Many real-world problems are dynamic, requiring optimization algorithms being able to continuously track changing optima （optimum） over time. This paper proposes an improved differential evolutionary algorithm using...Many real-world problems are dynamic, requiring optimization algorithms being able to continuously track changing optima （optimum） over time. This paper proposes an improved differential evolutionary algorithm using the notion of the near-neighbor effect to determine one individuals neighborhoods, for tracking multiple optima in the dynamic environment. A new mutation strategy using the near-neighbor effect is also presented. It creates individuals by utilizing the stored memory point in its neighborhood, and utilizing the differential vector produced by the ＇near- neighbor-superior＇ and ＇near-neighbor-inferior＇. Taking inspirations from the biological immune system, an immune system based scheme is presented for rapidly detecting and responding to the environmental changes. In addition, a difference- related multidirectional amplification scheme is presented to integrate valuable information from different dimensions for effectively and rapidly finding the promising optimum in the search space. Experiments on dynamic scenarios created by the typical dynamic test instance--moving peak problem, have demonstrated that the near-neighbor and immune system based differential evolution algorithm （NIDE） is effective in dealing with dynamic optimization functions.展开更多
基金partly supported by the Key Program of National Natural Science Foundation(NNSF) of China(Nos.70931001,70771021,70721001)the National Natural Science Foundation of China for Youth(Nos.61004121,70771021)+1 种基金the Science Fund for Creative Research Group of NNSF of China(No.60821063)the Ph.D.Programs Foundation of Ministry of Education of China(No.200801450008)
文摘Many real-world problems are dynamic, requiring optimization algorithms being able to continuously track changing optima （optimum） over time. This paper proposes an improved differential evolutionary algorithm using the notion of the near-neighbor effect to determine one individuals neighborhoods, for tracking multiple optima in the dynamic environment. A new mutation strategy using the near-neighbor effect is also presented. It creates individuals by utilizing the stored memory point in its neighborhood, and utilizing the differential vector produced by the ＇near- neighbor-superior＇ and ＇near-neighbor-inferior＇. Taking inspirations from the biological immune system, an immune system based scheme is presented for rapidly detecting and responding to the environmental changes. In addition, a difference- related multidirectional amplification scheme is presented to integrate valuable information from different dimensions for effectively and rapidly finding the promising optimum in the search space. Experiments on dynamic scenarios created by the typical dynamic test instance--moving peak problem, have demonstrated that the near-neighbor and immune system based differential evolution algorithm （NIDE） is effective in dealing with dynamic optimization functions.
