Hybrid Multi-population Based Approach for Controllers Structure and Parameters Selection

Łapa, Krystian; Cpałka, Krzysztof; Paszkowski, Józef

doi:10.1007/978-3-030-20912-4_42

Cited by 2 publications

(2 citation statements)

References 38 publications

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“…Examples of cooperative MAMP-HLH include the work of Zhang et al [22], Cruz-Chávez et al [58], and Łapa et al [59]. Zhang et al [22] hybridized CSA [33] with DE [56] to solve constrained engineering problems which can find satisfactory global optima and avoid premature convergence.…”

Section: Figure 2 Population-based Meta-heuristic Algorithm Implementationsmentioning

confidence: 99%

“…In this hybrid, diversification is performed by iterative SA [4] and the intensification of the search space is made through genetic approximation. Meanwhile, Łapa et al [59] propose a hybrid multi-population based approach where specified populations are processed using different population-based algorithms and synchronized accordingly for selecting the structure and parameters of the controller.…”

Section: Figure 2 Population-based Meta-heuristic Algorithm Implement...mentioning

confidence: 99%

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Hybrid Henry gas solubility optimization algorithm with dynamic cluster-to-algorithm mapping

Zamli

Kader

Azad

et al. 2021

Neural Comput & Applic

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Henry Gas Solubility Optimization Algorithm (HGSO) is a recently developed population-based metaheuristic algorithm in the literature. One notable feature of HGSO is that the algorithm divides its (single) population into a set of clusters that are individually mapped to an independent HGSO with its parameter settings (as well as its local best). At a glance, having multiple independent HGSO serving the given clusters in the population can definitely boost exploration (i.e., in terms of roaming the new potential region in the search space for better solution alternatives). However, a closer look reveals two main limitations. Firstly, HGSO-to-cluster mapping is statically defined. To be specific, the defined HGSO-to-cluster mapping does not consider its adaptive performance for the subsequent iteration. Secondly, HGSO implementation ignores the opportunity for hybridization with other meta-heuristic algorithms. With hybridization, one can compensate the limitation of a host algorithm with other algorithms' strength. Best results in the literature have often been associated with hybridization. Addressing these limitations, this paper proposes the development of Hybrid HGSO (HHGSO). Taking HGSO as the host algorithm, HHGSO is hybridized with four recently developed meta-heuristic algorithms, including Jaya Algorithm (JA), Sooty Tern Optimization Algorithm (STOA), Butterfly Optimization Algorithm (BOA) and Owl Search Algorithm (OSA). The individual mapping of each algorithm is made dynamic based on penalized and reward adaptive probability. Comparative performance of HHGSO with the aforementioned algorithms is conducted with a well-known Searchbased Software Engineering (SBSE) problem involving team formation problem. Additionally, the defined hybridization approach has also been adopted as a hybridization template for solving the combinatorial test generation problem with the same meta-heuristic algorithm combinations. Comparative performance is also undertaken against recently developed hyper-heuristic algorithms involving Exponential Monte Carlo with Counter, Modified Choice Function, Improvement Selection Rules, and Fuzzy Inference Selection. Our results indicate that the HHGSO hybridization has usefully improved the performance of the original HGSO and gives superior performance against the given competing algorithms.

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Section: Figure 2 Population-based Meta-heuristic Algorithm Implementationsmentioning

confidence: 99%

Section: Figure 2 Population-based Meta-heuristic Algorithm Implement...mentioning

confidence: 99%

Hybrid Henry gas solubility optimization algorithm with dynamic cluster-to-algorithm mapping

Zamli

Kader

Azad

et al. 2021

Neural Comput & Applic

View full text Add to dashboard Cite

show abstract

Multi-population Algorithm Using Surrogate Models and Different Training Plans

Kucharski,

Cpałka

2023

Lecture Notes in Computer Science

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Hybrid Multi-population Based Approach for Controllers Structure and Parameters Selection

Cited by 2 publications

References 38 publications

Hybrid Henry gas solubility optimization algorithm with dynamic cluster-to-algorithm mapping

Hybrid Henry gas solubility optimization algorithm with dynamic cluster-to-algorithm mapping

Multi-population Algorithm Using Surrogate Models and Different Training Plans

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