Efficient Fitness Estimation and Genetic Operation in Dynamic Environments

This paper introduces efficient fitness estimation and particle swarm operations for dynamic optimization problems in evolutionary computation. This fitness estimation method 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 a pair of real-valued vector (x, r) ¿ Rⁿ × R² in the evolutionary search population. The first vector x corresponds to a point in the n-dimensional search space (an object variable vector), while the second vector r represents the dynamic fitness value and the dynamic tendency of the individual x in the dynamic environment. r is the control variable (also called strategy variable), which allow self-adaptation. The object variable vector x is operated by different genetic operations according to its corresponding r. As a case study, we have integrated the new fitness estimation method into Particle Swarm Optimization (PSO), yielding an Particle Swarm Optimization in dynamic environments (PSODE). PSODE is experimentally tested with 5 benchmark dynamic problems. The results all demonstrate that PSODE outperforms other PSO on dynamic optimization problems.