Predicting air quality index using attention hybrid deep learning and quantum-inspired particle swarm optimization DOI Creative Commons
Anh Tuan Nguyen, Duy Hoang Pham, Bee Lan Oo

и другие.

Journal Of Big Data, Год журнала: 2024, Номер 11(1)

Опубликована: Май 11, 2024

Abstract Air pollution poses a significant threat to the health of environment and human well-being. The air quality index (AQI) is an important measure that describes degree its impact on health. Therefore, accurate reliable prediction AQI critical but challenging due non-linearity stochastic nature particles. This research aims propose hybrid deep learning model based Attention Convolutional Neural Networks (ACNN), Autoregressive Integrated Moving Average (ARIMA), Quantum Particle Swarm Optimization (QPSO)-enhanced-Long Short-Term Memory (LSTM) XGBoost modelling techniques. Daily data were collected from official Seoul registry for period 2021 2022. first preprocessed through ARIMA capture fit linear part followed by architecture developed in pretraining–finetuning framework non-linear data. used convolution extract features original data, then QPSO optimize hyperparameter LSTM network mining long-terms time series features, was adopted fine-tune final model. robustness reliability resulting assessed compared with other widely models across meteorological stations. Our proposed achieves up 31.13% reduction MSE, 19.03% MAE 2% improvement R-squared best appropriate conventional model, indicating much stronger magnitude relationships between predicted actual values. overall results show attentive inspired more feasible efficient predicting at both city-wide station-specific levels.

Язык: Английский

An exhaustive review of the metaheuristic algorithms for search and optimization: taxonomy, applications, and open challenges DOI Open Access
Kanchan Rajwar, Kusum Deep, Swagatam Das

и другие.

Artificial Intelligence Review, Год журнала: 2023, Номер 56(11), С. 13187 - 13257

Опубликована: Апрель 9, 2023

Язык: Английский

Процитировано

249
Puma optimizer (PO): a novel metaheuristic optimization algorithm and its application in machine learning DOI
Benyamın Abdollahzadeh, Nima Khodadadi, Saeid Barshandeh

и другие.

Cluster Computing, Год журнала: 2024, Номер 27(4), С. 5235 - 5283

Опубликована: Янв. 19, 2024

Язык: Английский

Процитировано

172
Mother optimization algorithm: a new human-based metaheuristic approach for solving engineering optimization DOI Creative Commons
Ivana Matoušová, Pavel Trojovský, Mohammad Dehghani

и другие.

Scientific Reports, Год журнала: 2023, Номер 13(1)

Опубликована: Июнь 26, 2023

This article's innovation and novelty are introducing a new metaheuristic method called mother optimization algorithm (MOA) that mimics the human interaction between her children. The real inspiration of MOA is to simulate mother's care children in three phases education, advice, upbringing. mathematical model used search process exploration presented. performance assessed on set 52 benchmark functions, including unimodal high-dimensional multimodal fixed-dimensional CEC 2017 test suite. findings optimizing functions indicate MOA's high ability local exploitation. global exploration. fixed-dimension multi-model suite show with balance exploitation effectively supports can generate appropriate solutions for problems. outcomes quality obtained from has been compared 12 often-used algorithms. Upon analysis comparison simulation results, it was found proposed outperforms competing algorithms superior significantly more competitive performance. Precisely, delivers better results most objective functions. Furthermore, application four engineering design problems demonstrates efficacy approach solving real-world statistical Wilcoxon signed-rank significant superiority twelve well-known managing studied this paper.

Язык: Английский

Процитировано

57
Lyrebird Optimization Algorithm: A New Bio-Inspired Metaheuristic Algorithm for Solving Optimization Problems DOI Creative Commons
Mohammad Dehghani,

Gulnara Bektemyssova,

Zeinab Montazeri

и другие.

