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optimization models to save energy and enlarge the operational life of water pumping systems

JOURNAL OF CLEANER PRODUCTION 2019年 213卷 89-98页

作者： Torregrossa, Dario Capitanescu, Florin Luxembourg Inst Sci & Technol ERIN Dept 41 Rue Brill L-4422 Belval Luxembourg

Water pumping systems are widely used in industrial and civil applications. During their operational life, they consume energy and materials (mainly for installation and replacement of components). The aim of this paper is to optimize the pump operations in order to save energy and enlarge the operational life of the pumps and their components. This paper addresses the multi-period (e.g. 24-hours time horizon) optimization of pumping systems. To this end, we have developed a simulation-based optimization approach including novel relevant technical features of a generic but realistic pumping system, namely cavitation and overflow. The proposed multi-period optimization differs further from the classic static optimization (i.e. at the design stage), proposing a new dynamic approach in which pump activation is steered dynamically for an optimal management of the variability of water inflow. Furthermore, the problem includes constraints on the number of pump activations allowed to reduce material strain. The performances of different meta-heuristic optimization algorithms (e.g. genetic algorithm, simulated annealing and particle swarm optimization) for solving the problem are compared. The numerical results show that energy savings are possible with the dynamic approach and that particle swarm optimization and simulated annealing algorithms provide the most suitable solutions for this problem. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Pump energy optimization meta-heuristic optimization algorithms Cavitation Energy efficiency Pump overflow

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Salp swarm algorithm: a comprehensive survey

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NEURAL COMPUTING & APPLICATIONS 2020年第15期32卷 11195-11215页

作者： Abualigah, Laith Shehab, Mohammad Alshinwan, Mohammad Alabool, Hamzeh Amman Arab Univ Fac Comp Sci & Informat Amman Jordan Aqaba Univ Technol Dept Comp Sci Aqaba Jordan Saudi Elect Univ Coll Comp & Informat Abha Saudi Arabia

This paper completely introduces an exhaustive and a comprehensive review of the so-called salp swarm algorithm (SSA) and discussions its main characteristics. SSA is one of the efficient recent meta-heuristic optimization algorithms, where it has been successfully utilized in a wide range of optimization problems in different fields, such as machine learning, engineering design, wireless networking, image processing, and power energy. This review shows the available literature on SSA, including its variants, like binary, modifications and multi-objective. Followed by its applications, assessment and evaluation, and finally the conclusions, which focus on the current works on SSA, suggest possible future research directions.

关键词： Salp swarm algorithm meta-heuristic optimization algorithms optimization problems Bio-inspired algorithms

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Advances in Sine Cosine Algorithm: A comprehensive survey

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ARTIFICIAL INTELLIGENCE REVIEW 2021年第4期54卷 2567-2608页

作者： Abualigah, Laith Diabat, Ali Amman Arab Univ Fac Comp Sci & Informat Amman 11953 Jordan New York Univ Abu Dhabi Div Engn Saadiyat Isl 129188 U Arab Emirates NYU Tandon Sch Engn Dept Civil & Urban Engn Brooklyn NY 11201 USA

The Sine Cosine Algorithm (SCA) is a population-based optimization algorithm introduced by Mirjalili in 2016, motivated by the trigonometric sine and cosine functions. After providing an overview of the SCA algorithm, we survey a number of SCA variants and applications that have appeared in the literature. We then present the results of a series of computational experiments to validate the performance of the SCA against similar algorithms.

关键词： Sine Cosine Algorithm meta-heuristic optimization algorithms optimization problems Population-based algorithms

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A comprehensive survey of the Grasshopper optimization algorithm: results, variants, and applications

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NEURAL COMPUTING & APPLICATIONS 2020年第19期32卷 15533-15556页

作者： Abualigah, Laith Diabat, Ali Amman Arab Univ Fac Comp Sci & Informat Amman Jordan New York Univ Abu Dhabi Div Engn Abu Dhabi 129188 Saadiyat Island U Arab Emirates NYU Tandon Sch Engn Dept Civil & Urban Engn Brooklyn NY 11201 USA

The grasshopper optimization algorithm is one of the dominant modern meta-heuristic optimization algorithms. It has been successfully applied to various optimization problems in several fields, including engineering design, wireless networking, machine learning, image processing, control of power systems, and others. We survey the available literature on the grasshopper optimization algorithm, including its modifications, hybridizations, and generalization to the binary, chaotic, and multi-objective cases. We review its applications, evaluate the algorithms, and provide conclusions.

