Multitasking Optimization

Posted:   October 05, 2022

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A Collection of Main Papers on Multitasking Optimization

Survey

Paper Title Venue Year Authors Materials Comment
1. Back to the Roots Multi-X Evolutionary Computation Cognitive Computation 2019 Gupta et al. [paper]  
2. Evolutionary Multitask Optimization a Methodological Overview Challenges and Future Research Directions arXiv 2021 Osaba et al. [paper]  
3. Multi-Task Optimization and Multi-Task Evolutionary Computation in the Past Five Years A Brief Review Mathematics 2021 Xu et al. [paper]  
4. Evolutionary Transfer Optimization A New Frontier in Evolutionary Computation Research CIM 2021 Tan et al. [paper]  
5. Half a Dozen Real-World Applications of Evolutionary Multitasking and More CIM 2022 Gupta et al. [paper]  
6. A Review on Evolutionary Multi-Task Optimization: Trends and Challenges TEVC 2022 Wei et al._ [paper]  

Multitasking Single-objective Optimization

Paper Title Venue Year Authors Materials Comment
1. Multifactorial Evolution Toward Evolutionary Multitasking TEVC 2016 Gupta et al. [paper] [code] [blog]
2. Evolutionary Multitasking via Explicit autoencoding TCYB 2018 Feng st al. [paper] [code] [blog]
3. A Group-based Approach to Improve Multifactorial Evolutionary Algorithm IJCAI 2018 Tang st al. [paper]  
4. Self-regulated Evolutionary Multi-task Optimization TEVC 2019 Zheng st al. [paper] [code] [blog]
5. MFEA-II Multifactorial Evolutionary Algorithm with Online Transfer Parameter Estimation MFEA-II TEVC 2019 Bali st al. [paper] [code] [blog]
6. An Adaptive Archive-Based Evolutionary Framework for Many-Task Optimization TETCI 2019 Chen st al. [paper] [code]  
7. Evolutionary Manytasking Optimization Based on Symbiosis in Biocoenosis AAAI 2019 Liaw st al. [paper] [blog]
8. Regularized Evolutionary Multi-Task Optimization Learning to Inter-Task Transfer in Aligned Subspace TEVC 2020 Tang st al. [paper]  
9. Solving Multi-task Optimization Problems with Adaptive Knowledge Transfer via Anomaly Detection TEVC 2021 Wang st al. [paper] [code]  
10. Evolutionary Multi-task Optimization with Adaptive Knowledge Transfer TEVC 2021 Xu st al. [paper]  
11. Evolutionary Many-task Optimization Based on Multi-source Knowledge Transfer TEVC 2021 Liang st al. [paper]  
12. Multi-Task Shape Optimization Using a 3D Point Cloud Autoencoder as Unified Representation TEVC 2021 Rios st al. [paper]  
13. Towards Large-Scale Evolutionary Multi-Tasking: A GPU-Based Paradigm TEVC 2021 Huang st al. [paper]  
14. Improving Evolutionary Multitasking Optimization by Leveraging Inter-Task Gene Similarity and Mirror Transformation CIM 2021 Ma st al. [paper]  
15. A Bi-objective Knowledge Transfer Framework for Evolutionary Many-Task Optimization TEVC 2022 Jiang st al. [paper]  
16. An Effective Knowledge Transfer Method Based on Semi-supervised Learning for Evolutionary Optimization IS 2022 Gao st al. [paper]  
17. Orthogonal Transfer for Multitask Optimization TEVC 2022 Wu st al. [paper]  

Multitasking Multi-objective Optimization

Paper Title Venue Year Authors Materials Comment
1. Multiobjective Multifactorial Optimization in Evolutionary Multitasking TCYB 2017 Gupta et al. [paper] [code]  
2. Evolutionary Multitasking via Explicit autoencoding TCYB 2018 Feng et al. [paper] [code]  
3. Multiobjective Multitasking OptimizationBased on Incremental Learning TEVC 2020 Lin et al. [paper]  
4. An Effective Knowledge Transfer Approach for Multiobjective Multitasking Optimization TCYB 2020 Lin st al. [paper] [code]  
5. Evolutionary Multitasking for Multiobjective Optimization With Subspace Alignmentand Adaptive Differential Evolution TCYB 2020 Liang st al. [paper] [code]  
6. Cognizant Multitasking in Multiobjective Multifactorial Evolution MO-MFEA-II TCYB 2020 Bali et al. [paper] [code]  
7. Multiobjective Multitasking Optimization Based on Decomposition with Dual Neighborhoods arXiv 2021 Wang et al. [paper]  
8. Towards Generalized Resource Allocation on Evolutionary Multitasking for Multi-Objective Optimization CIM 2021 Wei et al. [paper]  
9. Evolutionary Multitasking for Multi-objective Optimization Based on Generative Strategies TEVC 2022 Liang et al. [paper]  
10. Dynamic Auxiliary Task-Based Evolutionary Multitasking for Constrained Multi-objective Optimization TEVC 2022 Qiao et al. [paper]  
11. Multiobjective Multitask Optimization -Neighborhood as a Bridge for Knowledge Transfer TEVC 2022 Wang et al. [paper]  
12. A Multi-objective Multitask Optimization Algorithm Using Transfer Rank TEVC 2022 Chen et al. [paper]  
13. A Multiform Optimization Framework for Constrained Multiobjective Optimization TCYB 2022 Jiao et al. [paper]  
14. Multiobjective Multitasking Optimization With Subspace Distribution Alignment and Decision Variable Transfer TETCI 2022 Gao et al. [paper]  

