融合联合反向学习与宿主切换机制的䲟鱼优化算法

doi:10.3778/j.issn.1673-9418.2210057

摘要/Abstract

摘要： 䲟鱼优化算法（ROA）是2021年提出的元启发式优化算法，其模拟了海洋中?鱼寄生依附宿主、经验攻击和宿主觅食的行为。ROA的结构简单且易于实现，但全局性稍显不足，易导致算法收敛速度慢甚至后期难以收敛的现象。针对上述问题，在探索阶段加入宿主切换机制，引入新宿主白鲸，提高原算法的探索能力；同时加入联合反向学习策略，增强了算法跳出局部最优的能力，进一步提高了算法的综合优化性能。通过以上改进，提出了一种融合联合反向学习与宿主切换机制的?鱼优化算法（IROA）。为了验证IROA的性能与改进优势，将IROA与原始ROA、6种典型的原始算法以及4种关于ROA的改进算法进行对比。通过CEC2020标准测试函数的实验结果表明，IROA具有更强的寻优能力和更高的收敛精度；最后针对汽车防撞性设计问题的求解，进一步验证了IROA的优势和工程适用性。

关键词: ?鱼优化算法, 元启发式优化算法, 联合反向学习, 宿主切换机制, 白鲸优化算法, 基准函数测试, 工程问题求解

Abstract: The remora optimization algorithm (ROA) is a meta heuristic optimization algorithm proposed in 2021. It simulates the behavior of parasitic attachment to the host, empirical attack and host foraging in the ocean. The structure of ROA is simple and easy to implement, but the overall situation is slightly insufficient, which easily leads to ROA’s slow convergence speed and even difficult convergence in the later period. To solve the above problems, host switching mechanism is added in the exploration phase, and new host beluga is introduced to improve the exploration ability of original ROA. At the same time, through adding joint opposite selection strategy, the ability of the algorithm to jump out of the local optimum is enhanced, and the comprehensive optimization performance of the algorithm is further improved. Through the above improvements, an improved remora optim-ization algorithm (IROA) is proposed, which integrates the joint opposite selection and host switching mechanism. In order to verify the performance and improvement advantages of IROA, IROA is compared with the original ROA, six typical original algorithms and four improved algorithms on ROA. Experimental results of CEC2020 standard test function show that IROA has stronger optimization ability and higher convergence accuracy. Finally, the advantages and engineering applicability of the improved algorithm are further verified by solving the car crashworthiness design problem.

Key words: remora optimization algorithm, meta heuristic optimization algorithm, joint opposite selection, host switching mechanism, beluga whale optimization, benchmark function test, engineering problem solving

贾鹤鸣, 文昌盛, 吴迪, 饶洪华, 刘庆鑫, 力尚龙. 融合联合反向学习与宿主切换机制的䲟鱼优化算法[J]. 计算机科学与探索, 2023, 17(12): 2896-2912.

JIA Heming, WEN Changsheng, WU Di, RAO Honghua, LIU Qingxin, LI Shanglong. Remora Optimization Algorithm Combining Joint Opposite Selection and Host Switching[J]. Journal of Frontiers of Computer Science and Technology, 2023, 17(12): 2896-2912.