Biomimetics, Год журнала: 2023, Номер 8(6), С. 507 - 507

Опубликована: Окт. 23, 2023

In this paper, a new bio-inspired metaheuristic algorithm called the Lyrebird Optimization Algorithm (LOA) that imitates natural behavior of lyrebirds in wild is introduced. The fundamental inspiration LOA strategy when faced with danger. situation, scan their surroundings carefully, then either run away or hide somewhere, immobile. theory described and mathematically modeled two phases: (i) exploration based on simulation lyrebird escape (ii) exploitation hiding strategy. performance was evaluated optimization CEC 2017 test suite for problem dimensions equal to 10, 30, 50, 100. results show proposed approach has high ability terms exploration, exploitation, balancing them during search process problem-solving space. order evaluate capability dealing tasks, obtained from were compared twelve well-known algorithms. superior competitor algorithms by providing better most benchmark functions, achieving rank first best optimizer. A statistical analysis shows significant superiority comparison addition, efficiency handling real-world applications investigated through twenty-two constrained problems 2011 four engineering design problems. effective tasks while

Язык: Английский

Процитировано

46
Pufferfish Optimization Algorithm: A New Bio-Inspired Metaheuristic Algorithm for Solving Optimization Problems DOI Creative Commons
Osama Al-Baik, Saleh Ali Alomari,

Omar Alssayed

и другие.

Biomimetics, Год журнала: 2024, Номер 9(2), С. 65 - 65

Опубликована: Янв. 23, 2024

A new bio-inspired metaheuristic algorithm named the Pufferfish Optimization Algorithm (POA), that imitates natural behavior of pufferfish in nature, is introduced this paper. The fundamental inspiration POA adapted from defense mechanism against predators. In mechanism, by filling its elastic stomach with water, becomes a spherical ball pointed spines, and as result, hungry predator escapes threat. theory stated then mathematically modeled two phases: (i) exploration based on simulation predator’s attack (ii) exploitation escape spiny pufferfish. performance evaluated handling CEC 2017 test suite for problem dimensions equal to 10, 30, 50, 100. optimization results show has achieved an effective solution appropriate ability exploration, exploitation, balance between them during search process. quality process compared twelve well-known algorithms. provides superior achieving better most benchmark functions order solve competitor Also, effectiveness handle tasks real-world applications twenty-two constrained problems 2011 four engineering design problems. Simulation solutions

Язык: Английский

Процитировано

34
Dollmaker Optimization Algorithm: A Novel Human-Inspired Optimizer for Solving Optimization Problems DOI Open Access

Saleh Al Omari,

Khalid Kaabneh,

I. Abu-Falahah

и другие.

International journal of intelligent engineering and systems, Год журнала: 2024, Номер 17(3), С. 816 - 828

Опубликована: Май 3, 2024

In this article, a new human-based metaheuristic algorithm named Dollmaker Optimization Algorithm (DOA) is introduced, which imitates the strategy and skill of dollmaker when making dolls.The basic inspiration DOA derived from two natural behaviors in doll process (i) general changes to dollmaking materials (ii) precise small on appearance characteristics theory proposed then modeled mathematically phases exploration based simulation large made doll-making exploitation performance optimization evaluated twenty-three standard benchmark functions unimodal, high-dimensional multimodal, fixed-dimensional multimodal types.The results show that has achieved suitable for problems with its ability exploration, exploitation, balance them during search process.Comparison twelve competing algorithms shows superior compared by providing better all getting rank first best optimizer.In addition, efficiency handling real-world applications four engineering design problems.Simulation acceptable real world values variables objective algorithms.

Язык: Английский

Процитировано

19
Botox Optimization Algorithm: A New Human-Based Metaheuristic Algorithm for Solving Optimization Problems DOI Creative Commons
Marie Hubálovská, Štěpán Hubálovský, Pavel Trojovský

и другие.

Biomimetics, Год журнала: 2024, Номер 9(3), С. 137 - 137

Опубликована: Фев. 23, 2024

This paper introduces the Botox Optimization Algorithm (BOA), a novel metaheuristic inspired by operation mechanism. The algorithm is designed to address optimization problems, utilizing human-based approach. Taking cues from procedures, where defects are targeted and treated enhance beauty, BOA formulated mathematically modeled. Evaluation on CEC 2017 test suite showcases BOA’s ability balance exploration exploitation, delivering competitive solutions. Comparative analysis against twelve well-known algorithms demonstrates superior performance across various benchmark functions, with statistically significant advantages. Moreover, application constrained problems 2011 highlights effectiveness in real-world tasks.

