领导者引导与支配解进化的多目标矮猫鼬算法

doi:10.3778/j.issn.1673-9418.2211001

摘要/Abstract

摘要： 面对现实中日益复杂的多目标优化问题，需要发展新型多目标优化算法应对挑战。提出一种基于领导者引导与支配解动态缩减进化的多目标矮猫鼬优化算法（MODMO）。领导者引导机制通过引入动态权衡因子以调控侦察猫鼬探寻土丘的搜索半径，同时以非劣解集构建外部存档并根据非支配排序层级确定出领导者，进而引导侦察猫鼬向多目标前沿面推进以改善算法的收敛性；支配解动态缩减进化策略是为克服非劣解外部存档维护过程中的解冗余问题而构建，其以支配关系和拥挤距离动态筛选支配解并存入外部存档，以支配解信息融入种群进化实现多目标潜在前沿的挖掘并增强算法的多样性。在ZDT、DTLZ与WFG基准函数上，与5种代表性比较算法的实验结果表明MODMO算法在收敛性与多样性上均具有显著优势。

关键词: 多目标优化, 矮猫鼬优化算法, 领导者引导机制, 外部存档, 支配解动态缩减进化策略

Abstract: In the face of the increasingly complex multi-objective optimization problems, it is necessary to develop novel multi-objective optimization algorithms to meet the challenges. This paper proposes a multi-objective dwarf mongoose optimization algorithm (MODMO) with leader guidance and dominated solution dynamic reduction evolution mechanism. In the leader guidance mechanism, a dynamic trade-off factor is introduced to regulate the search radius of the scout mongoose exploring the mound. At the same time, an external archive is constructed with a non-inferior solution set and the leader is determined according to the non-dominated ranking level, and then the scout mongoose is guided to advance to the multi-objective frontier to improve the convergence of the algorithm. The dominant solution dynamic reduction evolution strategy is constructed to overcome the redundancy problem in the process of maintaining the external archive of non-inferior solutions. It dynamically selects the dominant solutions based on the dominance relationship and crowding distance and stores them in the external archive. The dominant solution information is integrated into the population evolution to realize the mining of multi-objective potential frontier and enhance the diversity of the algorithm. Compared with five representative algorithms on ZDT, DTLZ and WFG benchmark functions, experimental results show that MODMO algorithm has significant advantages in convergence and diversity.

Key words: multi-objective optimization, dwarf mongoose optimization algorithm, leader guidance mechanism, external archive, dominated solution dynamic reduction evolution strategy

赵世杰, 张红易, 马世林. 领导者引导与支配解进化的多目标矮猫鼬算法[J]. 计算机科学与探索, 2024, 18(2): 403-424.

ZHAO Shijie, ZHANG Hongyi, MA Shilin. Multi-objective Dwarf Mongoose Optimization Algorithm with Leader Guidance and Dominated Solution Evolution Mechanism[J]. Journal of Frontiers of Computer Science and Technology, 2024, 18(2): 403-424.

