A New Differential Evolution Algorithm for Dynamic Scheduling Problems with Variant Job Weights
In the real world, many practical applications are non-stationary optimization problems. This requires the optimization techniques not only. nd the global optimal solution but also track the trajectory of the changing global best solution. Dynamic scheduling problems pose great challenges to traditional differential evolutionary algorithms due to the diversity loss and low optimization ef.ciency. This paper introduces a new multi-population strategy for differential evolution (DE) algorithm to address the dynamic scheduling problems with variant job weighs. DE has always been applied for optimization problems in continuous solution space, while this new algorithm uses random key coding scheme to convey the continuous position vector to the sequential vector for each individual, and introduces a self-organized multi-population strategy to partition the population into parent population and child populations. The parent population is assigned to continuously search for new peaks, and child subpopulations are assigned for further exploitation in some promising areas. In addition, population sizes are adjusted according to their qualities for accelerating the optimization speed. It has been applied to the dynamic scheduling problems with variant job weights, the satisfactory results have been achieved.
Differential evolutionary Dynamic scheduling problems Multi-population strategy Self-organization
Lili Liu Dingwei Wang Jiafu Tang Yang Yu
Department of Systems Engineering, College of Information Science and Engineering, Northeastern Univ Department of Systems Engineering, College of Information Science and Engineering, Northeastern Univ
国际会议
2011 China Control and Decision Conference(2011中国控制与决策会议 CCDC)
四川绵阳
英文
274-278
2011-05-23（万方平台首次上网日期，不代表论文的发表时间）