This optimization algorithm is generalized from the mechanism of step leader propagation using the theory of fast particles known as projectiles.
The transition projectiles create the first step leader population for solutions, the space projectiles engage in exploration and attempt to become the leader, and the lead projectiles attempt to find and exploit the optimal solution .
If [P.sup.S.sub.i_new] new provides a good solution at step + 1 and the projectile energy [E.sup.S.sub.p_i] is greater than the step leader [E.sub.sl_i], then [p.sup.S.sub.i] is updated to [P.sup.S.sub.i_new].
Similarly, if [P.sup.L.sub.new] provides a good solution at step + 1 and the projectile energy [P.sup.L.sub.p_i] is greater than the step leader [E.sub.sl_i], then [p.sup.L] is updated to [P.sup.L.sub.new].
In the second type of forking, a channel is assumed to appear at a successful step leader tip because of the energy redistribution of the most unsuccessful leader after several propagation trials.
Elite opposition-based learning (EOBL)  is based on the elite step leader using OBL principle to generate elite opposition-based population to participate in competitive evolution, so as to improve population diversity of LSA.
If the elite opposition-based step leader [x'.sub.i,j] exceeds the search boundary, the processing formula is used as follows:
Define an elite step leader [X.sub.e] with the current best fitness value, and then use (12) to generate an elite opposition-based population with size n.
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