Useful Papers and Source Codes for Meta Black-Box Optimization (MetaBBO)

This respository aims to maintain a list of useful relevant papers and open source codes for MetaBBO. Our implementations of some of these methods can be accessed in MetaBox.

• 1. Survey Papers & Benchmarks
  • 1.1. Survey Papers
  • 1.2. Benchmarks
• 2. MetaBBO
  • 2.1. MetaBBO-RL
    • 2.1.1. Algorithm Selection
    • 2.1.2. Algorithm Configuration
    • 2.1.3. Algorithm Generation & Parameter
    • 2.1.4. Algorithm Imitation
    • 2.1.5. Others
  • 2.2. MetaBBO-SL
    • 2.2.1. Algorithm Selection
    • 2.2.2. Algorithm Configuration
    • 2.2.3. Algorithm Generation & Parameter
    • 2.2.4. Algorithm Imitation
    • 2.2.5. Others
  • 2.3. MetaBBO-NE
    • 2.3.1. Algorithm Selection
    • 2.3.2. Algorithm Configuration
    • 2.3.3. Algorithm Generation & Parameter
    • 2.3.4. Algorithm Imitation
    • 2.3.5. Others
  • 2.4. MetaBBO-ICL
    • 2.4.1. Algorithm Selection
    • 2.4.2. Algorithm Configuration
    • 2.4.3. Algorithm Generation & Parameter
    • 2.4.4. Algorithm Imitation
    • 2.4.5. Others
  • 2.5. Others
    • 2.5.1 Evaluation Indicator
    • 2.5.2 Landscape Feature
• 3. Classic BBO
  • 3.1. Differential Evolution
  • 3.2. Partical Swarm Optimization
  • 3.3. Evolution Strategies
  • 3.4. Bayesian Optimization
  • 3.5. Others

1. Survey Papers & Benchmarks

1.1. Survey Papers

Paper
Li P, Hao J, Tang H, et al. "Bridging Evolutionary Algorithms and Reinforcement Learning: A Comprehensive Survey on Hybrid Algorithms. IEEE Transactions on Evolutionary Computation. (2024).
Song Y, Wu Y, Guo Y, et al. "Reinforcement learning-assisted evolutionary algorithm: A survey and research opportunities. Swarm and Evolutionary Computation. (2024).
Nikolikj, Ana, et al. "Quantifying Individual and Joint Module Impact in Modular Optimization Frameworks." 2024 IEEE Congress on Evolutionary Computation (CEC). (2024).
Qian, Chao, Ke Xue, and Ren-Jian Wang. "Quality-Diversity Algorithms Can Provably Be Helpful for Optimization." arXiv preprint arXiv:2401.10539. (2024).
Huang, Beichen, et al. "Exploring the True Potential: Evaluating the Black-box Optimization Capability of Large Language Models." arXiv preprint arXiv:2404.06290. (2024).
Chernigovskaya, Maria, Andrey Kharitonov, and Klaus Turowski. "A Recent Publications Survey on Reinforcement Learning for Selecting Parameters of Meta-Heuristic and Machine Learning Algorithms." CLOSER. (2023).
Drugan, Madalina M. "Reinforcement learning versus evolutionary computation: A survey on hybrid algorithms." Swarm and Evolutionary Computation. (2019).

