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- rapid-communicationJuly 2024
Parameter tuning of modified adaptive backstepping controller for strict-feedback nonlinear systems
Automatica (Journal of IFAC) (AJIF), Volume 166, Issue CAug 2024https://doi.org/10.1016/j.automatica.2024.111726AbstractMost research on control of nonlinear systems using backstepping concentrates on construction of feedback controllers to achieve stability and/or steady-state tracking performance. Little research considers systematic methods for tuning nonlinear ...
- ArticleJuly 2024
Parameter Tuning of the Firefly Algorithm by Standard Monte Carlo and Quasi-Monte Carlo Methods
Computational Science – ICCS 2024Jul 2024, Pages 242–253https://doi.org/10.1007/978-3-031-63775-9_17AbstractAlmost all optimization algorithms have algorithm-dependent parameters, and the setting of such parameter values can significantly influence the behavior of the algorithm under consideration. Thus, proper parameter tuning should be carried out to ...
- research-articleJuly 2024
Simulated experiments on diesel engine speed control based on LSTM-PID control
CNIOT '24: Proceedings of the 2024 5th International Conference on Computing, Networks and Internet of ThingsMay 2024, Pages 241–245https://doi.org/10.1145/3670105.3670145The electronic speed regulator, as a key component of the diesel engine, governs the engine's speed, which is crucial for its operational performance. In response to the issue of PID parameter tuning for speed control under different operating conditions,...
- research-articleJune 2024
Analytical parameter tuning for a class of extended disturbance observers and sliding mode control
Applied Mathematics and Computation (APMC), Volume 469, Issue CMay 2024https://doi.org/10.1016/j.amc.2023.128527AbstractThis paper studies the parameter tuning problem for a class of extended disturbance observers (EDO) and the EDO-based sliding mode control (SMC). Based on a H 2 optimization technique, analytical solutions for the parameter tuning of the EDO and ...
Highlights- Parameter tuning for an extended disturbance observer and sliding mode control is studied.
- Analytical solutions are derived based on a H2 optimization method.
- Conditions for higher-order EDOs being better than the lower-order ones ...
- research-articleApril 2024
Learning to Branch: Generalization Guarantees and Limits of Data-Independent Discretization
Journal of the ACM (JACM), Volume 71, Issue 2Article No.: 13, Pages 1–73https://doi.org/10.1145/3637840Tree search algorithms, such as branch-and-bound, are the most widely used tools for solving combinatorial and non-convex problems. For example, they are the foremost method for solving (mixed) integer programs and constraint satisfaction problems. Tree ...
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- research-articleMay 2024
SEMeL-LR: An improvised modeling approach using a meta-learning algorithm to classify breast cancer
Engineering Applications of Artificial Intelligence (EAAI), Volume 129, Issue CMar 2024https://doi.org/10.1016/j.engappai.2023.107630AbstractIn the last two decades, cancer has continued to be prone to a larger extent in females globally. In this regard, early prevention and treatment can help individuals to anticipate unnecessary deaths. At present, many advanced machine learning (ML)...
- research-articleFebruary 2024
Differential evolution ensemble designer
Expert Systems with Applications: An International Journal (EXWA), Volume 238, Issue PCMar 2024https://doi.org/10.1016/j.eswa.2023.121674AbstractA meta-evolutionary framework called Differential Evolution Ensemble Designer (DEED) has been proposed in this paper to automate the design of DE ensemble algorithms. Given the design components of DE ensembles and a set of optimization problems, ...
Highlights- An automated ensemble differential evolution (DE) designer named DEED is presented.
- A meta-evolutionary approach to automatically choose ensemble DE design components.
- Grammatical evolution forms the meta-evolutionary engine in ...
- research-articleFebruary 2024
Parameter tuning for software fault prediction with different variants of differential evolution
Expert Systems with Applications: An International Journal (EXWA), Volume 237, Issue PCMar 2024https://doi.org/10.1016/j.eswa.2023.121251AbstractThe cost of software testing could be reduced if faulty entities were identified prior to the testing phase, which is possible with software fault prediction (SFP). In most SFP models, machine learning (ML) methods are used, and one aspect of ...
- research-articleJanuary 2024
Leveraging ensemble learning for stealth assessment model with game-based learning environment
Soft Computing - A Fusion of Foundations, Methodologies and Applications (SOFC), Volume 28, Issue 4Feb 2024, Pages 3509–3517https://doi.org/10.1007/s00500-023-09605-8AbstractA distinguishing feature of intelligent game-based learning environment is its capacity for assisting stealth assessment. Stealth assessment gathers data regarding student competency in an invisible way and enables drawing valid inferences with ...
- research-articleApril 2024
Low-cost and precise automated re-design of antenna structures using interleaved geometry scaling and gradient-based optimization
Knowledge-Based Systems (KNBS), Volume 284, Issue CJan 2024https://doi.org/10.1016/j.knosys.2023.111296AbstractDesign of contemporary antennas is an intricate endeavor involving multiple stages, among others, tuning of geometry parameters. In particular, re-designing antennas to different operating frequencies, makes parametric optimization imperative to ...
