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,σ,r)∈ Rn×Rn×R2 in the evolutionary search population. The first vector x corresponds to a point in the ndimensional 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.σ and r are thecontrol variables (also called strategy variables), which allow selfadaptation. The object variable vector x is operated by different genetic strategies according to its corresponding σ 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 S benchmark dynamic problems. The results all demonstrate that DOES outperforms other ES on dynamic optimization problems.
Evolutionary Algorithms Dynamic Optimization
Yong Liang
Faculty of Information Technology Macau University of Science and Technology Macau SAR,China
国际会议
上海
英文
61-65
2009-11-20（万方平台首次上网日期，不代表论文的发表时间）