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.|
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.|
//Chaotic variable element assignment.
|6.||Evaluate the fitness of :A (t), Δn (t) and ADC (t);|
|7.||Update the locally optimal particle: , and ;|
|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.|
// The particles with the worst fitness are replaced.
|//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.|
|17.||Output: Non-inferior optimal solution set P g (t).|
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