1.2. Benchmarks

Benchmark	Paper	Original Repository	Optimization Type
GP-based	He Y, Aranha C. "Evolving Benchmark Functions to Compare Evolutionary Algorithms via Genetic Programming". arXiv preprint arXiv:2403.14146 (2024).	GP-based
SELECTOR	Benjamins, Carolin, et al. "Instance Selection for Dynamic Algorithm Configuration with Reinforcement Learning: Improving Generalization." arXiv preprint arXiv:2407.13513 (2024).	automl/instance-dac
MetaBox	Ma, Zeyuan, et al. "MetaBox: A Benchmark Platform for Meta-Black-Box Optimization with Reinforcement Learning." Advances in Neural Information Processing Systems 36 (2023).	GMC-DRL/MetaBox
NN-based	Prager R P, Dietrich K, Schneider L, et al. "Neural Networks as Black-Box Benchmark Functions Optimized for Exploratory Landscape Features" Proceedings of the 17th ACM/SIGEVO Conference on Foundations of Genetic Algorithms (2023).	-
NeuroEvoBench	Lange, Robert, Yujin Tang, and Yingtao Tian. "Neuroevobench: Benchmarking evolutionary optimizers for deep learning applications." Advances in Neural Information Processing Systems 36 (2023)	neuroevobench/neuroevobench
MA-BBOB	Vermetten, Diederick, et al. "MA-BBOB: A Problem Generator for Black-Box Optimization Using Affine Combinations and Shifts." arXiv preprint arXiv:2312.11083 (2023).	Dvermetten/Many-affine-BBOB
IEEE CEC 2022	Abhishek Kumar, Kenneth V. Price, Ali Wagdy Mohamed, Anas A. Hadi, P. N. Suganthan, "Problem definitions and evaluation criteria for the cec 2022 Special Session and Competition on Single Objective Bound Constrained Numerical Optimization." Technical Report 2022	P-N-Suganthan/2022-SO-BO
Affine Recombination	Dietrich K, Mersmann O. "Increasing the diversity of benchmark function sets through affine recombination" International Conference on Parallel Problem Solving from Nature. (2022).	-
IEEE CEC 2021	Ali Wagdy, Anas A Hadi, Ali K. Mohamed, Prachi Agrawal, Abhishek Kumar and P. N. Suganthan, "Problem definitions and evaluation criteria for the cec 2021 Special Session and Competition on Single Objective Bound Constrained Numerical Optimization." Technical Report 2021	P-N-Suganthan/2021-SO-BCO
Zigzag BBO	Kudela, Jakub. "Novel zigzag-based benchmark functions for bound constrained single objective optimization." 2021 IEEE Congress on Evolutionary Computation (CEC). IEEE, (2021). Kudela, Jakub, and Radomil Matousek. "New benchmark functions for single-objective optimization based on a zigzag pattern." IEEE Access 10 (2022).	JakubKudela89/Zigzag
HPOBench	Eggensperger, Katharina, et al. "HPOBench: A collection of reproducible multi-fidelity benchmark problems for HPO." arXiv preprint arXiv:2109.06716 (2021).	automl/HPOBench
DACBench	Eimer, Theresa, et al. "DACBench: A benchmark library for dynamic algorithm configuration." arXiv preprint arXiv:2105.08541 (2021).	automl/DACBench
Olympus	Häse, Florian, et al. "Olympus: a benchmarking framework for noisy optimization and experiment planning." Machine Learning: Science and Technology (2021).	aspuru-guzik-group/olympus
NeurIPS BBO challenge	Turner R, Eriksson D, McCourt M, et al. "Bayesian optimization is superior to random search for machine learning hyperparameter tuning: Analysis of the black-box optimization challenge 2020" NeurIPS 2020 Competition and Demonstration Track. (2021)	NeurIPS BBO challenge
Random function generator	Tian Y, Peng S, Zhang X, et al. "A recommender system for metaheuristic algorithms for continuous optimization based on deep recurrent neural networks". IEEE transactions on artificial intelligence (2020).	Random function generator