参考文献

[1] SM A, SMM B, AL A. Grey wolf optimizer[J]. Advances in Engineering Software, 2014, 69: 46-61.
[2] HASHIM F A, HUSSIEN A G. Snake optimizer: a novel meta-heuristic optimization algorithm[J]. Knowledge-Based Syst-ems, 2022, 242: 108320.
[3] LI S M, CHEN H L, WANG M J, et al. Slime mould algorithm: a new method for stochastic optimization[J]. Future Generation Computer Systems, 2020, 111: 300-323.
[4] MIRJALILI S. SCA: a sine cosine algorithm for solving optimization problems[J]. Knowledge-Based Systems, 2016, 96: 1-14.
[5] AAHA B, SM C, HF D, et al. Harris hawks optimization: algorithm and applications[J]. Future Generation Computer Systems, 2019, 97: 849-872.
[6] ABUALIGAH L, DIABAT A, MIRJALILI S, et al. The arithmetic optimization algorithm[J]. Computer Methods in Applied Mechanics and Engineering, 2021, 376: 113609.
[7] WOLPERT D H, MACREADY W G. No free lunch theor-ems for optimization[J]. IEEE Transactions on Evolutio-nary Computation, 1997, 1(1): 67-82.
[8] 毛清华, 张强. 融合柯西变异和反向学习的改进麻雀算法[J]. 计算机科学与探索, 2021, 15(6): 1155-1164.
MAO Q H, ZHANG Q. Improved sparrow algorithm Com-bining Cauchy mutation and opposition-based learning[J]. Journal of Frontiers of Computer Science and Technology, 2021, 15(6): 1155-1164.
[9] 贾鹤鸣, 姜子超, 李瑶. 基于改进秃鹰搜索算法的同步优化特征选择[J]. 控制与决策, 2022, 37(2): 445-454.
JIA H M, JIANG Z C, LI Y. Simultaneous feature selection optimization based on improved bald eagle search algorithm[J]. Control and Decision, 2022, 37(2): 445-454.
[10] 罗仕杭, 何庆. 混沌精英池协同教与学改进的ChOA及其应用[J]. 计算机工程与应用, 2023, 59(6): 299-309.
LUO S H, HE Q. Chimp optimization algorithm improved by chaos elite pool collaborative teaching-learning and its mechanical application[J]. Computer Engineering and Applications, 2023, 59(6): 299-309.
[11] 魏伟一, 文雅宏. 一种精英反向学习的萤火虫优化算法[J]. 智能系统学报, 2017, 12(5): 710-716.
WEI W Y, WEN Y H. Firefly optimization algorithm utilizing elite opposition-based learning[J]. CAAI Transa-ctions on Intelligent Systems, 2017, 12(5): 710-716.
[12] 孙成硕, 戚志东, 叶伟琴, 等. 变异反向学习的自适应帝王蝶优化算法[J]. 计算机工程与应用, 2022, 58(11): 66-72.
SUN C S, QI Z D, YE W Q, et al. Adaptive monarch butterfly algorithm based on mutation reverse learning[J]. Computer Engineering and Applications, 2022, 58(11): 66-72.
[13] ZHANG H, WANG Z, CHEN W, et al. Ensemble mutation-driven salp swarm algorithm with restart mechanism: framework and fundamental analysis[J]. Expert Systems with Applications, 2021, 165: 113897.
[14] 贾鹤鸣, 刘宇翔, 刘庆鑫, 等. 融合随机反向学习的黏菌与算术混合优化算法[J]. 计算机科学与探索, 2022, 16(5): 1182-1192.
JIA H M, LIU Y X, LIU Q X, et al. Hybrid algorithm of slime mould algorithm and arithmetic optimization algori-thm based on random opposition-based learning[J]. Journal of Frontiers of Computer Science and Technology, 2022, 16(5): 1182-1192.
[15] 宋飞, 夏克文, 杨文彪. 融合多策略的鸟群算法及油层识别ELM模型优化[J]. 计算机工程与应用, 2022, 58(9): 279-287.
SONG F, XIA K W, YANG W B. Mix with multiple strategies bird swarm algorithm and optimization of ELM model in oil layer classification[J]. Computer Engineering and Applications, 2022, 58(9): 279-287.
[16] 张晗, 杨继斌, 张继业, 等. 基于多种群萤火虫算法的车载燃料电池直流微电网能量管理优化[J]. 中国电机工程学报, 2021, 41(3): 833-846.
ZHANG H, YANG J B, ZHANG J Y, et al. Multiple-population firefly algorithm-based energy management stra-tegy for vehicle-mounted fuel cell DC microgrid[J]. Proce-edings of the CSEE, 2021, 41(3): 833-846.
[17] JIA H M, PENG X X, LANG C B. Remora optimization algorithm[J]. Expert Systems with Applications, 2021, 185(9): 15665.
[18] LIU Q X, LI N, JIA H M, et al. Modified remora optim-ization algorithm for global optimization and multi-level thresholding image segmentation[J]. Mathematics, 2022, 10: 1014.
[19] ZHENG R, JIA H M, ABUALIGAH L, et al. An improved remora optimization algorithm with autonomous foraging mechanism for global optimization problems[J]. Mathe-matical Biosciences and Engineering, 2022, 19(4): 3994-4037.
[20] ZHONG C, LI G, MENG Z. Beluga whale optimization: a novel nature-inspired metaheuristic algorithm[J]. Knowledge-Based Systems, 2022, 251: 109215.
[21] ARINI F Y, CHIEWCHANWATTANA S, SOOMLEK C, et al. Joint opposite selection (JOS): a premiere joint of selective leading opposition and dynamic opposite enhanced Harris?? hawks optimization for solving single-objective problems[J]. Expert Systems with Applications, 2022, 188: 116001.
[22] DHARGUPTA S, GHOSH M, MIRJALILI S, et al. Sele-ctive opposition based grey wolf optimization[J]. Expert Systems with Applications, 2020, 151: 113389.
[23] MANDAL B, ROY P K. Optimal reactive power dispatch using quasi-oppositional teaching learning based optimiz-ation[J]. International Journal of Electrical Power & Energy Systems, 2013, 53: 123-134.
[24] FAN Q, CHEN Z, XIA Z. A novel quasi-reflected Harris hawks optimization algorithm for global optimization prob-lems[J]. Soft Computing, 2020, 24(19): 14825-14843.
[25] ALSATTAR H A, ZAIDAN A A, ZAIDAN B B. Novel meta-heuristic bald eagle search optimisation algorithm[J]. Artificial Intelligence Review, 2020, 53(6): 2237-2264.
[26] ABUALIGAH L, ELAZIZ M A, SUMARI P, et al. Reptile search algorithm (RSA): a novel nature-inspired meta-heuristic optimizer[J]. Expert Systems with Applications, 2022, 191: 116158.
[27] BRAIK M, HAMMOURI A, ATWAN J, et al. White shark optimizer: a novel bio-inspired meta-heuristic algorithm for global optimization problems[J]. Knowledge-Based Systems, 2022, 243: 108457 .
[28] TIZHOOSH H R. Opposition-based learning: a new scheme for machine intelligence[C]//Proceedings of the 2005 Inter-national Conference on Computational Intelligence for Modelling Control and Automation, International Confe-rence on Intelligent Agents, Web Technologies and Internet Commerce, Vienna, Nov 28-30, 2005. Washington: IEEE Computer Society, 2005: 695-701.
[29] WANG S, HUSSIEN A G, JIA H, et al. Enhanced remora optimization algorithm for solving constrained engineering optimization problems[J]. Mathematics, 2022, 10(10): 1696.
[30] WANG S, RAO H, WEN C, et al. Improved remora optimization algorithm with mutualistic strategy for solving constrained engineering optimization problems[J]. Processes, 2022, 10(12): 2606.
[31] WU D, RAO H, WEN C, et al. Modified sand cat swarm optimization algorithm for solving constrained engine-ering optimization problems[J]. Mathematics, 2022, 10(22): 4350.
[32] FARNAD B, JAFARIAN A. A new nature-inspired hybrid algorithm with a penalty method to solve constrained problem[J]. International Journal of Computational Meth-ods, 2018, 15(8): 1850069.