- research-articleDecember 2023
A modified aquila optimizer with wide plant adaptability for the tuning of optimal fractional proportional–integral–derivative controller
Soft Computing - A Fusion of Foundations, Methodologies and Applications (SOFC), Volume 28, Issue 7-8Apr 2024, Pages 6269–6305https://doi.org/10.1007/s00500-023-09473-2AbstractThe heuristic tuning method of fractional-order proportional–integral–derivative (FOPID) control systems lacks robustness, and its performance often changes with specific controlled plants. To solve this problem, this paper proposes a new modified ...
- research-articleNovember 2023
PTSSBench: a performance evaluation platform in support of automated parameter tuning of software systems
Automated Software Engineering (KLU-AUSE), Volume 31, Issue 1May 2024https://doi.org/10.1007/s10515-023-00402-zAbstractAs software systems become increasingly large and complex, automated parameter tuning of software systems (PTSS) has been the focus of research and many tuning algorithms have been proposed recently. However, due to the lack of a unified platform ...
- research-articleFebruary 2024
Dynamic ensemble multi-strategy based bald eagle search optimization algorithm: A controller parameters tuning approach
- Ying Liu,
- Gongfa Li,
- Du Jiang,
- Juntong Yun,
- Li Huang,
- Yuanmin Xie,
- Guozhang Jiang,
- Jianyi Kong,
- Bo Tao,
- Chunlong Zou,
- Zifan Fang
Applied Soft Computing (APSC), Volume 148, Issue CNov 2023https://doi.org/10.1016/j.asoc.2023.110881AbstractTo address the problems of bald eagle search algorithm (BES), easy fall into local optimums, limited diversity and slow convergence, a dynamic ensemble multi-strategy bald eagle search (DMBES) algorithm is proposed. Firstly, a nonlinear control ...
Highlights- A dynamic ensemble multi-strategy-based BES is proposed for tuning parameters.
- Performance of DMBES is investigated using numerical problems and real problems.
- A non-linear control factor is constructed to explore the potential ...
- research-articleOctober 2023
Surrogate-assisted analysis of the parameter configuration landscape for meta-heuristic optimisation
Applied Soft Computing (APSC), Volume 146, Issue COct 2023https://doi.org/10.1016/j.asoc.2023.110705AbstractMeta-heuristics can provide high-quality solutions to challenging problems in a reasonable amount of time, but are highly sensitive to the values assigned to their control parameters. The parameter configuration landscape (PCL) offers ...
Highlights- Usage of artificial neural networks (ANNs) as surrogate models.
- Enhanced ...
- research-articleSeptember 2023
CoTuner: A Hierarchical Learning Framework for Coordinately Optimizing Resource Partitioning and Parameter Tuning
ICPP '23: Proceedings of the 52nd International Conference on Parallel ProcessingAugust 2023, Pages 317–326https://doi.org/10.1145/3605573.3605578The performance of modern multi-core systems is reliant upon two crucial configurations: how the resources are partitioned among the co-located applications to mitigate resource contention, and the setting of the parameters of applications. However, ...
- research-articleAugust 2023
Neural-network-based parameter tuning for multi-agent simulation using deep reinforcement learning
World Wide Web (WWWJ), Volume 26, Issue 5Sep 2023, Pages 3535–3559https://doi.org/10.1007/s11280-023-01197-5AbstractThis study proposes a new efficient parameter tuning method for multi-agent simulation (MAS) using deep reinforcement learning. MAS is currently a useful tool for social sciences, but is hard to realize realistic simulations due to its ...
- research-articleJuly 2023
A novel sequential switching quadratic particle swarm optimization scheme with applications to fast tuning of PID controllers
Information Sciences: an International Journal (ISCI), Volume 633, Issue CJul 2023, Pages 305–320https://doi.org/10.1016/j.ins.2023.03.011AbstractIn this work, a sequential switching quadratic particle swarm optimization (SSQPSO) scheme is investigated, where the velocity update mechanism is improved to enhance the convergence performance. Considering the sequential characteristics (...
- research-articleJune 2023
Design of Adaptive Time-Varying Sliding Mode Controller for Underactuated Overhead Crane Optimized via Improved Honey Badger Algorithm
Journal of Intelligent and Robotic Systems (JIRS), Volume 108, Issue 3Jul 2023https://doi.org/10.1007/s10846-023-01907-1AbstractTo improve the robustness of overhead crane controllers to variations in system physical parameters and enable a smooth startup of crane systems, an adaptive time-varying sliding mode controller optimized via an improved honey badger algorithm (...
- research-articleJune 2023
SimCost: cost-effective resource provision prediction and recommendation for spark workloads
Distributed and Parallel Databases (DAPD), Volume 42, Issue 1Mar 2024, Pages 73–102https://doi.org/10.1007/s10619-023-07436-yAbstractSpark is one of the most popular big data analytical platforms. To save time, achieve high resource utilization, and remain cost-effective for Spark jobs, it is challenging but imperative for data scientists to configure suitable resource ...
- ArticleOctober 2023
Predict, Tune and Optimize for Data-Driven Shift Scheduling with Uncertain Demands
Learning and Intelligent OptimizationJun 2023, Pages 254–269https://doi.org/10.1007/978-3-031-44505-7_18AbstractWhen it comes to data-driven optimization under uncertainty, it is well known that a naïve predict-then-optimize pipeline in which point forecasts are plugged into a deterministic optimization model typically leads to a poor expected decision ...