CEC 2020 competition on real-world optimization problem	Kumar A, Wu G, Ali M Z, et al. "A test-suite of non-convex constrained optimization problems from the real-world and some baseline results. Swarm and Evolutionary Computation (2020).	CEC 2020 real-world
COCO	Hansen, Nikolaus, et al. "COCO: A platform for comparing continuous optimizers in a black-box setting." Optimization Methods and Software (2021).	numbbo/coco
EVOBBO	Muñoz, Mario A., and Kate Smith-Miles. "Generating new space-filling test instances for continuous black-box optimization." Evolutionary computation (2020).	andremun/EVOBBO_Instances
Bayesmark	Turner R, Eriksson D. "Bayesmark: Benchmark framework to easily compare bayesian optimization methods on real machine learning tasks." (2019).	[Bayesmark](https://github. com/uber/bayesmark)
IOHprofiler (IOHexperimenter)	Doerr, Carola, et al. "IOHprofiler: A benchmarking and profiling tool for iterative optimization heuristics." arXiv preprint arXiv:1810.05281 (2018). de Nobel, Jacob, et al. "Iohexperimenter: Benchmarking platform for iterative optimization heuristics." Evolutionary Computation (2023): 1-6.	IOHprofiler/ IOHexperimenter
MTMOOP	Yuan Y, Ong Y S, Feng L, et al. "Evolutionary multitasking for multiobjective continuous optimization: Benchmark problems, performance metrics and baseline results." arXiv preprint arXiv:1706.02766 (2017).	-
MTSOP	Da B, Ong Y S, Feng L, et al. "Evolutionary multitasking for single-objective continuous optimization: Benchmark problems, performance metric, and baseline results". arXiv preprint arXiv:1706.03470 (2017).	-
IEEE CEC 2017	N. H. Awad, M. Z. Ali, J. J. Liang, B. Y. Qu and P. N. Suganthan, "Problem definitions and evaluation criteria for the CEC 2017 competition on constrained real-parameter optimization." Technical Report (2017)	P-N-Suganthan/CEC2017-BoundContrained
IEEE CEC 2015	J. J. Liang, B. Y. Qu, P. N. Suganthan, Q. Chen, "Problem Definitions and Evaluation Criteria for the CEC 2015 Competition on Learning-based Real-Parameter Single Objective Optimization", Technical Report, Computational Intelligence Laboratory (2015).	P-N-Suganthan/CEC2015-Learning-Based
AClib	Hutter, Frank, et al. "AClib: A benchmark library for algorithm configuration." Learning and Intelligent Optimization: 8th International Conference (2014).	aclib.net
IEEE CEC 2013	J. J. Liang, B-Y. Qu, P. N. Suganthan, Alfredo G. Hernández-Díaz, "Problem Definitions and Evaluation Criteria for the CEC 2013 Special Session and Competition on Real-Parameter Optimization", Technical Report, Computational Intelligence Laboratory (2013).	P-N-Suganthan/CEC2013
Protein–Docking	Hwang, Howook, et al. "Protein–protein docking benchmark version 4.0." Proteins: Structure, Function, and Bioinformatics (2010).	Protein–Docking
BBOB 2009	Hansen N, Finck S, Ros R, et al. "Real-parameter black-box optimization benchmarking 2009: Noiseless functions definitions". INRIA. (2009).	BBOB 2009
WFG	Huband S, Hingston P, Barone L, et al. "A review of multiobjective test problems and a scalable test problem toolkit." IEEE Transactions on Evolutionary Computation. (2006).	WFG
DTLZ	Deb K, Thiele L, Laumanns M, et al. "Scalable multi-objective optimization test problems." Proceedings of the 2002 Congress on Evolutionary Computation (2002).	DTLZ
ZDT	Zitzler, E., Deb, K., and Thiele, L. "Comparison of Multiobjective Evolutionary Algorithms: Empirical Results." Evolutionary Computation (2000).	ZDT