Multitasking Combination Optimization

Paper Title Venue Year Authors Materials Comment
1. Evolutionary Multitasking in Permutation-Based Combinatorial Optimization Problems Realization with TSP QAP LOP and JSP TENCON 2016 Yuan et al. [paper]  
2. Evolutionary Multitasking in Combinatorial Search Spaces A Case Study in Capacitated Vehicle Routing Problem SSCI 2016 Zhou et al. [paper]  
3. Solving Generalized Vehicle Routing Problem With Occasional Drivers via Evolutionary Multitasking TCYB 2019 Feng st al. [paper] [code]  
4. Explicit Evolutionary Multitasking for Combinatorial Optimization A Case Study on Capacitated Vehicle Routing Problem TCYB 2020 Feng st al. [paper] [code]  
5. A Unified Framework of Graph-based Evolutionary Multitasking Hyper-heuristic TEVC 2021 Hao et al. [paper]  
6. Many-Objective Job-Shop Scheduling: A Multiple Populations for Multiple Objectives-Based Genetic Algorithm Approach TCYB 2022 Liu et al. [paper]  
7. Transfer Learning Assisted Batch Optimization of Jobs Arriving Dynamically in Manufacturing Cloud JOMS 2022 Zhou et al. [paper]  
8. Task Relatedness Based Multitask Genetic Programming for Dynamic Flexible Job Shop Scheduling TEVC 2022 Zhang et al. [paper]  
9. Knowledge Transfer Genetic Programming with Auxiliary Population for Solving Uncertain Capacitated Arc Routing Problem TEVC 2022 Ardeh et al. [paper]  
10. Multitask Multiobjective Genetic Programming for Automated Scheduling Heuristic Learning in Dynamic Flexible Job-Shop Scheduling TCYB 2022 Zhang et al. [paper]  

Multitasking High-dimensional Optimization

Paper Title Venue Year Authors Materials Comment
1. Large Scale optimization via Evolutionary Multitasking assisted Random Embedding CEC 2020 Feng et al. [paper]  
2. A Multi-Variation Multifactorial Evolutionary Algorithm for Large-Scale Multi-Objective Optimization TEVC 2021 Feng et al. [paper]  
3. Evolutionary Multitasking for Large-Scale Multiobjective Optimization TEVC 2022 Liu et al. [paper]  

Multitasking Data-Driven Evolutionary Optimization

Paper Title Venue Year Authors Materials Comment
1. Generalized Multi-tasking for Evolutionary Optimization of Expensive Problems TEVC 2017 Ding et al. [paper] [code]  
2. Novel Multitask Conditional Neural-Network Surrogate Models for Expensive Optimization TCYB 2020 Luo et al. [paper]  
3. Evolutionary Multitasking for Expensive Minimax Optimization in Multiple Scenarios CIM 2021 Wang et al. [paper] [code]  

Multitasking Genetic Programming and Swarm Intelligence

Paper Title Venue Year Authors Materials Comment
1. Learning Ensemble of Decision Trees through Multifactorial Genetic Programming CEC 2016 Wen et al. [paper]  
2. Multifactorial Genetic Programming for Symbolic Regression Problems TSMC 2018 Zhong et al. [paper]  
3. Self-Adjusting Multi-Task Particle Swarm Optimization TEVC 2021 Han et al. [paper]  
4. Self-adaptive Multi-task Particle Swarm Optimization arxiv 2021 Zheng et al. [paper]  
5. Multi-Task Particle Swarm Optimization with Dynamic On-Demand Allocation TEVC 2022 Han et al. [paper]  
6. Multi-Task Particle Swarm Optimization With Dynamic Neighbor and Level-Based Inter-Task Learning TETCI 2022 Tang et al. [paper]  

