Dynamic evolutionary algorithm with variable relocation vectors

Many real-world optimization problems have to be performed under the presence of various uncertainties. A significant number of these uncertainty problems fall into the dynamic optimization category. For this class of problems, an evolutionary algorithm is expected to perform well under different levels and frequencies of change in the landscape. In addition, the dynamic evolutionary algorithm should warrant an acceptable performance improvement to justify the additional computational cost. Effective reuse of previous evolutionary information is a must as it facilitates a faster convergence after a change has occurred. This paper introduces a new dynamic evolutionary algorithm that uses variable relocation vectors to adapt already converged or currently evolving individuals to the new environmental condition. The proposed algorithm relocates those individuals based on their change in functional value due to the change in the environment and the average sensitivities of their decision variables to the corresponding change in the objective space. The relocated population is shown to be better fit to the new environment than the original or any other randomly generated population. The algorithm has been tested for several dynamic benchmark problems and has shown better results.