*The complete list of IEEE CEC series can be access at ntu.edu.sg.

*The complete list of BBOB series can be access at numbbo.

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2. MetaBBO

2.1. MetaBBO with Reinforcement Learning (MetaBBO-RL)

2.1.1. Operator Selection

Algorithm	Paper	Original Repository	About
MRL-MOEA	Wang, Jing, et al. "A Novel Multi-State Reinforcement Learning-Based Multi-Objective Evolutionary Algorithm." Information Sciences (2024): 121397.	-	PDF BibTex
RL-DAS	Guo, Hongshu, et al. "Deep Reinforcement Learning for Dynamic Algorithm Selection: A Proof-of-Principle Study on Differential Evolution." IEEE Transactions on Systems, Man, and Cybernetics: Systems (2024).	-	PDF BibTex
RLEMMO	Lian, Hongqiao, et al. "RLEMMO: Evolutionary Multimodal Optimization Assisted By Deep Reinforcement Learning." arXiv preprint arXiv:2404.08242 (2024).	-	PDF BibTex
SA-DQN-DE	Liao, Zuowen, Qishuo Pang, and Qiong Gu. "Differential evolution based on strategy adaptation and deep reinforcement learning for multimodal optimization problems." Swarm and Evolutionary Computation 87 (2024): 101568.	-	PDF BibTex
PG-DE & PG-MPEDE	Zhang, Haotian, et al. "Learning to select the recombination operator for derivative-free optimization." Science China Mathematics (2024): 1-24.	-	PDF BibTex
CEDE-DRL	Hu, Zhenzhen, et al. "Deep reinforcement learning assisted co-evolutionary differential evolution for constrained optimization." Swarm and Evolutionary Computation 83 (2023): 101387.	-	PDF BibTex
RLMMDE	Han, Yupeng, et al. "Multi-strategy multi-objective differential evolutionary algorithm with reinforcement learning." Knowledge-Based Systems 277 (2023): 110801.	-	PDF BibTex
RLDMDE	Yang, Qingyong, et al. "Dynamic multi-strategy integrated differential evolution algorithm based on reinforcement learning for optimization problems." Complex & Intelligent Systems (2023): 1-33.	-	PDF BibTex
MPSORL	Meng, Xiaoding, Hecheng Li, and Anshan Chen. "Multi-strategy self-learning particle swarm optimization algorithm based on reinforcement learning." Mathematical Biosciences and Engineering 20.5 (2023): 8498-8530.	-	PDF BibTex
MOEA/D-DQN	Tian, Ye, et al. "Deep reinforcement learning based adaptive operator selection for evolutionary multi-objective optimization." IEEE Transactions on Emerging Topics in Computational Intelligence (2022).	-	PDF BibTex
DE-DQN	Tan, Zhiping, and Kangshun Li. "Differential evolution with mixed mutation strategy based on deep reinforcement learning." Applied Soft Computing 111 (2021): 107678.	-	PDF BibTex
MARLwCMA	Sallam, Karam M., et al. "Evolutionary framework with reinforcement learning-based mutation adaptation." IEEE Access 8 (2020): 194045-194071.	-	PDF BibTex
DE-DDQN	Sharma, Mudita, et al. "Deep reinforcement learning based parameter control in differential evolution." Proceedings of the Genetic and Evolutionary Computation Conference. 2019.	mudita11/DE-DDQN	PDF BibTex
DE-RLFR	Li, Zhihui, et al. "Differential evolution based on reinforcement learning with fitness ranking for solving multimodal multiobjective problems." Swarm and Evolutionary Computation 49 (2019): 234-244.	-	PDF BibTex

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2.1.2. Parameter Contorl

Algorithm	Paper	Original Repository	About
GLEET	Ma, Zeyuan, et al. "Auto-configuring Exploration-Exploitation Tradeoff in Evolutionary Computation via Deep Reinforcement Learning." arXiv preprint arXiv:2404.08239 (2024).	-	PDF BibTex
RLMODE	Yu, Xiaobing, et al. "Reinforcement learning-based differential evolution algorithm for constrained multi-objective optimization problems." Engineering Applications of Artificial Intelligence 131 (2024): 107817.	-	PDF BibTex
RLNS	Hong, Jiale, Bo Shen, and Anqi Pan. "A reinforcement learning-based neighborhood search operator for multi-modal optimization and its applications." Expert Systems with Applications 246 (2024): 123150.	-	PDF BibTex
NRLPSO	Li, Wei, et al. "Reinforcement learning-based particle swarm optimization with neighborhood differential mutation strategy." Swarm and Evolutionary Computation 78 (2023): 101274.	-	PDF BibTex
MOEADRL	Gao, Mengqi, et al. "An efficient evolutionary algorithm based on deep reinforcement learning for large-scale sparse multiobjective optimization." Applied Intelligence 53.18 (2023): 21116-21139.	-	PDF BibTex
RLAM	Yin, Shiyuan, et al. "Reinforcement-learning-based parameter adaptation method for particle swarm optimization." Complex & Intelligent Systems 9.5 (2023): 5585-5609.	-	PDF BibTex
LDE	Sun, Jianyong, et al. "Learning Adaptive Differential Evolution Algorithm from Optimization Experiences by Policy Gradient." IEEE Transactions on Evolutionary Computation 25.4 (2021): 666-680.	yierh/LDE	PDF BibTex
RL-PSO	Wu, Di, and G. Gary Wang. "Employing reinforcement learning to enhance particle swarm optimization methods." Engineering Optimization 54.2 (2022): 329-348.	-	PDF BibTex
RLEPSO	Yin, Shiyuan, et al. "RLEPSO: Reinforcement learning based Ensemble particle swarm optimizer." Proceedings of the 2021 4th International Conference on Algorithms, Computing and Artificial Intelligence. 2021.	-	PDF BibTex
QLPSO	Xu, Yue, and Dechang Pi. "A reinforcement learning-based communication topology in particle swarm optimization." Neural Computing and Applications 32 (2020): 10007-10032.	-	PDF BibTex
QLSOPSO & QLMOPSO	Liu, Yaxian, et al. "An adaptive online parameter control algorithm for particle swarm optimization based on reinforcement learning." 2019 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2019.	-	PDF BibTex
RLMPSO	Samma, Hussein, Chee Peng Lim, and Junita Mohamad Saleh. "A new reinforcement learning-based memetic particle swarm optimizer." Applied Soft Computing 43 (2016): 276-297.	-	PDF BibTex

