In this work a comparative analysis of the effects of using an adaptive mutation operator is presented in the operational framework of a multi-objective GA for the design and selection of electrical load management strategies.
In this work, a mutation operator is introduced which mutates position vectors of few particles selected randomly.
The new mutation operator is based on the four kinds of mutation operation in PGA, which eliminates the mutation probability.
In this article, a directional mutation operator is proposed.
Forced mutation operator is introduced when the identical genes appear via the crossover procedure.
In order to provide extra diversity, quite helpful when population is drifting to local optima, mutation operator is used.
To increase the diversity of structure, mutation operator is used to change the number of active rules for particles.
Since PSO algorithms suffer premature convergence, especially when global best is found, mutation operator is applied to overcome the drawback.
The mutation operator is based on constructing a predictive model of velocity deficits across a layout so that mutations are inherently biased towards better layouts.
The directional mutation operator is shown to be better than the proximity based mutation operator on the five variants in the DE family.
Once sufficient feasible solutions are found, a niching method (along with a controlled mutation operator) is used to maintain diversity among feasible solutions.