Multitasking Optimization in Complex Networks

Paper Title Venue Year Authors Materials Comment
1. Evolutionary Multitasking Sparse Reconstruction Framework and Case Study TEVC 2018 Li et al. [paper]  
2. MUMI Multitask Module Identification for Biological Networks TEVC 2020 Chen et al. [paper] [code]  
3. Evolutionary Multitasking Network Reconstruction from Time Series with Online Parameter Estimation KBS 2021 Shen et al. [paper]  
4. Learning Large-Scale Fuzzy Cognitive Maps Using an Evolutionary Many-Task Algorithm ASOC 2021 Wang et al. [paper]  
5. Evolutionary Multitasking Multilayer Network Reconstruction TCYB 2021 Wang et al. [paper] [code]  
6. Community detection in multiplex networks based on evolutionary multi-task optimization and evolutionary clustering ensemble TEVC 2022 Lyu et al. [paper]  

Multitasking Optimization in Machine Learning

Paper Title Venue Year Authors Materials Comment
1. Multi-task Bayesian Optimization NIPS 2012 Swersky et al. [paper]  
2. Co-evolutionary Multi-Task Learning for Dynamic Time Series Prediction ASOC 2018 Chandra et al. [paper] [code]  
3. Adaptive Multi-factorial Evolutionary Optimization for Multi-task Reinforcement Learning TEVC 2021 Martinez et al. [paper] [code]  
4. Can Transfer Neuroevolution Tractably Solve Your Differential Equations arXiv 2021 Huang et al. [paper]  
5.Simultaneously Evolving Deep Reinforcement Learning Models using Multifactorial Optimization arXiv 2020 Martinez et al. [paper] [code]  
6.Evolutionary Multitasking for Feature Selection in High-dimensional Classification via Particle Swarm Optimisation TEVC 2021 Chen et al. [paper]  
7.Evolutionary Machine Learning with Minions: A Case Study in Feature Selection TEVC 2021 Zhang et al. [paper]  
8.Learning and Sharing: A Multitask Genetic Programming Approach to Image Feature Learning TEVC 2021 Bi et al. [paper]  

Transfer Optimization

Paper Title Venue Year Authors Materials Comment
1. Insights on Transfer Optimization Because Experience is the Best Teacher TETCI 2018 Gupta et al. [paper]  
2. Curbing Negative Influences Online for Seamless Transfer Evolutionary Optimization TCYB 2019 Da et al. [paper] [code]  
3. Warm Starting CMA-ES for Hyperparameter Optimization AAAI 2020 Nomura et al. [paper]  
4. Generalizing Transfer Bayesian Optimization to Source-Target Heterogeneity TASE 2020 Min et al. [paper]  
5. Scalable Transfer Evolutionary Optimization Coping with Big Task Instances arXiv 2020 Shakeri et al. [paper] [code]  
6. Transfer Stacking from Low-to High-Fidelity A Surrogate-Assisted Bi-Fidelity Evolutionary Algorithm ASOC 2020 Wang et al. [paper] [code]  
7. Transfer Learning Based Co-surrogate Assisted Evolutionary Bi-objective Optimization for Objectives with Non-uniform Evaluation Times arxiv 2021 Wang et al. [paper]  
8. Evolutionary Sequential Transfer Optimization for Objective-Heterogeneous Problems TEVC 2022 Xue st al. [paper]  
9. Transfer Learning Based Co-Surrogate Assisted Evolutionary Bi-Objective Optimization for Objectives with Non-Uniform Evaluation Times EC 2022 Wang st al. [paper]  

Theoretical Analysis

Paper Title Venue Year Authors Materials Comment
1. Improve Theoretical Upper Bound of Jumpk Function by Evolutionary Multitasking HPCCT 2019 Lian et al. [paper]  
2. Analysis on the Efficiency of Multifactorial Evolutionary Algorithms PPSN 2020 Huang et al. [paper]  
3. From Multi-Task Gradient Descent to Gradient-Free Evolutionary Multitasking A Proof of Faster Convergence TCYB 2021 Bai et al. [paper]  

Other Applications

Paper Title Venue Year Authors Materials Comment
1. Evolutionary Multitasking In Bi-Level Optimization Complex & Intelligent Systems 2015 Gupta et al. [paper]  
2. An Evolutionary Multitasking Algorithm for Cloud Computing Service Composition World Congress on Services 2018 Bao st al. [paper]  
3. Multi-Tasking Genetic Algorithm (MTGA) TFS 2019 Wu et al. [paper] [blog]
4. A Multitasking Electric Power Dispatch Approach With Multi-Objective Multifactorial Optimization Algorithm Access 2020 Liu et al. [paper]  
5. Solving Dynamic Multiobjective Problem via Autoencoding Evolutionary Search TCYB 2020 Liang et al. [paper] [code]  
6. A Multi-Task Bee Colony Band Selection Algorithm with Variable-size Clustering for Hyperspectral Images TEVC 2022 He et al. [paper]  
7. Predicting Demands of COVID-19 Prevention and Control Materials via Co-Evolutionary Transfer Learning TCYB 2022 Song et al. [paper]  
8. Multi-View Point Cloud Registration Based on Evolutionary Multitasking With Bi-Channel Knowledge Sharing Mechanism TETCI 2022 Wu et al. [paper]  

