Particle Swarm Optimization Algorithm with Multiple Phases for Solving Continuous Optimization Problems

Table 1

The benchmark functions used in our experimental study, where is the dimension of each problem, global optimum is , is the minimum value of the function, and .

Function	Test function
Parabolic		100
Schwefel’s P2.22		100
Schwefel’s P1.2		100		450.0
Step quartic noise		100		180.0
		100		120.0
Rosenbrock		100
Schwefel		100
Rastrigin		100		450.0
Noncontinuous Rastrigin		100		180.0
Ackley		100		120.0
Griewank		100
Generalized penalized		100