关键词： Grasshopper optimization algorithm meta-heuristic optimization algorithms optimization problems Bio-inspired algorithms

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Fine-Tuning LightGBM Using an Artificial Ecosystem-Based Optimizer for Forest Fire Analysis

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FOREST SCIENCE 2023年第1期69卷 73-82页

作者： Quoc-Huy Nguyen Huu-Duy Nguyen Dinh Tan Le Quang-Thanh Bui Vietnam Natl Univ VNU Univ Sci Fac Geog 334 Nguyen Trai Str Hanoi Vietnam Trade Union Univ Fac Tourism Hanoi Vietnam

This study's main objective is to propose a hybrid machine learning model based on a gradient boosting algorithm named LightGBM and an artificial ecosystem-based optimization to improve the accuracy of forest fire susceptibility assessment. Four hundred twenty-six historical forest fires from the NASA portal and thirteen conditional factors including elevation, aspect, slope, curvature, normalized difference vegetation index, normalized difference water index, distance to residence, distance to road, distance to river, temperature, rain, humidity, and wind were used to train the model. The model performance was evaluated and compared with other benchmark models using root mean square error, area under receiver operating characteristics (AUC), and overall accuracy. The results show that the proposed model (AUC = 0.9705) outperforms others, such as Random Forest (AUC = 0.958), AdaBoost (AUC = 0.905), Bagging (AUC = 0.945), and Random Subspace (AUC = 0.938), respectively. The final model was interpreted to better understand the most influential factors of forest fire hazards. Study Implications: This study's main objective is to propose a hybrid machine learning model based on a gradient boosting algorithm called LightGBM and an artificial ecosystem-based optimization to improve the accuracy of forest fire susceptibility assessment. The final model was interpreted using the Shapley (SHAP) values to understand how input factors are related to the fire susceptibility level. This study fills a gap in the research literature, searching for optimal algorithms to improve forest fire susceptibility mapping.

关键词： forest fire susceptibility meta-heuristic optimization algorithms gradient boosting method

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Ant Lion Optimizer: A Comprehensive Survey of Its Variants and Applications

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ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING 2021年第3期28卷 1397-1416页

作者： Abualigah, Laith Shehab, Mohammad Alshinwan, Mohammad Mirjalili, Seyedali Abd Elaziz, Mohamed Amman Arab Univ Fac Comp Sci & Informat Amman Jordan Aqaba Univ Technol Comp Sci Dept Aqaba 77110 Jordan Torrens Univ Australia Ctr Artificial Intelligence Res & Optimisat Brisbane Qld 4006 Australia Zagazig Univ Fac Sci Dept Math Zagazig Egypt Wuhan Univ Technol Sch Comp Sci & Technol Wuhan Peoples R China

This paper introduces a comprehensive overview of the Ant Lion Optimizer (ALO). ALO is a novel metaheuristic swarm-based approach introduced by Mirjalili in 2015 to emulate the hunting behavior of ant lions in nature life. The review is highlighted the applications that are utilized ALO algorithm to solve various optimization problems. In ALO, the best solution is determined to enhance the performance of the functional and efficient during the optimization process by finding the minimum or maximum values to solve a certain problem. metaheuristic algorithms have become the focus of research due to introduce of decision-making and asses the benefits in solving various optimization problems. Also, a review of ALO variants is presented in this paper such as binary, modification, hybridization, enhanced, and others. The classifications of the ALO's applications include the benchmark functions, machine learning applications, networks applications, engineering applications, software engineering, and Image processing. Finally, According to the reviewed papers published in the literature, the ALO algorithm is mostly utilized in solving various optimization problems. Presenting an overview and reviewing the ALO applications are the main aims of this review paper.

关键词： Ant lion optimizer meta-heuristic optimization algorithms optimization problems Nature-inspired algorithms

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Optimal Synthesis of Concentric Circular Antenna Arrays Using Political Optimizer

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IETE JOURNAL OF RESEARCH 2022年第1期68卷 768-777页

作者： Durmus, Ali Kurban, Rifat Kayseri Univ Dept Elect & Energy Vocat Coll Kayseri Turkey Kayseri Univ Dept Comp Technol Vocat Coll Kayseri Turkey

In this study, political optimizer (PO) which has entered the literature as a novel socio-inspired meta-heuristic method is used to improve radiation properties of concentric circular antenna arrays (CCAAs) in the far field such as wireless communication of smart grids and Internet of things. The radiation pattern of far field is improved by reducing the sidelobe level (SLL), which is the most important parameter of the antenna radiation pattern. By reducing SLL, unwanted signals and interferences in the same frequency band are suppressed. PO is a novel global optimization method which is based on the multi-phased process of politics. The inter-element spacing and amplitude coefficients of concentric circular antenna array elements are determined optimally by PO algorithm to obtain a radiation pattern with a lower SLL value. To show the flexibility and performance of the proposed algorithm, three-ring CCAAs with different numbers of elements (4, 6, 8 elements and 8, 10, 12 elements) without and with the center element are synthesized. SLL values obtained by PO algorithm are compared with other recent optimization methods in the literature. The results obtained with PO algorithm in all comparisons show that it reached a lower SLL value than other optimization methods.

