A Comprehensive Review on RSM-Coupled Optimization Techniques and Its Applications

Susaimanickam, Anto; Premalatha, M.; Joseph, Annie

doi:10.1007/s11831-023-09963-4

Cited by 8 publications

(1 citation statement)

References 150 publications

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“…Intelligent decoupling, involving optimization algorithms in system decoupling and control, stands out as a promising research direction. On the other hand, study [2] provides a comprehensive analysis of optimization techniques widely used in engineering applications. It extensively elaborates on heuristic algorithms and swarm intelligence algorithms that emerge from phenomena and behaviors in nature, comparing their effectiveness for various optimization problems.…”

Section: Introductionmentioning

confidence: 99%

Layered Composite Decoupling Control Based on Regional Dynamic Sparrow Search Algorithm

Fu,

You,

2023

Processes

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This paper addresses the issues of coupling and disturbances in a dual-tank water level control system within the context of process control in chemical water treatment at industrial facilities. In response to these challenges, a Layered Composite Decoupling Control system based on the Regional Dynamic Sparrow Search Algorithm (RDSSA-LCDC) is proposed. The utilization of an enhanced Regionally Dynamic Sparrow Search Algorithm (RDSSA) addresses the pitfalls of the Sparrow Search Algorithm (SSA), such as susceptibility to local optima and inadequate precision. RDSSA is employed for the parameter tuning of the system’s PID controller. Structurally, it incorporates a Hierarchical Composite Decoupling Control (LCDC) strategy, initially establishing a forward channel to construct an inner-layer decoupling model employing pre-feedback to rectify the lower-level system’s inputs, thereby mitigating inter-branch coupling. Subsequently, it develops an improved disturbance observer model based on pseudo-inverse compensation in the feedback channel, addressing conventional disturbance observer biases, and observing and suppressing system coupling and disturbances. Finally, within the dual-tank water level control system, various control schemes are simulated and compared, affirming the approach’s commendable decoupling, responsiveness, and disturbance rejection performance.

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Section: Introductionmentioning

confidence: 99%

Layered Composite Decoupling Control Based on Regional Dynamic Sparrow Search Algorithm

Fu,

You,

2023

Processes

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Advancements in Q‐learning meta‐heuristic optimization algorithms: A survey

Yang,

Gao,

Ding

et al. 2024

WIREs Data Min & Knowl

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This paper reviews the integration of Q‐learning with meta‐heuristic algorithms (QLMA) over the last 20 years, highlighting its success in solving complex optimization problems. We focus on key aspects of QLMA, including parameter adaptation, operator selection, and balancing global exploration with local exploitation. QLMA has become a leading solution in industries like energy, power systems, and engineering, addressing a range of mathematical challenges. Looking forward, we suggest further exploration of meta‐heuristic integration, transfer learning strategies, and techniques to reduce state space.This article is categorized under: Technologies > Computational Intelligence Technologies > Artificial Intelligence

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Extraction and optimization of biomolecules recovery from Chlorella sp. NITT 02: Unleashing potential through ultrasound and triphasic system of extraction

Anto,

Premalatha,

Mathimani

2024

J Appl Phycol

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A Comprehensive Review on RSM-Coupled Optimization Techniques and Its Applications

Cited by 8 publications

References 150 publications

Layered Composite Decoupling Control Based on Regional Dynamic Sparrow Search Algorithm

Layered Composite Decoupling Control Based on Regional Dynamic Sparrow Search Algorithm

Advancements in Q‐learning meta‐heuristic optimization algorithms: A survey

Extraction and optimization of biomolecules recovery from Chlorella sp. NITT 02: Unleashing potential through ultrasound and triphasic system of extraction

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