Язык: Английский

Процитировано

18
Machine learning-aided modeling for predicting freshwater production of a membrane desalination system: A long-short-term memory coupled with election-based optimizer DOI Creative Commons
Mohamed Abd Elaziz, Mohamed E. Zayed,

H. Abdelfattah

и другие.

Alexandria Engineering Journal, Год журнала: 2023, Номер 86, С. 690 - 703

Опубликована: Дек. 28, 2023

Membrane desalination (MD) is an efficient process for desalinating saltwater, combining the uniqueness of both thermal and separation distillation configurations. In this context, optimization strategies sizing methodologies are developed from balance system's energy demand. Therefore, robust prediction modeling thermodynamic behavior freshwater production crucial optimal design MD systems. This study presents a new advanced machine-learning model to obtain permeate flux tubular direct contact membrane unit. The was established by optimizing long-short-term memory (LSTM) election-based algorithm (EBOA). inputs were temperatures feed flow, rate salinity flow. optimized compared with other LSTM models sine–cosine (SCA), artificial ecosystem optimizer (AEO), grey wolf (GWO). All trained, tested, evaluated using different accuracy measures. LSTM-EBOA outperformed in predicting based on had highest coefficient determination 0.998 0.988 lowest root mean square error 1.272 4.180 training test, respectively. It can be recommended that paper provide useful pathway parameters selection performance systems makes optimally designed rates without costly experiments.

Язык: Английский

Процитировано

35
Kookaburra Optimization Algorithm: A New Bio-Inspired Metaheuristic Algorithm for Solving Optimization Problems DOI Creative Commons
Mohammad Dehghani, Zeinab Montazeri,

Gulnara Bektemyssova

и другие.

Biomimetics, Год журнала: 2023, Номер 8(6), С. 470 - 470

Опубликована: Окт. 1, 2023

In this paper, a new bio-inspired metaheuristic algorithm named the Kookaburra Optimization Algorithm (KOA) is introduced, which imitates natural behavior of kookaburras in nature. The fundamental inspiration KOA strategy when hunting and killing prey. theory stated, its mathematical modeling presented following two phases: (i) exploration based on simulation prey (ii) exploitation kookaburras’ ensuring that their killed. performance has been evaluated 29 standard benchmark functions from CEC 2017 test suite for different problem dimensions 10, 30, 50, 100. optimization results show proposed approach, by establishing balance between exploitation, good efficiency managing effective search process providing suitable solutions problems. obtained using have compared with 12 well-known algorithms. analysis shows KOA, better most functions, provided superior competition addition, implementation 22 constrained problems 2011 suite, as well 4 engineering design problems, approach acceptable to competitor algorithms handling real-world applications.

Язык: Английский

Процитировано

33
Drawer Algorithm: A New Metaheuristic Approach for Solving Optimization Problems in Engineering DOI Creative Commons
Eva Trojovská, Mohammad Dehghani, Víctor Leiva

и другие.

Biomimetics, Год журнала: 2023, Номер 8(2), С. 239 - 239

Опубликована: Июнь 6, 2023

Metaheuristic optimization algorithms play an essential role in optimizing problems. In this article, a new metaheuristic approach called the drawer algorithm (DA) is developed to provide quasi-optimal solutions The main inspiration for DA simulate selection of objects from different drawers create optimal combination. process involves dresser with given number drawers, where similar items are placed each drawer. based on selecting suitable items, discarding unsuitable ones and assembling them into appropriate described, its mathematical modeling presented. performance tested by solving fifty-two objective functions various unimodal multimodal types CEC 2017 test suite. results compared twelve well-known algorithms. simulation demonstrate that DA, proper balance between exploration exploitation, produces solutions. Furthermore, comparing shows effective problems much more competitive than against which it was to. Additionally, implementation twenty-two constrained 2011 suite demonstrates high efficiency handling real-world applications.

Язык: Английский

Процитировано

30