Review on Nature-Inspired Algorithms

Korani, Wael; Mouhoub, Malek

doi:10.1007/s43069-021-00068-x

Cited by 28 publications

(9 citation statements)

References 154 publications

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“…PSO is an exemplary metaheuristic algorithm, and it has effectively solved various types of optimization problems. Korani and Mouhoub (2021) is a recent review of metaheuristic algorithms and their applications across various disciplines. PSO first generates a swarm of candidate solutions (known as particles) to the optimization problem (5).…”

Section: Constrained Mle and The Pso Algorithmmentioning

confidence: 99%

Single-cell generalized trend model (scGTM): a flexible and interpretable model of gene expression trend along cell pseudotime

Cui

Wong

Song

et al. 2021

Preprint

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Modeling single-cell gene expression trends along cell pseudotime is a crucial analysis for exploring biological processes. Most existing methods rely on nonparametric regression models for their flexibility; however, nonparametric models often provide trends too complex to interpret. Other existing methods use interpretable but restrictive models. Since model interpretability and flexibility are both indispensable for understanding biological processes, the single-cell field needs a model that improves the interpretability and largely maintains the flexibility of nonparametric regression models. Here we propose the single-cell generalized trend model (scGTM) for capturing a gene’s expression trend, which may be monotone, hill-shaped, or valley-shaped, along cell pseudotime. The scGTM has three advantages: (1) it can capture non-monotonic trends that are still easy to interpret, (2) its parameters are biologically interpretable and trend informative, and (3) it can flexibly accommodate common distributions for modeling gene expression counts. To tackle the complex optimization problems, we use the particle swarm optimization algorithm to find the constrained maximum likelihood estimates for the scGTM parameters. As an application, we analyze several single-cell gene expression data sets using the scGTM and show that it can capture interpretable gene expression trends along cell pseudotime and reveal molecular insights underlying the biological processes. We also provide an open-access Python package for fitting a scGTM at https://github.com/ElvisCuiHan/scGTM.

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Section: Constrained Mle and The Pso Algorithmmentioning

confidence: 99%

Single-cell generalized trend model (scGTM): a flexible and interpretable model of gene expression trend along cell pseudotime

Cui

Wong

Song

et al. 2021

Preprint

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“…Some are targeted to specific disciplines; for example, applications in building energy power and storage systems 4 , agriculture 19 , chemical engineering 20 , or for feature selection 21 with numerous applications in finance and reinforce learning, to name a few. Overview papers on metaheuristics are plentiful; see for example 22 – 24 . A most recent paper that gives a comprehensive overview on metaheuristics is 25 .…”

Section: Introductionmentioning

confidence: 99%

“…These algorithms have gained significant popularity in solving real, high-dimensional, and complex optimization problems. They have found widespread application in engineering, computer science, and various other disciplines to address challenging optimization problems 22 , 29 . Despite their versatility, these algorithms are under-utilized in some disciplines.…”

Section: Introductionmentioning

confidence: 99%

Applications of nature-inspired metaheuristic algorithms for tackling optimization problems across disciplines

Cui,

Zhang,

Chen

et al. 2024

Sci Rep

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Nature-inspired metaheuristic algorithms are important components of artificial intelligence, and are increasingly used across disciplines to tackle various types of challenging optimization problems. This paper demonstrates the usefulness of such algorithms for solving a variety of challenging optimization problems in statistics using a nature-inspired metaheuristic algorithm called competitive swarm optimizer with mutated agents (CSO-MA). This algorithm was proposed by one of the authors and its superior performance relative to many of its competitors had been demonstrated in earlier work and again in this paper. The main goal of this paper is to show a typical nature-inspired metaheuristic algorithmi, like CSO-MA, is efficient for tackling many different types of optimization problems in statistics. Our applications are new and include finding maximum likelihood estimates of parameters in a single cell generalized trend model to study pseudotime in bioinformatics, estimating parameters in the commonly used Rasch model in education research, finding M-estimates for a Cox regression in a Markov renewal model, performing matrix completion tasks to impute missing data for a two compartment model, and selecting variables optimally in an ecology problem in China. To further demonstrate the flexibility of metaheuristics, we also find an optimal design for a car refueling experiment in the auto industry using a logistic model with multiple interacting factors. In addition, we show that metaheuristics can sometimes outperform optimization algorithms commonly used in statistics.

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“…Nature-inspired algorithms have been widely applied in recent years for solving various range mathematical and engineering optimization non-deterministic polynomial hard (NP-hard) problems [1] due to its high robustness and efficiency in exploiting and exploring vast search space domain. Of all nature-inspired approaches, evolutionary algorithms (EA) and swarm intelligence metaheuristics stand out the most and they have been effectively applied to different NP-hard real-world challenges [2][3][4].…”

Section: Introductionmentioning

confidence: 99%

Novel Improved Salp Swarm Algorithm: An Application for Feature Selection

Živković

Stoean

Chhabra

et al. 2022

Sensors

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We live in a period when smart devices gather a large amount of data from a variety of sensors and it is often the case that decisions are taken based on them in a more or less autonomous manner. Still, many of the inputs do not prove to be essential in the decision-making process; hence, it is of utmost importance to find the means of eliminating the noise and concentrating on the most influential attributes. In this sense, we put forward a method based on the swarm intelligence paradigm for extracting the most important features from several datasets. The thematic of this paper is a novel implementation of an algorithm from the swarm intelligence branch of the machine learning domain for improving feature selection. The combination of machine learning with the metaheuristic approaches has recently created a new branch of artificial intelligence called learnheuristics. This approach benefits both from the capability of feature selection to find the solutions that most impact on accuracy and performance, as well as the well known characteristic of swarm intelligence algorithms to efficiently comb through a large search space of solutions. The latter is used as a wrapper method in feature selection and the improvements are significant. In this paper, a modified version of the salp swarm algorithm for feature selection is proposed. This solution is verified by 21 datasets with the classification model of K-nearest neighborhoods. Furthermore, the performance of the algorithm is compared to the best algorithms with the same test setup resulting in better number of features and classification accuracy for the proposed solution. Therefore, the proposed method tackles feature selection and demonstrates its success with many benchmark datasets.

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Review on Nature-Inspired Algorithms

Cited by 28 publications

References 154 publications

Single-cell generalized trend model (scGTM): a flexible and interpretable model of gene expression trend along cell pseudotime

Single-cell generalized trend model (scGTM): a flexible and interpretable model of gene expression trend along cell pseudotime

Applications of nature-inspired metaheuristic algorithms for tackling optimization problems across disciplines

Novel Improved Salp Swarm Algorithm: An Application for Feature Selection

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