Open Access
Table 1
Pseudo code of the CIMPSO algorithm.
| Input: Learning factor c1,c2; Particle swarm size N; Number of iterations T; Velocity inertia weight wu; Position inertia weight wx; Expansion factors α and β; Control parameter μ; Chaotic variable element numbering c. | |
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Initialization: The position of the particles  ;//j is the number of the particle element. |
|
| 1. | For n = 1 to T |
| 2. | For m = i to N |
| 3. | Characteristic number of nonlinear element  ; |
| //i is Number of the particles. | |
| 4. | Generate chaotic variables  ; |
| //t is the number of iteration steps. | |
| 5. |
 ;//Chaotic variable element assignment. |
| 6. | Evaluate the fitness of  :A (t), Δn (t) and ADC (t); |
| 7. | Update the locally optimal particle:  ,  and  ; |
| 8. | End For |
| 9. | Perform non-dominated sorting on the local optimal particle |
| fitness to get the first dominated layer particle P (t); | |
| 10. | Global optimal particle P g (t+1) = maxfit [P d (t)]; |
| //maxfit (⋅) represents the particle with the optimal fitness. | |
| 11. |
 ; //Chaos (⋅) represents chaotic map. |
| 12. |
 ;// The particles with the worst fitness are replaced. |
| 13. |
 ; |
| //worst represents the subscript of the worst-fit particle. | |
| 14. | V i (t + 1) = wv V i (t) + c1 r1 [P i (t) − X i (t)] + c2 r2 [P g (t) − X i (t)]; |
| // Update particle velocity. | |
| 15. | X i (t + 1) = wx X i (t) + V i (t + 1); // Update particle position. |
| 16. | EndFor |
| 17. | Output: Non-inferior optimal solution set P g (t). |
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