基于趋势预测模型的多目标分布估计算法.pdfVIP

Artificial Intelligence and Robotics Research 浜哄伐鏅鸿兘涓庢満鍣ㄤ汉鐮旂┒, 2016, 5(1), 1-12 Published Online February 2016 in Hans. /journal/airr /10.12677/airr.2016.51001 Trend Prediction Model Based Multi-Objective Estimation of Distribution Algorithm Zhongqiang Huang, Min Jiang Fujian Key Lab of the Brain-Like Intelligent Systems, Xiamen University (XMU), Xiamen Fujian th th th Received: Mar. 11 , 2016; accepted: Mar. 26 , 2016; published: Mar. 30 , 2016 Copyright 漏 2016 by authors and Hans Publishers Inc. This work is licensed under the Creative Commons Attribution International License (CC BY). /licenses/by/4.0/ Abstract Multi-objective optimization problems exist widely in real world applications. Traditional evolu- tionary algorithms usually employ individual-based evolution strategies to solve these optimiza- tion problems, leading to low convergence rate, strong dependency on population size and poor results. As a meta-heuristic algorithm, the Estimation of Distribution Algorithm (EDA) combines the statistical machine learning with population evolution model and has attracted a wide spread attention. In this paper, we proposed a trend-prediction-model (TPM) based EDA method, called TPM-EDA, to solve multi-objective problems. The characteristic of TPM is that it effectively utilizes the historic information generated in evolutionary process to predict the trend of particles, so as to promote the search speed for finding Pareto-optimal front and the search ability of algorithm. Meanwhile, the sparseness is applied in our algorithm to control the sampling frequencies of indi- viduals for the purpose of achieving the diversity of population. We compared our method with