参考文献

[1] DENG W, ZHANG X, ZHOU Y, et al. An enhanced fast non-dominated solution sorting genetic algorithm for multiobjective problems[J]. Information Sciences, 2022, 585: 441-453.
[2] LI J, WANG P, DONG H, et al. A classification surrogate-assisted multi-objective evolutionary algorithm for expensive optimization[J]. Knowledge-Based Systems, 2022, 242: 108416.
[3] XU X, FU S, LI W, et al. Multi-objective data placement for workflow management in cloud infrastructure using NSGA-II[J]. IEEE Transactions on Emerging Topics in Com-putational Intelligence, 2020, 4(5): 605-615.
[4] COELLO C A C, PULIDO G T, LECHUGA M S. Handling multiple objectives with particle swarm optimization[J]. IEEE Transactions on Evolutionary Computation, 2004, 8(3): 256-279.
[5] ZITZLER E, LAUMANNS M, THIELE L. SPEA2: improving the strength Pareto evolutionary algorithm[J]. TIK-Report, 2001, 103.
[6] ZHANG Q, LI H. MOEA/D: a multiobjective evolutionary algorithm based on decomposition[J]. IEEE Transactions on Evolutionary Computation, 2007, 11(6): 712-731.
[7] 韩红桂, 卢薇, 乔俊飞. 一种基于多样性信息和收敛度的多目标粒子群优化算法[J]. 电子学报, 2018, 46(2): 315-324.
HAN H G， LU W， QIAO J F. A multiobjective particle swarm optimization algorithm based on the diversity information and convergence degree[J]. Acta Electronica Sinica, 2018, 46(2): 315-324.
[8] 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.
[9] 沈艳霞, 陈杰, 吴定会. 一种基于进化知识融合的多目标人工蜂群算法[J]. 控制与决策, 2017, 32(12): 2176-2182.
SHEN Y X, CHEN J, WU D H. A multi-objective artificial bee colony based on evolutionary knowledge integrated[J]. Control and Decision, 2017, 32(12): 2176-2182.
[10] 刘双双, 黄宜庆. 多策略蚁群算法在机器人路径规划中的应用[J]. 计算机工程与应用, 2022, 58(6): 278-286.
LIU S S, HUANG Y Q. Application of multi-strategy ant colony algorithm in robot path planning[J]. Computer Engineering and Applications, 2022, 58(6): 278-286.
[11] 蔡雨希, 何英杰, 陈涛, 等. 基于粒子群的三电平并网逆变器LCL滤波参数的高效精确设计方法[J]. 中国电机工程学报, 2020, 40(20): 6663-6674.
CAI Y X, HE Y J, CHEN T, et al. Efficient and accurate design method of LCL filter for three-level grid-connected inverter based on particle swarm optimization[J]. Chinese Journal of Electrical Engineering, 2020, 40(20): 6663-6674.
[12] WHITLEY D. A genetic algorithm tutorial[J]. Statistics and Computing, 1994, 4(2): 65-85.
[13] POLI R, KENNEDY J, BLACKWELL T. Particle swarm optimization[J]. Swarm Intelligence, 2007, 1(1): 33-57.
[14] DORIGO M, BIRATTARI M, STUTZLE T. Ant colony optimization[J]. IEEE Computational Intelligence Magazine, 2006, 1(4): 28-39.
[15] MIRJALILI S, GANDOMI A H, MIRJALILI S Z, et al. Salp swarm algorithm: a bio-inspired optimizer for engineering design problems[J]. Advances in Engineering Software, 2017, 114: 163-191.
[16] MIRJALILI S. SCA: a sine cosine algorithm for solving optimization problems[J]. Knowledge-Based Systems, 2016, 96: 120-133.
[17] 安家乐, 刘晓楠, 何明, 等. 量子群智能优化算法综述[J]. 计算机工程与应用, 2022, 58(7): 31-42.
AN J L, LIU X N, HE M, et al. Survey of quantum swarm intelligence optimization algorithm[J]. Computer Engineering and Applications, 2022, 58(7): 31-42.
[18] JANGIR P, BUCH H, MIRJALILI S, et al. MOMPA: multi-objective marine predator algorithm for solving multi-objective optimization problems[J]. Evolutionary Intelligence, 2023, 16(1): 169-195.
[19] CHOU J S, TRUONG D N. Multi-objective optimization inspired by behavior of jellyfish for solving structural design problems[J]. Chaos, Solitons & Fractals, 2020, 135: 109738.
[20] MIRJALILI S, SAREMI S, MIRJALILI S M, et al. Multi-objective grey wolf optimizer: a novel algorithm for multi-criterion optimization[J]. Expert Systems with Applications, 2016, 47: 106-119.
[21] DHIMAN G, KUMAR V. Multi-objective spotted hyena optimizer: a multi-objective optimization algorithm for engineering problems[J]. Knowledge-Based Systems, 2018, 150: 175-197.
[22] KOPPEN M, WOLPERT D H, MACEREADY W G. Remarks on a recent paper on the “no free lunch” theorems[J]. IEEE Transactions on Evolutionary Computation, 2001, 5(3): 295-296.
[23] AGUSHAKA J O, EZUGWU A E, ABUALIGAH L. Dwarf mongoose optimization algorithm[J]. Computer Methods in Applied Mechanics and Engineering, 2022, 391: 114570.
[24] AKINOLA O A, AGUSHAKA J O, EZUGWU A E. Binary dwarf mongoose optimizer for solving high-dimensional feature selection problems[J]. PLoS One, 2022, 17(10): e0274850.
[25] ALDOSARI F, ABUALIGAH L, ALMOTAIRI K H. A normal distributed dwarf mongoose optimization algorithm for global optimization and data clustering applications[J]. Symmetry, 2022, 14(5): 1021.
[26] 张孟健, 龙道银, 王霄, 等. 基于马尔科夫链的灰狼优化算法收敛性研究[J]. 电子学报, 2020, 48(8): 1587-1595.
ZHANG M J， LONG D Y， WANG X, et al. Research on convergence of grey wolf optimization algorithm based on Markov chain[J]. Acta Electronica Sinica, 2020, 48(8): 1587-1595.
[27] ZHANG S, REN Z, LI C, et al. A perturbation adaptive pursuit strategy based hyper-heuristic for multi-objective optimization problems[J]. Swarm and Evolutionary Computation, 2020, 54: 100647.
[28] SANDOVAL C, CUATE O, GONZALEZ L C, et al. Towards fast approximations for the hypervolume indicator for multi-objective optimization problems by genetic programming[J]. Applied Soft Computing, 2022, 125: 109103.
[29] DUMAN S, AKBEL M, KAHRAMAN H T. Development of the multi-objective adaptive guided differential evolution and optimization of the MO-ACOPF for wind/PV/tidal energy sources[J]. Applied Soft Computing, 2021, 112: 107814.
[30] HALLAM N, BLANCHFIELD P, KENDALL G. Handling diversity in evolutionary multi objective optimization[C]//Proceedings of the 2005 IEEE Congress on Evolutionary Computation. Piscataway: IEEE, 2005: 2233-2240.
[31] HUSSAIN K, SALLEH M N M, CHENG S, et al. On the exploration and exploitation in popular swarm-based metaheuristic algorithms[J]. Neural Computing and Applications, 2019, 31(11): 7665-7683.