Dynamic Neighborhood-Based Particle Swarm Optimization for Multimodal Problems

Algorithm 2

DNPSO.

 Input: The population size, N_PSO, the maximum number of evaluations, MaxFEs;

 Output: Pbest;

(1) Generate an initial population with the size of NPSO with LHS, initialize Pbest;

(2) Evaluate the fitness of the particles in the initial population;

(3) FEs = N_PSO;

(4) While FEs < MaxFEs do;

(5) For i = 1: N_PSO;

(6)  Find the neighbors of Pbest_i with Algorithm1, include Ndr and Nr;

(7)  If the fitness of Pbest_i is better than that of each individual in Ndr and Nr, then;

(8)   Update the position of X_i by strategy (6);

(9)  Elseif the fitness of Pbest_i is the best in Ndr but is not the best in Nr, then;

(10)   Update the velocity and position of X_i using strategies (4) and (2), respectively;

(11)  Elseif the fitness of Pbest_i is not the best in Ndr and is not the worst in Nr, then;

(12)   Update the velocity and position of X_i with strategies (4) and (2), respectively;

(13)  Elseif the fitness of Pbest_i is worst in Ndr and Nr, then;

(14)   Update the velocity and position of X_i with strategies (7) and (2), respectively;

(15)  End;

(16)  Evaluate the fitness of X_i;

(17)  Update Pbest_i;

(18)  FEs = FEs + 1;

(19) End;

(20) End.