2017年12月14日 |  English version
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Improving Metaheuristic Algorithms with Information Feedback Models

报告题目Improving Metaheuristic Algorithms with Information Feedback Models

报告人:王改革副教授,江苏师范大学

时间:2017412日(周三)下午15:30

地点:行健楼学术活动室665

邀请人:孙越泓 副教授

报告摘要:In most metaheuristic algorithms, the updating process fails to make use of information available from individuals in previous iterations. If this useful information could be exploited fully and used in the later optimization process, the quality of the succeeding solutions would be improved significantly. This lecture presents a method for reusing the valuable information available from previous individuals to guide later search. In our approach, previous useful information was fed back to the updating process, and then six information feedback models were proposed. In these models, individuals from previous iterations were selected in either a fixed or random manner. Their useful information was incorporated into the updating process. Accordingly, an individual at the current iteration was updated based on the basic algorithm plus some selected previous individuals by using a simple fitness weighting method. By incorporating six different information feedback models into 10 metaheuristic algorithms, this approach provided a number of variants of the basic algorithms. We demonstrated experimentally that the variants outperformed the basic algorithms significantly on 14 standard test functions and 10 CEC 2011 real world problems, thereby establishing the value of the information feedback models.

 
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