Development, Evaluation and Application of Multi-objective Optimization Methods by Genetic Algorithms

遗传算法多目标优化方法的开发、评估和应用

• 批准号: 12680402
• 负责人: NISHIKAWA Ikuko
• 金额: $ 1.41万
• 依托单位: Ritsumeikan University
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Scientific Research (C)
• 财政年份: 2000
• 资助国家: 日本
• 起止时间: 2000 至 2001
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-12680402/
• 关键词: Genetic Algorithm; Multi-objective Optimization; Parato Set; Coding; Parallel Selection; Correlation Coefficient; Evaluation; Knapsack Problem; 多目的最適化; デコーディング; 並列選択; パレート解; ナップザック問題; フローショップ問題

1. Multiple Coding Genetic Algorithm (MCGA) is proposed as a multi-objective GA method, in which each individual specializes on one of many objectives and has a different decoding according each objective. The comparison with a conventional parallel selection method by computer experiment shows the complementary performance in searching, therefore a hybrid method of the both is also proposed.2. The proposed method is applied 2-objective knapsack problems and 2-objective flowshop scheduling problem, and its performance is compared with the ordinal parallel selection.(1) In the application to knapsack problems, the data sets with different correlation coefficients are generated in a systematic way to control the distribution of a Pareto set.(2) In the application to flowshop scheduling, heuristic decoding methods are proposed for each objective.3. Quantitative evaluations of the obtained solutions are given by relative accuracy, cover ratio, diversity and the number of acquired solutions.4. Simulation result shows that the proposed method is effective to obtain diverse solutions especially in the problem with a large Pareto set, which results either from a statistical character of a given problem, from a largeness of a problem scale, or from a high dimensionality of a solution space.

1. 多重编码遗传算法（MCGA）是一种多目标遗传算法，其中每个个体专注于多个目标中的一个，并根据每个目标有不同的解码。通过计算机实验与传统的并行选择方法进行比较，显示了搜索性能的互补，因此提出了两者的混合方法。 2．该方法应用于2目标背包问题和2目标流水作业调度问题，并与序数并行选择的性能进行了比较。(1)在背包问题的应用中，不同相关系数的数据集生成在一个(2)在流水作业调度中的应用中，针对每个目标提出了启发式解码方法。3．通过相对准确度、覆盖率、多样性和获得的解的数量对所获得的解进行定量评价。 4．仿真结果表明，所提出的方法能够有效地获得多种解，特别是在具有大帕累托集的问题中，该问题要么是由于给定问题的统计特征、问题规模很大，要么是由于问题的高维性而导致。解空间。

项目成果

Ikuko Nishikawa, Naoki Hayashi: "An Autonomous Mobile Agent by a Chaotic Neural Network"Proc.2001 International Joint Conference on Neural Network. (CD-ROM). No.503 (2001)

Ikuko Nishikawa、Naoki Hayashi：“混沌神经网络的自治移动代理”Proc.2001 国际神经网络联合会议。

Ikuko Nishikawa, Naoki Hayashi: "An Autonomous Mobile Agent by a Chaotic Neural Network"Proc. 2001 International Joint Conference or Neural Networks. (CD-ROM). NO.503 (2001)

Ikuko Nishikawa、Naoki Hayashi：“混沌神经网络的自主移动代理”Proc。

西川郁子, 内田周志: "ニューラルネットワークの確率的学習則と自律移動ロボットの行動獲得への適用"計測自動制御学会論文誌. 37・12. 1169-1177 (2001)

Ikuko Nishikawa、Shuji Uchida：“神经网络的随机学习规则及其在自主移动机器人行为获取中的应用”《仪器与控制工程师学会杂志》37・12（2001）。

近藤 啓介, 西川 郁子: "行動ネットワークのHebb型学習による自律ロボットの行動獲得"システム制御情報学会論文誌. 15・7(掲載決定). (2002)

Keisuke Kondo、Ikuko Nishikawa：“通过 Hebb 型行为网络学习获取自主机器人行为”，系统、控制和信息工程师学会汇刊 15/7（2002 年出版）。

Ikuko Nishikawa, Keisuke Nishide: "A Mehod of Multiobjective Genetic Algoritums - Multiple Coding GA -"Trans. of the Society of Instrument and Control Engineers. 38-1. 87-96 (2002)

Ikuko Nishikawa、Keisuke Nishide：“多目标遗传算法的一种方法 - 多重编码 GA -”Trans。