Throughput Optimization of Backscatter-Assisted Wireless Relay Networks in Obstacle Environment

Algorithm 1

Algorithm of three parameter selection and maximum throughput solving.

1. The number of initialization population particles , the maximum number of iteration , lower limit of position range , the , the speed limit , . The -th particle is expressed as = (,, ), substituting it into (15) to calculate its fitness value.

2. Evaluate the fitness of each particle. Particle iteration range

    for itera =1: maxItera;

3. For each particle, compare its fitness value with the fitness value fpbest of the best position pbest it has experienced. If it is larger, take as the current best position pbest. For all particles,

    if index = find(fpbest<); then

        fpbest = ;

        pbest = ;

    end if

    The best fitness value and best position of individual history are updated.

4. For each particle , its fitness value is compared with the fitness value fgbest of the best position gbest experienced by all particles. If it is larger, it is regarded as the best position gbest of all particles at present to group.

    If fgbest < ; then

        fgbest = ;

        gbest = ;

    end if

    The best fitness value and best position of population history are updated.

5. Record the historical best position and best fitness value of the population.

    recg(itera) = gbest;

    recfg(itera) = fgbest;

    end for

6. Update the velocity and position of the particles according to (26) and (27).

7. Return to step 2 until the given maximum number of iterations is reached.