The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
본 논문에서는 다목적 운송 문제를 해결하기 위해 트리 기반 유전자 알고리즘을 포괄하는 새로운 접근 방식을 제시합니다. 네트워크 최적화 문제의 특별한 유형인 운송 문제는 운송 그래프라는 특징을 갖는 솔루션의 특별한 데이터 구조를 가지고 있습니다. 운송 문제 인코딩에서는 가능한 모든 기본 솔루션을 동일하고 고유하게 표현할 수 있는 스패닝 트리 기반 노드 인코딩 중 하나를 소개합니다. 교차 및 돌연변이는 이 인코딩을 기반으로 설계되었습니다. 또한 염색체가 항상 운송 트리로 변환되어 타당성을 갖는다는 기준을 설계했습니다. 진화 과정에서는 (μ+λ) 선택과 룰렛 휠 선택이 포함된 혼합 전략이 사용됩니다. 수치 실험은 제안된 알고리즘의 효율성과 효율성을 보여줍니다.
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부
Mitsuo GEN, Yinzhen LI, Kenichi IDA, "Solving Multi-Objective Transportation Problem by Spanning Tree-Based Genetic Algorithm" in IEICE TRANSACTIONS on Fundamentals,
vol. E82-A, no. 12, pp. 2802-2810, December 1999, doi: .
Abstract: In this paper, we present a new approach which is spanning tree-based genetic algorithm for solving a multi-objective transportation problem. The transportation problem as a special type of the network optimization problems has the special data structure in solution characterized as a transportation graph. In encoding transportation problem, we introduce one of node encodings based on a spanning tree which is adopted as it is capable of equally and uniquely representing all possible basic solutions. The crossover and mutation were designed based on this encoding. Also we designed the criterion that chromosome has always feasibility converted to a transportation tree. In the evolutionary process, the mixed strategy with (µ+λ)-selection and roulette wheel selection is used. Numerical experiments show the effectiveness and efficiency of the proposed algorithm.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e82-a_12_2802/_p
부
@ARTICLE{e82-a_12_2802,
author={Mitsuo GEN, Yinzhen LI, Kenichi IDA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Solving Multi-Objective Transportation Problem by Spanning Tree-Based Genetic Algorithm},
year={1999},
volume={E82-A},
number={12},
pages={2802-2810},
abstract={In this paper, we present a new approach which is spanning tree-based genetic algorithm for solving a multi-objective transportation problem. The transportation problem as a special type of the network optimization problems has the special data structure in solution characterized as a transportation graph. In encoding transportation problem, we introduce one of node encodings based on a spanning tree which is adopted as it is capable of equally and uniquely representing all possible basic solutions. The crossover and mutation were designed based on this encoding. Also we designed the criterion that chromosome has always feasibility converted to a transportation tree. In the evolutionary process, the mixed strategy with (µ+λ)-selection and roulette wheel selection is used. Numerical experiments show the effectiveness and efficiency of the proposed algorithm.},
keywords={},
doi={},
ISSN={},
month={December},}
부
TY - JOUR
TI - Solving Multi-Objective Transportation Problem by Spanning Tree-Based Genetic Algorithm
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 2802
EP - 2810
AU - Mitsuo GEN
AU - Yinzhen LI
AU - Kenichi IDA
PY - 1999
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E82-A
IS - 12
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - December 1999
AB - In this paper, we present a new approach which is spanning tree-based genetic algorithm for solving a multi-objective transportation problem. The transportation problem as a special type of the network optimization problems has the special data structure in solution characterized as a transportation graph. In encoding transportation problem, we introduce one of node encodings based on a spanning tree which is adopted as it is capable of equally and uniquely representing all possible basic solutions. The crossover and mutation were designed based on this encoding. Also we designed the criterion that chromosome has always feasibility converted to a transportation tree. In the evolutionary process, the mixed strategy with (µ+λ)-selection and roulette wheel selection is used. Numerical experiments show the effectiveness and efficiency of the proposed algorithm.
ER -