Particle Swarm Optimization with a Simplex Strategy to Avoid Getting Stuck on Local Optimum

Steffen, Vilmar

doi:10.5772/acrt.11

Cited by 5 publications

(1 citation statement)

References 86 publications

(79 reference statements)

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“…Employing the evolutionary algorithms to optimize the controllers nonetheless can be challenging when the nonlinearities are incorporated into the model as local minima issue is bound to occur, which consequently leads to suboptimal values [51]. One way to improve the algorithm is through hybridization, which involves combining different methods [52]- [54].…”

Section: Introductionmentioning

confidence: 99%

Modeling and Hybrid PSO-WOA-Based Intelligent PID and State-Feedback Control for Ball and Beam Systems

Ahmad

2023

IEEE Access

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The ball and beam (BnB) system serves as a benchmark in control engineering as it provides a foundational concept applicable to addressing stabilization challenges of various underactuated nonlinear systems. This includes tasks like maintaining the balance of goods carried by mobile robots and controlling the attitude of unmanned aerial vehicles. In this study, the focus is on enhancing control optimization strategies for BnB systems that take into account inherent nonlinearities arising from actuator constraints and state measurements. The work introduces a novel intelligent control approach, termed hybrid PSO-WOA, which combines Particle Swarm Optimization (PSO) and Whale Optimization Algorithm (WOA) to automate optimal parameter search for proportional-integral-derivative (PID) and state feedback (SF) controllers. The collaborative technique between PSO and WOA is formulated to strike a balance between exploration and exploitation phases, and to mitigate premature convergence risks due to the system's complexities. Additionally, three control schemes, namely cascade PID-PID, cascade PID-SF, and cascade PID-observer are introduced, each with tailored cost functions for optimization through the hybrid PSO-WOA algorithm, accommodating both measurable and unmeasurable state scenarios. Simulation results consistently demonstrate the superior performance of the hybrid approach compared to individual PSO and WOA methods, as well as conventional PID and linear quadratic regulator approaches. Notable, the hybrid approach exhibits a significant improvement in error metrics, reducing integral-time absolute error by 18.99%, integral squared error by 35.37%, and steady-state error by 92.86%. This substantial enhancement suggests promising directions for future research in automated control parameter tuning for underactuated nonlinear systems.

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Section: Introductionmentioning

confidence: 99%

Modeling and Hybrid PSO-WOA-Based Intelligent PID and State-Feedback Control for Ball and Beam Systems

Ahmad

2023

IEEE Access

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Optimisation-based strategies to identification of material parameters of hygro-thermo-mechanical problems

Vaz,

Zdanski

2024

Discov Mechanical Engineering

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Applications involving hygro-thermo-mechanical models are found in engineering fields as diverse as electronic packaging, concrete structures, composite materials and wood structures. Within the framework of wood processing, hygro-thermo-mechanical problems account for the cross-dependence of moisture content, temperature and dimensional changes of wooden components. Numerical simulation plays an important role in predicting the behaviour of wooden structures and, therefore, use of appropriate process and material parameters is essential for a successful prediction. The present work is inserted in this context and addresses application of optimisation techniques to identification of heat transfer, moisture diffusion and moisture-dependent swelling/shrinkage parameters. The direct problem is formulated based on a fully coupled transient solution of the energy, moisture and momentum transfer conservation laws. A global–local hybrid optimisation technique is proposed combining swarm intelligence and deterministic approaches, respectively based on particle swarm optimisation and the Nelder–Mead (NM) technique. The first stage aims at reducing the search space, so that the NM algorithm is able to determine the global minimum with acceptable accuracy. The strategy is both robust and efficient, being able to avoid local minima with reduced number of fitness computations. An illustrative example featuring a moisture-gaining process is also discussed, in which special attention is placed on the elastic and material swelling coupled effects caused by moisture transfer.

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Phenomenological modeling and simulation of competitive biosorption of ternary heavy metal systems in a fixed bed column

Suzaki

Triques²,

Munaro³

et al. 2023

Chemical Engineering Research and Design

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Particle Swarm Optimization with a Simplex Strategy to Avoid Getting Stuck on Local Optimum

Cited by 5 publications

References 86 publications

Modeling and Hybrid PSO-WOA-Based Intelligent PID and State-Feedback Control for Ball and Beam Systems

Modeling and Hybrid PSO-WOA-Based Intelligent PID and State-Feedback Control for Ball and Beam Systems

Optimisation-based strategies to identification of material parameters of hygro-thermo-mechanical problems

Phenomenological modeling and simulation of competitive biosorption of ternary heavy metal systems in a fixed bed column

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