Datasets

Paper Title Venue Year Authors Materials Comment
1. Evolutionary Multitasking for Single-objective Continuous Optimization Benchmark Problems Performance Metric and Baseline Results arXiv 2017 Da et al. [paper] [code]  
2. Evolutionary Multitasking for Multiobjective Continuous Optimization Benchmark Problems Performance Metrics and Baseline Results arXiv 2017 Yuan et al. [paper] [code]  
3. Evolutionary Multitasking Optimization for Complex Problems CEC, GECCO 2017~2021 Feng et al. [code]  
4. Evolutionary Many-tasking Optimization CEC, GECCO 2017~2021 Feng et al. [code]  
5. Evolutionary Transfer Optimization CEC 2021 Tan et al. [paper] [code]  

Tools

Name Authors/Organizations Materials Comment
1. Deap Fortin et al. [paper] [code] DEAP is a novel evolutionary computation framework for rapid prototyping and testing of ideas. It seeks to make algorithms explicit and data structures transparent. It works in perfect harmony with parallelisation mechanisms such as multiprocessing and SCOOP.
2. Geatpy2 South China Agricultural University et al. [homepage] [code] Capability of solving single-objective, multi-objectives, many-objectives and combinatorial optimization problems fast. A huge number of operators with high performance of evolutionary algorithms (selection, recombination, mutation, migration…). Support numerous encodings for the chromosome of the population. Many evolutionary algorithm templates, including GA, DE, ES for single/multi-objective(s) evolution. Multiple population evolution. Support polysomy evolution. Parallelization and distribution of evaluations. Testbeds containing most common benchmarks functions. Support tracking analysis of the evolution iteration. Many evaluation metrics of algorithms.
3. Inspyred Garrett et al. [homepage] [code] Inspyred is a free, open source framework for creating biologically-inspired computational intelligence algorithms in Python, including evolutionary computation, swarm intelligence, and immunocomputing. Additionally, inspyred provides easy-to-use canonical versions of many bio-inspired algorithms for users who do not need much customization.
4. PlatEMO Tian et al. [paper] [code] Developed by BIMK (Institute of Bioinspired Intelligence and Mining Knowledge) of Anhui University and NICE (Nature Inspired Computing and Engineering Group) of University of Surrey. 150+ open source evolutionary algorithms, 300+ open source benchmark problems, Powerful GUI for performing experiments in parallel, Generating results in the format of Excel or LaTeX table by one-click operation, State-of-the-art algorithms will be included continuously.
5. EvoGrad Uber AI Labs [code] EvoGrad is a lightweight tool for differentiating through expectation, built on top of PyTorch. EvoGrad enables fast prototyping of NES-like algorithms. We believe there are many interesting algorithms yet to be discovered in this vein, and we hope this library will help to catalyze progress in the machine learning community.

Disclaimer

If you have any questions, please feel free to contact us. Emails: xiaofengxd@126.com

Authors of scientific papers including results generated using MTEA-AD or EM2MNR are encouraged to cite the following paper:

@ARTICLE{9489377, author={Wu, Kai and Wang, Chao and Liu, Jing}, journal={IEEE Transactions on Cybernetics}, title={Evolutionary Multitasking Multilayer Network Reconstruction}, year={2021}, volume={}, number={}, pages={1-15}, doi={10.1109/TCYB.2021.3090769}}

@ARTICLE{9385398, author={Wang, Chao and Liu, Jing and Wu, Kai and Wu, Zhaoyang}, journal={IEEE Transactions on Evolutionary Computation}, title={Solving Multi-task Optimization Problems with Adaptive Knowledge Transfer via Anomaly Detection}, year={2021}, volume={}, number={}, pages={1-1}, doi={10.1109/TEVC.2021.3068157}}

@article{WANG2021107441, title = {Learning large-scale fuzzy cognitive maps using an evolutionary many-task algorithm}, journal = {Applied Soft Computing}, volume = {108}, pages = {107441}, year = {2021}, issn = {1568-4946}, doi = {https://doi.org/10.1016/j.asoc.2021.107441}, url = {https://www.sciencedirect.com/science/article/pii/S1568494621003641}, author = {Chao Wang and Jing Liu and Kai Wu and Chaolong Ying}}

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