteration ,

     Number of Iterations , Weights for continuous samples , Weights for

     discrete samples , Initial guess

  Output: Approximate optimal solution

  // Initialization

(1) , ,

(2) while    do

    // Fix the discrete variables to and use CMA to solve for  

(3)   ← Call CMA (Algorithm 1) with

      // Fix the continous variables and sample from the multivariate Bernoulli distrubution

(4)   for   in   do

      // Sample

(5)      using Algorithm 2  

    // Sort the candidate Solutions Based on Their Cost

(6)   , such that

    // Move the mean to low cost solutions

(7)   

    // Update The Covariance Matrix

(8)   

(9)   

(10)   

     // After the algorithm stops, output the best sample

(11) ,