Empirical analysis of five nature-inspired algorithms on real parameter optimization problems

Agarwal, Parul; Mehta, Shikha

doi:10.1007/s10462-017-9547-5

“…In other words, choosing a route to achieve the desired goal is not definitive. In order to apply ACO for a problem, memory, artificial obstacles, and living in a discrete environment must be investigated [34,35], respectively. The first one enables researchers to store the route of the moment.…”

Section: Ant Colony Optimization (Aco)mentioning

confidence: 99%

Higher order modeling of a free-piston Stirling engine: analysis and experiment

Zare

¹

,

Shourangiz-Haghighi

²

2018

Int J Energy Environ Eng

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This paper focuses on higher order modeling and design of the free-piston Stirling engine (FPSE) based on Ant Colony Optimization (ACO). First, the governing thermodynamics and dynamical equations of the engine have been derived. Then, the design parameters of the engine are selected taking into account the finite heat transfer coefficient (resulting in a fifth-order model) and pressure drop (resulting in a sixth-order model) in the dynamical system and the corresponding differential equations are derived in detail. In the following, the mentioned methods and their performance in modeling the FPSE dynamics are investigated. The simulated results show that the effect of the pressure drop on the places of the closed-loop poles of the system is not significant, while the heat transfer coefficient has a considerable effect on the engine dynamics. Accordingly, a fifth-order model along with ACO algorithm is proposed to justify the FPSE behavior. To validate the presented modeling scheme, the prototype engine SUTECH-SR-1 was experimented. It is found that the values of parameters obtained from the proposed design method are close to those of the experiment. Besides, the presented higher order model predicts the engine behavior with an acceptable accuracy through which the validity of the design technique is affirmed. Initial pressure of working gas (Pa) P w Power generation (J/s) P Nonlinear pressure (Pa) R Ideal gas constant J kgVolume of expansion space m

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“…The evolutionary algorithm has been incorporated to form effective subspace clusters (Agarwal & Mehta, 2014, 2017) (L. Abualigah, Diabat, et al, 2021). The first study in this field was made by Sarafiset al (Sarafis et al, 2003).…”

Section: Introductionmentioning

confidence: 99%

“…A survey on natureinspired algorithms with evolutionary strategies and applications is illustrated in (Agarwal & Mehta, 2014). A comprehensive analysis of nature-inspired algorithms is shown in (Agarwal & Mehta, 2017). Comparative analysis of these algorithms on clustering is depicted in (Agarwal & Mehta, 2015).…”

Section: Introductionmentioning

confidence: 99%

A meta-heuristic density-based subspace clustering algorithm for high-dimensional data

Agarwal

¹

,

Mehta

²

,

Abraham

³

2021

Self Cite

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Subspace clustering is one of the efficient techniques for determining the clusters in different subsets of dimensions. Ideally, these techniques should find all possible non-redundant clusters in which the data point participates. Unfortunately, existing hard subspace clustering algorithms fail to satisfy this property. Additionally, with the increase in dimensions of data, classical subspace algorithms become inefficient. This work presents a new density-based subspace clustering algorithm (S_FAD) to overcome the drawbacks of classical algorithms. S_FAD is based on a bottom-up approach and finds subspace clusters of varied density using different parameters of the DBSCAN algorithm. The algorithm optimizes parameters of the DBCAN algorithm through a hybrid meta-heuristic algorithm and uses hashing concepts to discover all non-redundant subspace clusters. The efficacy of S_FAD is evaluated against various existing subspace clustering algorithms on artificial and real datasets in terms of F_Score and rand_index. Performance is assessed based on three parameters: average ranking, SRR ranking, and scalability on varied dimensions. Statistical analysis is performed through the Wilcoxon signed-rank test. Results reveal that S_FAD performs considerably better on the majority of the datasets and scales well up to 6400 dimensions on the actual dataset.

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“…A survey on natureinspired algorithms with evolutionary strategies and applications is illustrated in (Agarwal & Mehta, 2014). A comprehensive analysis of nature-inspired algorithms is shown in (Agarwal & Mehta, 2017). Comparative analysis of these algorithms on clustering is depicted in (Agarwal & Mehta, 2015).…”

Section: Introductionmentioning

confidence: 99%

A Meta-heuristic Density-Based Subspace Clustering Algorithm for High Dimensional Data

Agarwal

¹

,

Mehta

²

,

Abraham

³

2021

Preprint

Self Cite

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Subspace clustering is one of the efficient techniques for determining the clusters in different subsets of dimensions. Ideally, these techniques should find all possible non-redundant clusters in which the data point participates. Unfortunately, existing hard subspace clustering algorithms fail to satisfy this property. Additionally, with the increase in dimensions of data, classical subspace algorithms become inefficient. This work presents a new density-based subspace clustering algorithm (S_FAD) to overcome the drawbacks of classical algorithms. S_FAD is based on a bottom-up approach and finds subspace clusters of varied density using different parameters of the DBSCAN algorithm. The algorithm optimizes parameters of the DBCAN algorithm through a hybrid meta-heuristic algorithm and uses hashing concepts to discover all non-redundant subspace clusters. The efficacy of S_FAD is evaluated against various existing subspace clustering algorithms on artificial and real datasets in terms of F_Score and rand_index. Performance is assessed based on three parameters: average ranking, SRR ranking, and scalability on varied dimensions. Statistical analysis is performed through the Wilcoxon signed-rank test. Results reveal that S_FAD performs considerably better on the majority of the datasets and scales well up to 6400 dimensions on the actual dataset.

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Empirical analysis of five nature-inspired algorithms on real parameter optimization problems

Cited by 23 publications

References 23 publications

Higher order modeling of a free-piston Stirling engine: analysis and experiment

Higher order modeling of a free-piston Stirling engine: analysis and experiment

A meta-heuristic density-based subspace clustering algorithm for high-dimensional data

A Meta-heuristic Density-Based Subspace Clustering Algorithm for High Dimensional Data

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