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2.1.3. Operator & Parameter

Algorithm	Paper	Original Repository	About
ALDes	Zhao, Qi, et al. "Automated Metaheuristic Algorithm Design with Autoregressive Learning." arXiv preprint arXiv:2405.03419 (2024).	-	PDF BibTex
MADAC	Xue, Ke, et al. "Multi-agent dynamic algorithm configuration." Advances in Neural Information Processing Systems 35 (2022): 20147-20161.	-	PDF BibTex
RL-HPSDE	Tan, Zhiping, et al. "Differential evolution with hybrid parameters and mutation strategies based on reinforcement learning." Swarm and Evolutionary Computation 75 (2022): 101194.	-	PDF BibTex

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2.1.4. Symbolic

Algorithm	Paper	Original Repository	About
SYMBOL	Chen, Jiacheng, et al. "Symbol: Generating Flexible Black-Box Optimizers through Symbolic Equation Learning." The Twelfth International Conference on Learning Representations. 2024.	GMC-DRL/Symbol	PDF BibTex

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2.1.5. Others

Algorithm	Paper	Original Repository	About
UES-CMA-ES	Bolufé-Röhler, Antonio, and Bowen Xu. "Deep reinforcement learning for smart restarts in exploration-only exploitation-only metaheuristic hybrids." Metaheuristics International Conference. Cham: Springer Nature Switzerland, 2024.	-	PDF BibTex
AGSEA	Shao, Shuai, Ye Tian, and Xingyi Zhang. "Deep reinforcement learning assisted automated guiding vector selection for large-scale sparse multi-objective optimization." Swarm and Evolutionary Computation 88 (2024): 101606.	-	PDF BibTex
MSORL	Wang, Xujie, et al. "A multi-swarm optimizer with a reinforcement learning mechanism for large-scale optimization." Swarm and Evolutionary Computation (2024): 101486.	-	PDF BibTex
MELBA	Chaybouti, Sofian, et al. "Meta-learning of Black-box Solvers Using Deep Reinforcement Learning." NeurIPS 2022, MetaLearn Workshop. 2022.	-	PDF BibTex
LTO-POMDP	Gomes, Hugo Siqueira, Benjamin Léger, and Christian Gagné. "Meta learning black-box population-based optimizers." arXiv preprint arXiv:2103.03526 (2021).	LTO-POMDP	PDF BibTex

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2.2. MetaBBO with Supervised Learning (MetaBBO-SL)

Algorithm	Paper	Original Repository	About
GLHF	Li, Xiaobin, et al. "GLHF: General Learned Evolutionary Algorithm Via Hyper Functions." arXiv preprint arXiv:2405.03728 (2024).	-	PDF BibTex
LEO	Yu, Peiyu, et al. "Latent Energy-Based Odyssey: Black-Box Optimization via Expanded Exploration in the Energy-Based Latent Space." arXiv preprint arXiv:2405.16730 (2024).	-	PDF BibTex
RIBBO	Song, Lei, et al. "Reinforced In-Context Black-Box Optimization." arXiv preprint arXiv:2402.17423 (2024).	songlei00/RIBBO	PDF BibTex
NAP	Maraval, Alexandre, et al. "End-to-end meta-Bayesian optimisation with transformer neural processes." Advances in Neural Information Processing Systems 36 (2024).	-	PDF BibTex
OptFormer	Chen, Yutian, et al. "Towards learning universal hyperparameter optimizers with transformers." Advances in Neural Information Processing Systems 35 (2022): 32053-32068.	google-research/optformer	PDF BibTex
RNN-Opt	TV, Vishnu, et al. "Meta-learning for black-box optimization." Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Cham: Springer International Publishing, 2019.	-	PDF BibTex
RNN-OI	Chen, Yutian, et al. "Learning to learn without gradient descent by gradient descent." International Conference on Machine Learning. PMLR, 2017.	-	PDF BibTex

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2.3. MetaBBO with Neuroevolution (MetaBBO-NE)

