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| Received:September 21, 2010Revised:September 08, 2012 |
| 基金项目:This work was 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), the Science Fund for Creative Research Group of NNSF of China (No. 60821063), and the Ph.D. Programs Foundation of Ministry of Education of China (No. 200801450008). |
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| An immune system based differential evolution algorithm using near-neighbor effect in dynamic environments |
| Lili LIU,Dingwei WANG,Jiafu TANG |
| (PetroChina Northeast Refining & Chemicals Engineering Company Limited;School of Information Science and Engineering, Northeastern University; State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University) |
| Abstract: |
| 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 ‘nearneighbor-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 differencerelated 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. |
| Key words: Differential evolution Immune system based scheme Near-neighbor effect Difference-related multidirectional amplification Dynamic optimization problem. |
