A Prediction Strategy Based on Guide-Individual for Dynamic Multi-Objective Optimization

ZHENG Jin-hua; PENG Zhou; ZOU Juan; SHEN Rui-min

doi:10.3969/j.issn.0372-2112.2015.09.021

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A Prediction Strategy Based on Guide-Individual for Dynamic Multi-Objective Optimization

更新时间：2025-07-16

- A Prediction Strategy Based on Guide-Individual for Dynamic Multi-Objective Optimization
- Acta Electronica Sinica Vol. 43, Issue 9, Pages: 1816-1825(2015)
- 作者机构：
  
  1. 湘潭大学信息工程学院,湖南,湘潭,411105
  2. 智能计算与信息处理教育部重点实验室,湖南,湘潭,411105
  3. 湘潭大学信息工程学院,湖南,湘潭,411105
  4. 智能计算与信息处理教育部重点实验室,湖南,湘潭,411105
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0372-2112.2015.09.021
  CLC：
- Published：2015
- 稿件说明：
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ZHENG Jin-hua, PENG Zhou, ZOU Juan, et al. A Prediction Strategy Based on Guide-Individual for Dynamic Multi-Objective Optimization[J]. Acta Electronica Sinica, 2015, 43(9): 1816-1825.
DOI：

ZHENG Jin-hua, PENG Zhou, ZOU Juan, et al. A Prediction Strategy Based on Guide-Individual for Dynamic Multi-Objective Optimization[J]. Acta Electronica Sinica, 2015, 43(9): 1816-1825. DOI： 10.3969/j.issn.0372-2112.2015.09.021.

摘要

很多现实的优化问题都是动态多目标问题

这类问题不仅具有多个目标

并且也受环境的影响不断变化.本文基于引导个体的预测策略提出一种新的求解动态多目标优化问题的策略.通过记录每次环境变化初始时和种群自主进化一小段时间后种群中心点位置的前后变化

预测最优解的所在方向.同时根据在该方向上均匀分布的若干检测个体

选出一串非支配的个体作为当前环境下的引导个体.为了避免陷入局部最优

在选出的引导个体周围一个小的区域半径内随机产生若干伴随引导个体.实验结果表明

新策略具有更快的响应环境变化的能力.

Abstract

Many real-world problems are dynamic multi-objective optimization problem.This kind of problem has multiple objectives

and these objectives change constantly due to the influence of environment.In this paper

a prediction strategy based on guide-individual (GIPS) is proposed.When environment changes

by recording the different center positions between populations in the initial environment and the ones evolving independently after a short time

GIPS predicts the direction of the optimal solutions.Moreover

from uniformly distributed individuals detected in the prediction direction

GIPS selects a bunch of non-dominated individuals as the guide-individuals for the current environment.In addition

the proposed strategy randomly generates a number of associated guide-individuals within a small area in order to avoid population to be trapped into local optimum.Compared with two state-of-the-art prediction-based dynamic multi-objective optimization algorithms

GIPS show faster response to the environmental changes.

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