关键词： Antenna array synthesis concentric circular antenna array meta-heuristic optimization algorithms political optimizer sidelobe level wireless communication

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A hybrid rolling grey framework for short time series modelling

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NEURAL COMPUTING & APPLICATIONS 2021年第17期33卷 11339-11353页

作者： Cui, Zhesen Wu, Jinran Ding, Zhe Duan, Qibin Lian, Wei Yang, Yang Cao, Taoyun Changzhi Univ Dept Comp Sci Changzhi 046031 Peoples R China Queensland Univ Technol Sch Math Sci Brisbane Qld 4001 Australia Queensland Univ Technol Sch Comp Sci Brisbane Qld 4001 Australia Nanjing Univ Posts & Telecommun Coll Automat Nanjing 210023 Peoples R China Nanjing Univ Posts & Telecommun Coll Artificial Intelligence Nanjing 210023 Peoples R China Guangdong Univ Finance & Econ Sch Stat & Math Guangzhou 510320 Peoples R China

Time series modelling is gaining spectacular popularity in the prediction process of decision making, with applications including real-world management and engineering. However, for short time series, prediction has to face unavoidable limitation for modelling extremely complex systems. It has to apply inadequate and incomplete data from short time to predict unknown observations. With such limited data source, existing techniques, such as statistical modelling or machine learning methods, fail to predict short time series effectively. To address this problem, this paper provides a global framework for short time series modelling predictions, integrating the rolling mechanism, grey model, and meta-heuristic optimization algorithms. In addition, dragonfly algorithm and whale optimization algorithm are investigated and deployed to optimize the framework by enhancing its predicting accuracy with less computational costs. To verify the performance of the proposed framework, three industrial cases are introduced as simulation experiments in this paper. Experimental results confirm that the framework solves corresponding short time series modelling predictions with greater accuracy and speed than the standard GM(1,1) models. The source codes of this framework are available at: https://***/zhesencui/HybridRollingGreyFramework.

关键词： Short time series Rolling mechanism Grey model meta-heuristic optimization algorithms Forecasting

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An enhanced charged system search for configuration optimization using the concept of fields of forces

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STRUCTURAL AND MULTIDISCIPLINARY optimization 2011年第3期43卷 339-351页

作者： Kaveh, Ali Talatahari, Siamak Iran Univ Sci & Technol Ctr Excellence Fundamental Studies Struct Engn Tehran Iran Univ Tabriz Dept Civil Engn Tabriz Iran

The concept of the "fields of forces" is utilized as a general model of meta-heuristic algorithms from physics. This model is capable of representing the properties of different meta-heuristics and in this paper, it is used to enhance the recently developed meta-heuristic, the Charged System Search (CSS). The enhanced CSS is then applied to determine the configuration optimum design of structures. Comparison of the results for some examples, illustrates the efficiency of the enhanced CSS algorithm.

关键词： Fields of forces meta-heuristic optimization algorithms Charged system search Configuration optimization

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Dragonfly algorithm: a comprehensive survey of its results, variants, and applications

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MULTIMEDIA TOOLS AND APPLICATIONS 2021年第10期80卷 14979-15016页

作者： Alshinwan, Mohammad Abualigah, Laith Shehab, Mohammad Abd Elaziz, Mohamed Khasawneh, Ahmad M. Alabool, Hamzeh Al Hamad, Husam Amman Arab Univ Fac Comp Sci & Informat Amman 11953 Jordan Aqaba Univ Technol Comp Sci Dept Aqaba 77110 Jordan Wuhan Univ Technol Sch Comp Sci & Technol Wuhan Peoples R China Zagazig Univ Dept Math Fac Sci Zagazig Egypt Saudi Elect Univ Coll Comp & Informat Abha Saudi Arabia

This paper thoroughly introduces a comprehensive review of the so-called Dragonfly algorithm (DA) and highlights its main characteristics. DA is considered one of the promising swarm optimization algorithms because it successfully applied in a wide range of optimization problems in several fields, such as engineering design, medical applications, image processing, power and energy systems, and economic load dispatch problems. The review describes the available literature on DA, including its variants like binary, discrete, modify, and hybridization of DA. Conclusions focus on the current work on DA, highlighting its disadvantages with suggests possible future research directions. Researchers and practitioners of DA belonging to a wide range of audiences from the domains of optimization, engineering, medical, data mining, and clustering, among others will benefit from this study.

关键词： Dragonfly algorithm meta-heuristic optimization algorithms optimization problems Nature-inspired algorithms Swarm intelligence

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