A Competitive Swarm Optimizer-Based Technoeconomic Optimization with Appliance Scheduling in Domestic PV-Battery Hybrid Systems

Algorithm 1

Pseudo code of the CSO algorithm.

	Definition:
	x: the particle;
	P: the swarm;
	: the swarm size, i.e., the number of particles;
	G: number of iterations;
	and l: the indices of winner and loser particles;
	: the fitness function, assuming that this is a minimization problem;
	Terminal condition: the maximum number of iteration is reached;(1) Begin(2) Initialize population with particles;(3) while do(4)  P(G + 1) = ;(5)  while do(6)   Generate two random indices and from ;(7)   if then(8)    ;(9)   else(10)    ;(11)   end if(12)   put into ;(13)   If x is coded as continuous variables, update with (23) and (22);(14)   If x is coded as discrete variables, update with (24);(15)   If x contains both continuous and discrete parts, update the two parts separately;(16)   put the updated loser particle into ;(17)   remove particles and from ;(18)  end while(19)  G = G + 1;(20) end while(21) choose the particle with the best fitness from ;(22) Return ;(23) End