<{V}_{mathrm{id}}X Y mathrm{C}mathrm{ H}mathrm{R}mathrm{E}mathrm{S} mathrm{Time}> where V id is the vehicle identity, X and Y are the coordinates received from GPS to locate the segment, CH is the channel sensed from 1 to M, RES is the sensing result (1 or 0), and Time is the sensing time.
Velocities v id new and v id old are the new and old velocities, respectively.
In the case that id v' ≤ id v, we can switch v and v' and thus get time usage O id v id v' min{ id v, id v' }).
Velocities v id new and v id old denote the dth velocities of the ith particle in the current and the last generations, respectively.
The internal degree of an internal node v, id v, is the number of non-leaf subtrees connected to it.
The total time of computing all sums mentioned is thus O id v id v' ) and this is the key to reducing the time usage of shared v, v').
The pseudocode of the HPSO algorithm is as follows [ 24]: Begin Initialize the population while (termination condition = false) do for (i = 1 to n) Evaluate fitness Update p id Update p gd for (d = 1 to dimensionality of the search space) Calculate v id Update x id end for end for end while End.
The position of the ith particle is represented as X i = (x i 1, x i 2,…, x iD ), and the velocity V i = (v i 1, v i 2,…, v iD ), where 1 ≤ i ≤ N. The positions and velocities of particles are confined within [ X min, X max] D and [ V min, V max] D, respectively.
Moreover, suppose Xi = (x i 1, x i 2,... x id ) is the position of the ith particle, Vi = (v i 1, v i 2,..., v id ) is its velocity vector, Pi = (p i 1, p i 2,..., p id ) is its personal best position and P g = (p g 1, p g 2,..., p gd ) is the best particle found so far.
