以优秀个体为导向的多策略差分进化算法

doi:10.3778/j.issn.1002-8331.2008-0085

摘要/Abstract

摘要： 提出一种以优秀个体为导向的多策略差分进化算法。根据适应度值将种群等分为三个子种群，针对不同的种群使用不同的变异策略和控制参数。针对适应度值较差的种群提出了一种新的变异策略，通过引入学习因子和平衡因子，对提高收敛速度、精度和易陷入局部最优状态进行平衡，并对其中个体的控制参数采取自适应的机制，降低种群陷入停滞状态的概率。除此，在每次迭代完成之后，三个种群会重新组成一个新的种群，从而实现了不同种群之间信息的交互。用19个标准测试函数对所提出算法的性能进行了测试，并将其与一些主流差分算法进行比较。实验结果表明，所提出的算法在大部分函数的收敛速度以及精度上有明显的提升。

关键词: 差分进化（DE）, 多策略, 优秀个体, 学习因子, 平衡因子

Abstract: Multi-strategy differential evolutionary algorithm oriented by excellent individual is proposed. Firstly, According to the fitness value, the population is divided to three equal sub-population, different mutation strategies and control parameters are used for different populations. Secondly, a new mutation strategy is proposed for the population with poor fitness value, by introducing the learning factor and balance factor to balance the improvement of convergence speed, accuracy and falling into the local optimal state. An adaptive mechanism is adopted to the control parameters of the individual to reduce the probability of the population stagnating. In addition, a new population will be formed among the three populations after each iteration, thus the information interaction between different populations is realized. Finally, the performance of the proposed algorithm is tested by 19 standard test functions and compared with some mainstream differential evolutionary algorithms. The experimental results show that the convergence speed and accuracy of most functions are improved obviously.

Key words: differential evolution（DE）, multi-strategy, excellent individual, learning factor, balance factor

陈颖洁, 刘三阳, 张哲辰. 以优秀个体为导向的多策略差分进化算法[J]. 计算机工程与应用, 2022, 58(2): 137-144.

CHEN Yingjie, LIU Sanyang, ZHANG Zhechen. Multi-strategy Differential Evolutionary Algorithm Oriented by Excellent Individual[J]. Computer Engineering and Applications, 2022, 58(2): 137-144.

参考文献

[1] STORN R，PRICE K.Differential evolution-a simple and efficient heuristic for global optimization over continuous space[J].Journal of Global Optimization，1997，11（4）：341-359.
[2] STORN R P，PRICE K.Differential evolution-a simple and efficient adaptive scheme for global optimization over continuous spaces：Technical Report TR-95-012[R].Berkeley，California：International Computer Science Institute，1995.
[3] DAS S，SUGANTHAN P N.Differential evolution：a survey of the state-of-the-art[J].IEEE Transactions on Evolutionary Computation，2011，15（1）：4-31.
[4] ZELINKA I，DAVENDRA D，SNASEL V，et al.Preliminary investigation on relations between complex networks and evolutionary algorithms dynamics[C]//2010 International Conference on Computer Information Systems and Industrial Management Applications（CISIM），2010：148-153.
[5] 丁毓，刘三阳，陈静静，等.基于复杂网络的差分进化算法研究[J].复杂系统与复杂性科学，2018，15（2）：1-9.
DING Y，LIU S Y，CHEN J J，et al.Differential evolution algorithm based on complex networks[J].Complex Systems and Complexity Science，2018，15（2）：1-9.
[6] SKANDEROVA L，FABIAN T，ZELINKA I.Differential evolution based on node strength[J].Int J Bio-Inspired Computation，2018，11（1）：34-45.
[7] 张哲辰，刘三阳.基于拓扑改进与交叉策略的萤火虫算法[J].计算机工程与应用，2019，55（7）：1-8.
ZHANG Z C，LIU S Y.Firefly algorithm based on topology improvement and crossover strategy[J].Computer Engineering and Applications，2019，55（7）：1-8.
[8] 肖婧，许小可，张永建，等.差分进化算法及其高维多目标优化应用[M].北京：人民邮电出版社，2018：12-31.
XIAO J，XU X K，ZHANG Y J，et al.Differential evolution algorithm and its application in multi-objective optimization[M].Beijing：Posts & Telecom Press，2018：12-31.
[9] 冯勇，张冰茹，徐红艳，等.结合改进差分进化和模块密度的社区发现算法[J].计算机科学与探索，2020，14（6）：1070-1080.
FENG Y，ZHANG B R，XU H Y，et al.Community detection algorithms combining improved differential evolution and modularity density[J].Journal of Frontiers of Computer Science and Technology，2020，14（6）：1070-1080.
[10] 黄辉先，胡拚，丁灿，等.进化信息引导的烟花差分混合多目标算法[J].计算机科学与探索，2019，13（3）：481-493.
HUANG H X，HU P，DING C，et al.Fireworks and differential hybrid multi-objective algorithm guided by evolutionary information[J].Journal of Frontiers of Computer Science and Technology，2019，13（3）：481-493.
[11] 张海南，游晓明，刘升，等.交互式学习的布谷鸟搜索算法[J].计算机工程与应用，2020，56（7）：147-154.
ZHANG H N，YOU X M，LIU S，et al.Interactive learning cuckoo search algorithm[J].Computer Engineering and Applications，2020，56（7）：147-154.
[12] LIANG L L，QIN A K，SUGANTHAN P N.Comprehensive learning particle swarm optimizations for global optimization of multimodal functions[J].IEEE Transactions on Evolutionary Computation，2006，10（3）：281-295.
[13] LI S，GU Q，GONG W，et al.An enhanced adaptive differential evolution algorithm for parameter extraction of photovoltaic models[J].Energy Conversion and Management，2020，205：112443.
[14] 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.
[15] QIN A K，HUANG V L，SUGANTHAN P N.Differential evolution algorithm with strategy adaptation for global numerical optimization[J].IEEE Transactions on Evolutionary Computation，2009，13（2）：398-417.
[16] ZHU L，MA Y J，BAI Y L.A self-adaptive multi-population differential evolution algorithm[J].Natural Computing，2020，19：211-235.
[17] LIU X F，ZHAN Z H.Historical and heuristic-based adaptive differential evolution[J].IEEE Transactions on Systems，Man，and Cybernetics Systems，2019，49（12）：2623-2635.
[18] 孙灿，周新宇，王明文.一种融合邻域搜索的多策略差分进化算法[J].系统仿真学报，2020，32（6）：1071-1084.
SUN C，ZHOU X Y，WANG M W.A multi-strategy differential evolution algorithm combined with neighborhood search[J].Journal of System Simulation，2020，32（6）：1071-1084.
[19] LI X T，DONG Y，LIU J Y.Differential evolution with an individual-dependent mechanism[J].IEEE Transactions on Evolutionary Computation，2015，19（4）：560-574.
[20] SUN G J，WU Y R，DENG L B，et al.Elite representative based individual adaptive regeneration framework for differential evolution[J].IEEE Access，2020，8：61226-61245.
[21] ALI M Z，AWAD N H，SUGANTHAN P N.Multi-population differential evolution with balanced ensemble of mutation strategies for large-scale global optimization[J].Applied Soft Computing，2015，33：304-327.