Multi-Objective Firefly Algorithm Based on Multiply Cooperative Strategies

XIE Cheng-wang; ZHANG Fei-long; LU Jian-bo; XIAO Chi; LONG Guang-lin

doi:10.3969/j.issn.0372-2112.2019.11.018

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Multi-Objective Firefly Algorithm Based on Multiply Cooperative Strategies

更新时间：2025-07-16

- Multi-Objective Firefly Algorithm Based on Multiply Cooperative Strategies
- Acta Electronica Sinica Vol. 47, Issue 11, Pages: 2359-2367(2019)
- 作者机构：
  
  1. 南宁师范大学计算机与信息工程学院,广西,南宁,530299
  2. 华东交通大学软件学院,江西,南昌,330013
  3. 南宁师范大学计算机与信息工程学院,广西,南宁,530299
  4. 华东交通大学软件学院,江西,南昌,330013
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0372-2112.2019.11.018
  CLC： TP18
- Published Online：25 November 2019，
  
  Published：2019
- 稿件说明：
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XIE Cheng-wang, ZHANG Fei-long, LU Jian-bo, et al. Multi-Objective Firefly Algorithm Based on Multiply Cooperative Strategies[J]. Acta Electronica Sinica, 2019, 47(11): 2359-2367.
DOI：

XIE Cheng-wang, ZHANG Fei-long, LU Jian-bo, et al. Multi-Objective Firefly Algorithm Based on Multiply Cooperative Strategies[J]. Acta Electronica Sinica, 2019, 47(11): 2359-2367. DOI： 10.3969/j.issn.0372-2112.2019.11.018.

摘要

现实中的多目标优化问题不断增多且日益复杂，需要不断发展新型启发式算法应对挑战.提出一种多策略协同的多目标萤火虫算法MOFA-MCS.该算法采用均匀化与随机化相结合的方法产生初始种群；利用档案集中的精英解个体指导萤火虫移动；并在移动的过程施加Lvy flights随机扰动；最后，利用-三点最短路径策略维护档案解群的多样性.MOFA-MCS算法与其他6种经典的多目标进化算法一同在12个基准的多目标测试问题上进行实验，结果表明所提算法在收敛性、多样性方面总体上具有显著的性能优势.

Abstract

More and more complex multi-objective optimization problems have emerged in the real world

and the novel heuristic algorithms need to be developed to meet the challenge. A multi-objective firefly algorithm based on multiply cooperative strategies (MOFA-MCS) is proposed in the paper. MOFA-MCS uses the method of homogenization and randomization to generate the initial population

adopts the elite solutions in the external archive to lead the firefly to move

exerts Lévy flights to add random disturbance in the moving process

and finally

the ε-three-point shortest path strategy is also applied to maintain the diversity of the archive solutions. MOFA-MCS is compared with other six representative multi-objective evolutionary algorithms on 12 benchmark multi-objective test problems

and the experimental results show that MOFA-MCS has significant performance advantages in terms of convergence and diversity.

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