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A critical survey on proton exchange membrane fuel cell parameter estimation using meta -heuristic algorithms

JOURNAL OF CLEANER PRODUCTION 2020年 265卷 121660-121660页

作者： Yang, Bo Wang, Jingbo Yu, Lei Shu, Hongchun Yu, Tao Zhang, Xiaoshun Yao, Wei Sun, Liming Kunming Univ Sci & Technol Fac Elect Power Engn Kunming 650500 Peoples R China South China Univ Technol Coll Elect Power Guangzhou 510640 Peoples R China Shantou Univ Coll Engn Shantou 515063 Peoples R China Huazhong Univ Sci & Technol Sch Elect & Elect Engn State Key Lab Adv Electromagnet Engn & Technol Wuhan 430074 Peoples R China Guangzhou Shuimuqinghua Technol Co Ltd Guangzhou 510898 Peoples R China

A reliable and accurate mathematical modelling is crucial for performance analysis and optimal control of proton exchange membrane fuel cell (PEMFC) systems, which is mainly based on precise parameters estimation. With the significant computation advancement in the past decade, numerous meta-heuristic algorithms have been developed to solve such problem which have achieved quite promising results. Hence, this paper aims to undertake a comprehensive review on state-of-the-art meta-heuristic algorithms and related variants applied on PEMFC parameter estimation, which has greatly enhanced the diversity of algorithms compared with prior review. Note that over 160 literatures related to such problem are investigated for a systematic analysis and integration of relevant reported works. Particularly, these algorithms are classified into four categories, e.g., biology-based, physics-based, sociology-based and mathematics-based, upon which readers can systematically utilize these approaches to deal with PEMFC parameter estimation. Besides, various evaluation criteria, specific experimental performance, and other estimation techniques are also thoroughly discussed. Moreover, a thorough summary is carried out to systematically guide the readers to grasp and utilize these approaches. Lastly, some perspectives and recommendations are proposed in conclusion for future researches. In general, this paper can be regarded as a one-stop handbook for future in-depth researches in the related field.

关键词： meta-heuristic algorithms Parameter estimation Proton exchange membrane fuel cell Optimization methods

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Evaluating the performance of meta-heuristic algorithms on CEC 2021 benchmark problems

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NEURAL COMPUTING & APPLICATIONS 2023年第2期35卷 1493-1517页

作者： Mohamed, Ali Wagdy Sallam, Karam M. Agrawal, Prachi Hadi, Anas A. Mohamed, Ali Khater Cairo Univ Fac Grad Studies Stat Res Operat Res Dept Giza 12613 Egypt Amer Univ Cairo Dept Math & Actuarial Sci Sch Sci & Engn Cairo 11835 Egypt Univ Canberra Fac Sci & Technol Sch IT & Syst Canberra ACT 2601 Australia Comillas Pontificial Univ Inst Res Technol Madrid 28015 Spain Arab Open Univ Fac Comp Studies POB 84901 Riyadh 11681 Saudi Arabia October Univ Modern Sci & Arts MSA Fac Comp Sci Giza 12451 Egypt

To develop new meta-heuristic algorithms and evaluate on the benchmark functions is the most challenging task. In this paper, performance of the various developed meta-heuristic algorithms are evaluated on the recently developed CEC 2021 benchmark functions. The objective functions are parametrized by inclusion of the operators, such as bias, shift and rotation. The different combinations of the binary operators are applied to the objective functions which leads to the CEC2021 benchmark functions. Therefore, different meta-heuristic algorithms are considered which solve the benchmark functions with different dimensions. The performance of some basic, advanced meta-heuristics algorithms and the algorithms that participated in the CEC2021 competition have been experimentally investigated and many observations, recommendations, conclusions have been reached. The experimental results show the performance of meta-heuristic algorithms on the different combinations of binary parameterized operators.

关键词： Benchmark functions CEC2021 meta-heuristic algorithms Parameterization

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Spatial information sampling: another feedback mechanism of realising adaptive parameter control in meta-heuristic algorithms

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INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 2022年第1期19卷 48-58页

作者： Yang, Haichuan Tao, Sichen Zhang, Zhiming Cai, Zonghui Gao, Shangce Univ Toyama Fac Engn Toyama 9308555 Japan

This paper innovatively proposes a spatial information sampling strategy to adaptively control the parameters of meta-heuristic algorithms (MHAs). The solutions' spatial distribution information in current iterations is used to control the parameters in the following iterations. An adaptive parameter control method requires obtaining information from the operation of MHAs and feeding it back to the adjustment of parameters. The mainstream information acquisition method is to record the changes to the solutions in the iterative process. In essence, the proposed feedback method, i.e., chaotic perceptron (CP), makes use of the temporal information arising from the change of solutions in MHAs. The wingsuit flying search algorithm and differential evolution are employed as case studies. Experimental results validate the effectiveness of the proposed strategy. The source code of CP can be found at https: //***/.