Algorithm	Paper	Original Repository	About
EvoTF	Lange, Robert Tjarko, Yingtao Tian, and Yujin Tang. "Evolution Transformer: In-Context Evolutionary Optimization." arXiv preprint arXiv:2403.02985 (2024).	RobertTLange/evosax	PDF BibTex
LES	Lange, Robert, et al. "Discovering evolution strategies via meta-black-box optimization." The Eleventh International Conference on Learning Representations. 2023.	-	PDF BibTex
LGA	Lange, Robert, et al. "Discovering attention-based genetic algorithms via meta-black-box optimization." Proceedings of the Genetic and Evolutionary Computation Conference. 2023.	-	PDF BibTex

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2.4. MetaBBO with LLMs

Algorithm	Paper	Original Repository	About
AS-LLM	Wu, Xingyu, et al. "Large language model-enhanced algorithm selection: towards comprehensive algorithm representation." International Joint Conference on Artificial Intelligence, 2024.	-	PDF BibTex
LLMOPT	Huang, Yuxiao, et al. "Towards Next Era of Multi-objective Optimization: Large Language Models as Architects of Evolutionary Operators." arXiv preprint arXiv:2406.08987 (2024).	-	PDF BibTex
LLaMoCo	Ma, Zeyuan, et al. "LLaMoCo: Instruction Tuning of Large Language Models for Optimization Code Generation." arXiv preprint arXiv:2403.01131 (2024).	LLaMoCo-722A	PDF BibTex
LLaMEA	van Stein, Niki, and Thomas Bäck. "LLaMEA: A Large Language Model Evolutionary Algorithm for Automatically Generating Metaheuristics." arXiv preprint arXiv:2405.20132 (2024).	-	PDF BibTex
EvoLLM	Lange, Robert Tjarko, Yingtao Tian, and Yujin Tang. "Large Language Models As Evolution Strategies." arXiv preprint arXiv:2402.18381 (2024).	-	PDF BibTex
CMOEA-LLM	Wang, Zeyi, et al. "Large Language Model-Aided Evolutionary Search for Constrained Multiobjective Optimization." arXiv preprint arXiv:2405.05767 (2024).	-	PDF BibTex
LEO	Brahmachary, Shuvayan, et al. "Large Language Model-Based Evolutionary Optimizer: Reasoning with elitism." arXiv preprint arXiv:2403.02054 (2024).	-	PDF BibTex
EvoPrompt	Guo, Qingyan, et al. "Connecting large language models with evolutionary algorithms yields powerful prompt optimizers." The Twelfth International Conference on Learning Representations (2024).	beeevita/EvoPrompt	PDF BibTex
Evoprompting	Chen, Angelica, David Dohan, and David So. "Evoprompting: Language models for code-level neural architecture search." Advances in Neural Information Processing Systems 36 (2024).	-	PDF BibTex
Pluhacek, Michal, et al	Pluhacek, Michal, et al. "Leveraging large language models for the generation of novel metaheuristic optimization algorithms." Proceedings of the Companion Conference on Genetic and Evolutionary Computation. 2023.	-	PDF BibTex
LMEA	Liu, Shengcai, et al. "Large language models as evolutionary optimizers." arXiv preprint arXiv:2310.19046 (2023).	-	PDF BibTex
AEL	Liu, Fei, et al. "Algorithm evolution using large language model." arXiv preprint arXiv:2311.15249 (2023).	-	PDF BibTex
OPRO	Yang, Chengrun, et al. "Large language models as optimizers." arXiv preprint arXiv:2309.03409 (2023).	-	PDF BibTex
Guo, Pei-Fu, et al	Guo, Pei-Fu, et al. "Towards optimizing with large language models." arXiv preprint arXiv:2310.05204 (2023).	-	PDF BibTex
OptiMUS	AhmadiTeshnizi, Ali, Wenzhi Gao, and Madeleine Udell. "OptiMUS: Optimization Modeling Using mip Solvers and large language models." arXiv preprint arXiv:2310.06116 (2023).	teshnizi/OptiMUS	PDF BibTex
MOEA/D-LLM	Liu, Fei, et al. "Large language model for multi-objective evolutionary optimization." arXiv preprint arXiv:2310.12541 (2023).	-	PDF BibTex
EoH	Liu, Fei, et al. "Evolution of Heuristics: Towards Efficient Automatic Algorithm Design Using Large Language Model." arXiv preprint arXiv:2309.03409 (2023).	nobodynobodypaper/EoH	PDF BibTex
Zhang, Michael R., et al	Zhang, Michael R., et al. "Using Large Language Models for Hyperparameter Optimization." NeurIPS 2023 Foundation Models for Decision Making Workshop. 2023.	-	PDF BibTex

See also FeiLiu36/LLM4Opt and jxzhangjhu/Awesome-LLM-Prompt-Optimization.

