An Efficient Hybrid Optimization Approach Using Adaptive Elitist Differential Evolution and Spherical Quadratic Steepest Descent and Its Application for Clustering
Algorithm 2
Given the function and its domain , this algorithm finds the such that .
INPUT: and its domain ; convergence criteria , ,,, population size NP, scale factor F, crossover control parameter CR, and step limit d
Use Formula (1) to generate an initial population of NP individuals
Compute
WHILE delta >
Run the aeDE using mutation operator “rand/1” (formula (2)), binomial crossover operator (formula (4)) and elitist selection technique
Compute
ENDWHILE
WHILE delta >
Run the aeDE using mutation operator “current to best/1” (formula (3)), binomial crossover operator (Formula (4)) and elitist selection technique
Compute
ENDWHILE
Initialize a starting point
Run SQSD algorithm using convergences criteria and , step limit d > 0