关键词： meta-heuristic algorithms feedback method space-based information

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Improvements in meta-heuristic algorithms for minimum cost design of reinforced concrete rectangular sections under compression and biaxial bending

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ENGINEERING STRUCTURES 2017年 130卷 162-179页

作者： Sanchez-Olivares, G. Tomas, A. Univ Politecn Cartagena UPCT Dept Civil Engn Paseo Alfonso XIII 52 Murcia 30203 Spain

A numerical procedure is proposed in this paper for achieving the minimum cost design of reinforced concrete rectangular sections under compression and biaxial bending by using biologically-inspired meta-heuristic optimization algorithms. The problem formulation includes the costs of concrete, reinforcement and formwork, obtaining the detailed optimum design in which the section dimensions and the reinforcement correspond to values used in practice. The formulation has been simplified in order to reduce the computational cost while ensuring the rigor necessary to achieve safe designs. The numerical procedure includes the possibility of using high-strength concrete and several design constraints, such as minimum reinforcement and limiting the neutral axis depth. Two numerical examples are presented, drawing comparisons between the results obtained by ACI318 and EC2 standards. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Optimization meta-heuristic algorithms Reinforced concrete structures Cross section dimensioning Biaxial bending

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A Review on Community Detection in Large Complex Networks from Conventional to Deep Learning Methods: A Call for the Use of Parallel meta-heuristic algorithms

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IEEE ACCESS 2021年 9卷 96501-96527页

作者： Al-Andoli, Mohammed Nasser Tan, Shing Chiang Cheah, Wooi Ping Tan, Sin Yin Multimedia Univ Fac Informat Sci & Technol Malacca 75450 Malaysia Univ Nottingham Ningbo China Sch Comp Sci Fac Sci & Engn Ningbo 315100 Peoples R China

Complex networks (CNs) have gained much attention in recent years due to their importance and popularity. The rapid growth in the size of CNs leads to more difficulties in the analysis of CNs tasks. Community Detection (CD) is an important multidisciplinary research area where many machine/deep learning-based methods have been applied to map CNs into a low-dimensional representation for extracting information similarity among members of CNs. Currently, Deep Learning (DL) is one of the promising methods to extract knowledge and learn information from high dimensional space and represent it in low dimensional space. However, designing an accurate and efficient DL-based CD method especially when dealing with large CNs is always an on-going research endeavor to pursue. meta-heuristic (MH) algorithms have shown their potentials in improving DL models in terms of solution quality and computational cost. In addition, parallel computing is a feasible solution for building efficient DL models. The algorithmic principle of MH is parallel in nature;however, its computation framework in DL training that is reported in the literature is not really implemented in a parallel computing setup. In this paper, we present a systematic review of CD in CNs from conventional machine learning to DL methods and point out the gap of applying DL-based CD methods in large CNs. In addition, the relevant studies on DL with parallel and MH approaches are reviewed and their implications on DL models are highlighted to prospect effective solutions to overcome the challenges of DL-based CD methods. We also point out research challenges in the field of CD and suggest possible future research directions.

关键词： Deep learning Computational modeling Complex networks Social networking (online) Optimization Big Data Terminology Community detection deep learning complex networks meta-heuristic algorithms parallel computing

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Optimal Design of Reinforced Concrete Cantilever Retaining Walls Utilizing Eleven meta-heuristic algorithms: A Comparative Study

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PERIODICA POLYTECHNICA-CIVIL ENGINEERING 2020年第1期64卷 156-168页

作者： Kaveh, Ali Hamedani, Kiarash Biabani Bakhshpoori, Taha Iran Univ Sci & Technol Ctr Excellence Fundamental Studies Struct Engn Sch Civil Engn Tehran 16 Iran Univ Guilan Fac Technol & Engn Dept Civil Engn Rudsar Vajargah Iran

In this paper, optimum design of reinforced concrete cantilever retaining walls is performed under static and dynamic loading conditions utilizing eleven population-based meta-heuristic algorithms. These algorithms consist of Artificial Bee Colony algorithm, Big Bang-Big Crunch algorithm, Teaching-Learning-Based Optimization algorithm, Imperialist Competitive Algorithm, Cuckoo Search algorithm, Charged System Search algorithm, Ray Optimization algorithm, Tug of War Optimization algorithm, Water Evaporation Optimization algorithm, Vibrating Particles System algorithm, and Cyclical Parthenogenesis Algorithm. Two well-known methods consisting of the Rankine and Coulomb methods are used to determine lateral earth pressures acting on cantilever retaining wall under static loading condition. In addition, Mononobe-Okabe method is employed for dynamic loading condition. The design is based on ACI 318-05 and the goal of optimization is to minimize the cost function of the cantilever retaining wall. The performance of the utilized algorithms is investigated through an optimization example of cantilever retaining wall. In addition, convergence histories of the algorithms are provided for better understanding of their performance.

