Elite opposition-based learning fish swarm algorithm for solving MMVRP

LI Xiaochuan; LIU Yuanhua; WANG Yingge

doi:10.13873/j.1000-9787(2020)02-0140-04

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Elite opposition-based learning fish swarm algorithm for solving MMVRP

更新时间：2025-09-11

- Elite opposition-based learning fish swarm algorithm for solving MMVRP
- Issue 2, Pages: 140-143+152(2020)
- 作者机构：
  
  上海理工大学管理学院
- 作者简介：
- 基金信息：
- DOI：10.13873/j.1000-9787(2020)02-0140-04
  CLC： TP18
- Published：2020
- 稿件说明：
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LI Xiaochuan, LIU Yuanhua, WANG Yingge. Elite opposition-based learning fish swarm algorithm for solving MMVRP[J]. 2020, (2): 140-143+152.
DOI：

LI Xiaochuan, LIU Yuanhua, WANG Yingge. Elite opposition-based learning fish swarm algorithm for solving MMVRP[J]. 2020, (2): 140-143+152. DOI： 10.13873/j.1000-9787(2020)02-0140-04.

摘要

针对以缩短最大子线路长度为目标的最小最大车辆路径问题的求解

提出一种精英反向学习鱼群算法。根据客户近邻矩阵构建初始鱼群

提高鱼群的优良性。结合问题特征与人工鱼群算法的寻优思想

重新定义了群体的觅食、聚群和追尾行为。受反向学习的启发

构建了精英完全反向学习和精英局部反向学习两种求离散反向解的机制

提高算法的全局搜索能力。通过仿真实验及与其它算法对比

表明精英反向学习鱼群算法在求解该问题时具有较高的搜索质量和稳定性。

Abstract

To solve the min-max vehicle routing problem（ MMVRP）

aiming at shortening length of the longest sub-line

an elite opposition-based learning artificial fish swarm algorithm（ AFSA） is proposed. According to the customer’s nearest neighbor matrix

the initial artificial fish swarm is constructed to improve the quality of the fish swarm. Considering the features of this problem and the artificial fish swarm algorithm

the intelligent behaviors of artificial fishes are redefined. Inspired by opposition-based learning

two mechanisms are built for constructing discrete opposition-based solutions including full elite opposition-baesd learning and partial elite opposition-baesd learning. Simulation results show that the elite opposition-based fish swarm algorithm can get a high search quality and stability in solving this problem.

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