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2.5. Others

2.5.1 Evaluation Indicator

2.5.2 Landscape Feature

3. Classic BBO

3.1. Differential Evolution

Algorithm	Paper	Original Repository	About
ModDE	Vermetten, Diederick, et al. "Modular Differential Evolution." arXiv preprint arXiv:2304.09524 (2023).	Dvermetten/ModDE	PDF BibTex
AMCDE	Ye, Chenxi, et al. "Differential evolution with alternation between steady monopoly and transient competition of mutation strategies." Swarm and Evolutionary Computation 83 (2023): 101403.	-	PDF BibTex
NL-SHADE-LBC	Stanovov, Vladimir, Akhmedova, Shakhnaz and Semenkin, Eugene "NL-SHADE-LBC algorithm with linear parameter adaptation bias change for CEC 2022 Numerical Optimization." 2022 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2022.	-	PDF BibTex
MadDE	Biswas, Subhodip, et al. "Improving differential evolution through Bayesian hyperparameter optimization." 2021 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2021.	subhodipbiswas/ MadDE	PDF BibTex
jDE21	Brest, Janez, Mirjam Sepesy Maučec, and Borko Bošković. "Self-adaptive differential evolution algorithm with population size reduction for single objective bound-constrained optimization: Algorithm j21." 2021 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2021.	-	PDF BibTex
NL-SHADE-RSP	Stanovov, Vladimir, Shakhnaz Akhmedova, and Eugene Semenkin. "NL-SHADE-RSP algorithm with adaptive archive and selective pressure for CEC 2021 numerical optimization." 2021 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2021.	-	PDF BibTex
EDEV	Wu, Guohua, et al. "Ensemble of differential evolution variants." Information Sciences 423 (2018): 172-186.	-	PDF BibTex
HMJCDE	Li, Genghui, et al. "A novel hybrid differential evolution algorithm with modified CoDE and JADE." Applied Soft Computing 47 (2016): 577-599.	-	PDF BibTex
L-SHADE	Tanabe, Ryoji, and Alex S. Fukunaga. "Improving the search performance of SHADE using linear population size reduction." 2014 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2014.	-	PDF BibTex
SHADE	Tanabe, Ryoji, and Alex Fukunaga. "Success-history based parameter adaptation for differential evolution." 2013 IEEE Congress on Evolutionary Computation. IEEE, 2013.	-	PDF BibTex
CoDE	Wang, Yong, Zixing Cai, and Qingfu Zhang. "Differential evolution with composite trial vector generation strategies and control parameters." IEEE Transactions on Evolutionary Computation 15.1 (2011): 55-66.	-	PDF BibTex
EPSDE	Mallipeddi, Rammohan, et al. "Differential evolution algorithm with ensemble of parameters and mutation strategies." Applied Soft Computing 11.2 (2011): 1679-1696.	-	PDF BibTex
rJADE	Peng, Fei, et al. "Multi-start JADE with knowledge transfer for numerical optimization." 2009 IEEE Congress on Evolutionary Computation. IEEE, 2009.	-	PDF BibTex
JADE	Zhang, Jingqiao, and Arthur C. Sanderson. "JADE: adaptive differential evolution with optional external archive." IEEE Transactions on Evolutionary Computation 13.5 (2009): 945-958.	-	PDF BibTex
jDE	Brest, Janez, et al. "Self-adapting control parameters in differential evolution: A comparative study on numerical benchmark problems." IEEE Transactions on Evolutionary Computation 10.6 (2006): 646-657.	-	PDF BibTex
SaDE	Qin, A. Kai, and Ponnuthurai N. Suganthan. "Self-adaptive differential evolution algorithm for numerical optimization." 2005 IEEE Congress on Evolutionary Computation (CEC). Vol. 2. IEEE, 2005.	-	PDF BibTex
Vanilla DE	Storn, Rainer, and Kenneth Price. "Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces." Journal of Global Optimization 11.4 (1997): 341.	-	PDF BibTex

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3.2. Partical Swarm Optimization

