each particle has three elements : location, velocity and fitness. Loc has p number of directions. P = {a1,a2, .......a_p}
Sum{square a_i } =1
fitness = Q(a)
Pid[i] = (loc, fitness)： the optimized position and fitness for each particle
Pgd : the most optimized situation for the group of particle.
Particles[i].velocity(t+1) = w particle[i].velocity(t) + m1r1(Pid[i].loc(t) - Particle[i].loc(t)) +m2r2(Pid[i].loc(t) - Particle[i].loc(t)) 1
Particles[i].loc(t+1) = w particle[i].loc(t) + m1r1(Pid[i].loc(t) - Particle[i].loc(t)) +m2r2(Pid[i].loc(t) - Particle[i].loc(t)) 2
Step one : M number of particles, the max interation Max gen R =p error
=e
Set M and max gen and R
And e
Step two:
Choose a as initial solution
Step three:
Calculate each fitness and set initial Verticity as 0
Step four:
Find the best Pid[i] and Pgd
Step five
Gen =1
Step six
Sumabs[(zt(i) - zt-1(I))]>=e and gen <maxgen to step 7
Otherwise 14
Step seven
Calculate velocity and location according to 1 and 2 equation.
Step eight
Get the 投影值
Step nine
Get Q(a)
Step ten
Update Pid[i] and the most optimized Pgd;
Step eleven
Gen = gen+1
Step 12
Return to six
Step 13
Best a star and z star
Step 14
根据一惟K均值聚类算法、对最佳投影值zstar进行聚类结果分析
