A Novel Bioinspired Multiobjective Optimization Algorithm for Designing Wireless Sensor Networks in the Internet of Things

Algorithm 1

MOCAS.

Step  1. Initialize the main population (ant colony) ;

     for each individual in

     initialize its position, search range, organization factor and other variables;

     initialize the archive ;

Step  2. Evaluate each individual in the population;

Step  3. Classify the individuals in into dominated individuals and non-dominated individuals ND;

     for each individual in

     flag = 0;

     for each individual in

       if is dominated by

        ;

       else

        ;

        flag = 1;

     if flag == 0

       ;

Step  4. Calculate crowding distance for ND;

     Initialize the distance to be zero for all individuals in ND ( denotes the size of ND);

     for each objective

     sort the individuals in ND based on objective ;

     assign infinite distance to boundary values for each individual in , i.e. and ;

     for to

      ;

     /* is the value of the th objective function of the th individual in */

Step  5. Update the archive ;

      ;

      classify into dominated individuals and non-dominated individuals ;

      ;

Step  6. Generate new population by (3) and re-defined concept of neighbor selection;

Step  7. if terminate is true

     Output the population;

   else

     goto   step  2;