Algorithm	Paper	Original Repository	About
SAHLPSO	Tao, Xinmin, et al. "Self-Adaptive two roles hybrid learning strategies-based particle swarm optimization." Information Sciences 578 (2021): 457-481.	-	PDF BibTex
EPSO	Lynn, Nandar, and Ponnuthurai Nagaratnam Suganthan. "Ensemble particle swarm optimizer." Applied Soft Computing 55 (2017): 533-548.	-	PDF BibTex
GLPSO	Yue-Jiao Gong, Jing-Jing Li, Yicong Zhou, Yun Li, Henry Shu-Hung Chung, Yu-hui Shi, Jun Zhang. "Genetic learning particle swarm optimization." IEEE Transactions on Cybernetics 46.10 (2015): 2277-2290.	YuejiaoGong/ genetic_learning_PSO	PDF BibTex
sDMS-PSO	Liang, Jing J., et al. "A self-adaptive dynamic particle swarm optimizer." 2015 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2015.	-	PDF BibTex
DMS-PSO	Liang, Jane-Jing, and Ponnuthurai Nagaratnam Suganthan. "Dynamic multi-swarm particle swarm optimizer." Proceedings 2005 IEEE Swarm Intelligence Symposium, 2005. SIS 2005.. IEEE, 2005.	-	PDF BibTex
FIPSO	Mendes, Rui, James Kennedy, and José Neves. "The fully informed particle swarm: simpler, maybe better." IEEE Transactions on Evolutionary Computation 8.3 (2004): 204-210.	-	PDF BibTex
Vanilla PSO	Kennedy, James, and Russell Eberhart. "Particle swarm optimization." Proceedings of ICNN'95-International Conference on Neural Networks. Vol. 4. IEEE, 1995.	-	PDF BibTex

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3.3. Evolution Strategies

Algorithm	Paper	Original Repository	About
PSA-CMA-ES	Nishida, Kouhei, and Youhei Akimoto. "Psa-cma-es: Cma-es with population size adaptation." Proceedings of the Genetic and Evolutionary Computation Conference. 2018.	-	PDF BibTex
CC-CMA-ES	Liu, Jinpeng, and Ke Tang. "Scaling up covariance matrix adaptation evolution strategy using cooperative coevolution." International Conference on Intelligent Data Engineering and Automated Learning. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.	-	PDF BibTex
BIPOP-CMA-ES	Hansen, Nikolaus. "Benchmarking a BI-population CMA-ES on the BBOB-2009 function testbed." Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: late breaking papers. 2009.	-	PDF BibTex
IPOP-CMA-ES	Auger, Anne, and Nikolaus Hansen. ""A restart CMA evolution strategy with increasing population size." 2005 IEEE Congress on Evolutionary Computation (CEC). Vol. 2. IEEE, 2005.	-	PDF BibTex
CMA-ES	Hansen, Nikolaus, Sibylle D. Müller, and Petros Koumoutsakos. "Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (CMA-ES)." Evolutionary Computation 11.1 (2003): 1-18.	-	PDF BibTex

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3.4. Bayesian Optimization

Algorithm	Paper	Original Repository	About
BO	Snoek, Jasper, Hugo Larochelle, and Ryan P. Adams. "Practical bayesian optimization of machine learning algorithms." Advances in Neural Information Processing Systems 25 (2012).	-	PDF BibTex
SMAC3	Lindauer, Marius, et al. "SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization." The Journal of Machine Learning Research 23.1 (2022): 2475-2483.	automl/SMAC3	PDF BibTex

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3.5. Others

Algorithm	Paper	Original Repository	About
MFEA(-II)	Gupta, Abhishek, Yew-Soon Ong, and Liang Feng. "Multifactorial evolution: toward evolutionary multitasking." IEEE Transactions on Evolutionary Computation 20.3 (2015): 343-357. Bali, Kavitesh Kumar, et al. "Multifactorial evolutionary algorithm with online transfer parameter estimation: MFEA-II." IEEE Transactions on Evolutionary Computation 24.1 (2019): 69-83.	-	PDF BibTex
MOEA/D	Zhang, Qingfu, and Hui Li. "MOEA/D: A multiobjective evolutionary algorithm based on decomposition." IEEE Transactions on Evolutionary Computation 11.6 (2007): 712-731.	-	PDF BibTex
VNCDE	Zhang, Yu-Hui, et al. "Parameter-free voronoi neighborhood for evolutionary multimodal optimization." IEEE Transactions on Evolutionary Computation 24.2 (2019): 335-349.	-	PDF BibTex

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