改进的自适应约束差分进化算法

粟朝阳; 封全喜; 韦彦婷; 苑赛楠

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改进的自适应约束差分进化算法

更新时间：2026-09-04

- 改进的自适应约束差分进化算法
- Improved self-adaptive constrained differential evolution algorithm
- 微电子学与计算机 2019年36卷第9期页码：30-37
- 作者机构：
  
  桂林理工大学大数据处理与算法技术研究中心,广西,桂林,541006
- 作者简介：
- 基金信息：
  
  国家自然科学基金 61763008国家自然科学基金 11661030国家自然科学基金 11401357国家自然科学基金 11861027广西区自然科学基金项目 2018GXNSFAA138095防城港市科学技术攻关项目防财教201442号
- DOI：
  中图分类号：
- 纸质出版：2019
- 稿件说明：
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粟朝阳, 封全喜, 韦彦婷, 苑赛楠. 改进的自适应约束差分进化算法[J]. 微电子学与计算机, 2019, 36(9): 30-37.

SU Zhao-yang, FENG Quan-xi, WEI Yan-ting, et al. Improved self-adaptive constrained differential evolution algorithm[J]. 2019, 36(9): 30-37.
粟朝阳, 封全喜, 韦彦婷, 苑赛楠. 改进的自适应约束差分进化算法[J]. 微电子学与计算机, 2019, 36(9): 30-37. DOI：

SU Zhao-yang, FENG Quan-xi, WEI Yan-ting, et al. Improved self-adaptive constrained differential evolution algorithm[J]. 2019, 36(9): 30-37. DOI：

摘要

为了更好地求解约束优化问题

提出了一种新的差分进化算法.首先该算法利用两个变异策略平衡局部搜索与全局搜索.其次用可行性规则选出新种群

并利用替换策略替换新种群中的较差个体.然后使用一种劣解变异机制

对最差个体进行变异以保持种群多样性.最后引入自适应参数控制机制

增强算法的鲁棒性和适应性.为了验证该算法的有效性

算法测试了10个标准约束优化问题和10个工程约束优化问题

实验结果表明

该算法求解精度较高、具有较快的收敛速度和较强的鲁棒性.

Abstract

In order to solve constrained optimization problems

a novel differential evolution algorithm is proposed. Firstly

the algorithm uses two mutation strategies to balance local search ability and global search ability. Secondly

the new population is selected based on the feasibility-based rule

and poor individuals in the new population are replaced by the substitution strategy. Then

a mutation mechanism is used to mutate the worst individual to maintain the diversity of the population. Finally

the adaptive parameter control mechanism is introduced to enhance the robustness and adaptability of the algorithm. In order to verify the effectiveness of this algorithm

10 benchmark constraint optimization problems and 10 engineering constraint optimization problems are tested. Experimental results show that the algorithm has high precision

fast convergence speed

and strong robustness.

关键词

Keywords

references

WANG Y, WANG B C, LI H X, et al. Incorporating objective function information into the feasibility rule for constrained evolutionary optimization[J]. IEEE Transactions on Cybernetics, 2016, 46(12): 2938-2952. DOI: 2938

MALLIPEDDI R, SUGANTHAN P N. Ensemble of constraint handling techniques[J]. IEEE Transactions on Evolutionary Computation, 2010, 14(4): 561-579. DOI: 561

MOHAMED A W. A novel differential evolution algorithm for solving constrained engineering optimization problems[J]. Journal of Intelligent Manufacturing, 2018, 29(3): 659-692. DOI: 659

LIANG J J, RUNARSSON T P, MEZURA-MONTES E, et al. Problem definitions and evaluation criteria for the CEC 2006 special session on constrained real-parameter optimization[J]. Journal of Applied Mechanics, 2006, 41(8): 8-31. DOI: 8

BREST J, GREINER S, BOSKOVIC B, et al. Self-adapting control parameters in differential evolution: a comparative study on numerical benchmark problems[J]. IEEE Transactions on Evolutionary Computation, 2006, 10(6): 646-657. DOI: 646

STORN R, PRICE K. Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces[J]. Journal of Global Optimization, 1997, 11(4): 341-359. DOI: 341

WANG B C, LI H X, LI J P, et al. Composite differential evolution for constrained evolutionary optimization[J]. IEEE Transactions on Systems Man % Cybernetics Systems, 2018, DOI: 10.1109/TSMC.2018.2807785

WANG Y, CAI Z, ZHOU Y, et al. An adaptive tradeoff model for constrained evolutionary optimization[J]. IEEE Transactions on Evolutionary Computation, 2008, 12(1): 80-92. DOI: 80

赵齐辉, 杜兆宏, 刘升, 等.差分进化的蜻蜓算法[J].微电子学与计算机, 2018, 35(7): 101-105. DOI: 101

WANG Y, CAI Z, ZHOU Y. Accelerating adaptive trade‐off model using shrinking space technique for constrained evolutionary optimization[J]. International Journal for Numerical Methods in Engineering, 2010, 77(11): 1501-1534. DOI: 1501

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