An New Efficient Evolutionary Approach for Dynamic Optimization Problems

To improve the efficiency of the currently known evolutionary algorithms for dynamic optimization problems, we have proposed a novel variable representation allows static evolutionary optimization approaches to be extended to efficiently explore global and better local optimal areas in dynamic fitness landscapes It represents a single individual as three real-valued vectors (x,sigma,r)is an element of R-n x R-n x R-2 in the evolutionary search population The first vector x corresponds to a point in the n-dimensional search space (an object variable vector), the second vector describes the search step of x, while the third vector r represents the dynamic fitness value and the dynamic tendency of the individual x in the dynamic environment, sigma and r are the control variables (also called strategy variables), which allow self-adaptation The object variable vector x is operated by different genetic strategies according to its corresponding sigma and r As a case study, we have integrated the new variable representation into Evolution Strategy (ES), yielding an Dynamic Optimization Evolution Strategy (DOES) DOES is experimentally tested with 5 benchmark dynamic problems The results all demonstrate that DOES outperforms other ES on dynamic optimization problems