• Specify the parameters for MOPSO Algorithm


## • Initialize Swarm Population

	- Initialize external repository rep
	- Create grid and find grid index

process. At each generation, for each particle in the swarm, by using Roulette wheel selection, a leader is selected from the external repository. This leader then guides other particles towards better regions of the search space by modifying the flight of the particles. A special mutation operator is applied to the particles of the swarm and also to the range of each design variable of the problem to be solved to improve the explorative behavior of the algorithm. The value of the mutation operator is decreased during the iteration. To produce well spread Pareto fronts the MOPSO algorithm in [23] uses an adaptive grid. The MOPSO algorithm for solving the CEED

problem is stated below:

90 Particle Swarm Optimization with Applications

• Specify the parameters for the CEED problem

• Number of decision variables nVar

• Specify the parameters for MOPSO Algorithm

• Maximum number of iteration MaxIt

• Number of grids per dimension nGrid

• Generate a random swarm particles

• Initialize external repository rep • Create grid and find grid index

• Population Size nPop • Repository size nRep

gamma

• Mutation rate mu

• Determine Domination

• Initialize Swarm Population

tion 3) is carried out.

• The total demand of the power system Pd

• Fuel cost and emission coefficients for each generating unit

• Inertia Weight w and Inertia Weight damping rate wdamp

• Store the values of the particles as their personal best pBest

• Personal learning coefficient c1 and Global learning coefficient c2

• Inflation Rate alpha, leader selection pressure beta, Deletion selection pressure

• Once the random particles are initialized the Constraint Handling Mechanism (Sec-

• B matrix coefficients for transmission loss calculations

• Lower bounds of the decision variables VarMin • Upper bounds of the decision variables VarMax


## • End for