关键词： structural optimization cantilever retaining wall meta-heuristic algorithms Mononobe-Okabe method Rankine method Coulomb method

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Software prototype for validation of machining optimization solutions obtained with meta-heuristic algorithms

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EXPERT SYSTEMS WITH APPLICATIONS 2013年第17期40卷 6985-6996页

作者： Kovacevic, Marko Madic, Milos Radovanovic, Miroslav Univ Nis Fac Elect Engn Nish Serbia Univ Nis Fac Mech Engn Nish Serbia

Optimization of machining processes is of primary importance for increasing machining efficiency and economics. Determining optimal values of machining parameters is performed by applying optimization algorithms to mathematical models of relationships between machining parameters and machining performance measures. In recent years, there has been an increasing trend of using empirical models and meta-heuristic optimization algorithms. The use of meta-heuristic optimization algorithms is justified because of their ability to handle highly non-linear, multi-dimensional and multi-modal optimization problems. meta-heuristic algorithms are powerful optimization tools which provide high quality solutions in a short amount of computational time. However, their stochastic nature creates the need to validate the obtained solutions. This paper presents a software prototype for single and multi-objective machining process optimization. Since it is based on an exhaustive iterative search, it guarantees the optimality of determined solution in given discrete search space. The motivation for the development of the presented software prototype was the validation of machining optimization solutions obtained by meta-heuristic algorithms. To analyze the software prototype applicability and performance, six case studies of machining optimization problems, both single and multi-objective, were considered. In each case study the optimization solutions that had been determined by past researchers using meta-heuristic algorithms were either validated or improved by using the developed software prototype. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Machining Optimization meta-heuristic algorithms Exhaustive iterative search

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A new method of facial image compression based on meta-heuristic algorithms with variable bit budget allocation

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SIGNAL IMAGE AND VIDEO PROCESSING 2023年第8期17卷 3923-3931页

作者： Khodadadi, Reza Ardeshir, Gholamreza Grailu, Hadi Babol Noshirvani Univ Technol Fac Elect & Comp Engn Babol *** Iran Shahrood Univ Technol Fac Elect Engn Shahrud Iran

One of the important research areas in imaging is the formation of images, which plays an important role in many different applications, including surveillance, control, and security affairs. On the other hand, high spatial resolution is one of the most important factors for increasing image quality, but it increases the amount of storage memory. In face recognition systems, one of the existing challenges is maintaining the image recognition rate. Proposing a method that at least does not reduce detection rates would be very desirable. This article investigates how to compress facial images with high spatial resolution using innovative algorithms to reduce or even increase their accuracy as much as possible. In this article, meta-heuristic algorithms are used in a way that they are responsible for identifying the important and similar areas of matching macroblocks in the whole image segmentation. In the simulation and evaluation section, the facial images of the CIE and FEI databases have been examined as a selective study. The simulation results show the significant impact of the proposed methods using meta-heuristic algorithms in increasing the quality of PSNR and SSIM in contrast to the recognition efficiency. According to the proposed method, the larger the value of dividing the blocks, the better the average PSNR and SSIM. In general, depending on the type of application of the problem, there is a compromise to achieve the highest average PSNR or SSIM, using a genetic algorithm or gray wolf. The gray wolf algorithm, however, reaches its optimal answer much faster than the genetic algorithm.

关键词： Face image compression High spatial resolution meta-heuristic algorithms Recognition efficiency Bit budget allocation

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A survey on population-based meta-heuristic algorithms for motion planning of aircraft

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SWARM AND EVOLUTIONARY COMPUTATION 2021年 62卷 100844-100844页

作者： Wu, Yu Chongqing Univ Coll Aerosp Engn Chongqing 400044 Peoples R China Nanyang Technol Univ Sch Mech & Aerosp Engn Singapore 639798 Singapore

meta-heuristic algorithms have turned out to be good methods to address optimization problems with complicated constraints, and those algorithms have been widely applied to many aircraft motion planning problems. An optimal flight path is crucial for the aircraft to complete the specific task safely and efficiently. The existing surveys lack a broad review on the population-based meta-heuristic algorithms focusing on the aircraft motion planning problem, which fails to summarize the latest progress in this field. Therefore, a comprehensive survey on this topic is carried out from new perspectives. The mathematical model, the population-based meta-heuristic algorithms and their modifications regarding the aircraft motion planning problem are all included. Discussion is also made based on the statistical data from the collected literatures. It is anticipated that this survey will provide the researchers with some suggestions on how to select appropriate population-based meta-heuristic algorithms for a particular aircraft motion planning problem.

关键词： meta-heuristic algorithms Optimization problems Complicated constraints Aircraft Motion planning

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Performance evaluation of meta-heuristic algorithms for designing multi-objective multi-product multi-echelon supply chain network

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SOFT COMPUTING 2023年第17期27卷 12223-12248页

作者： Duffuaa, Salih O. Mohammed, Awsan King Fahd Univ Petr & Minerals Ind & Syst Engn Dept Dhahran 31261 Saudi Arabia King Fahd Univ Petr & Minerals Construct Engn & Management Dept Dhahran 31262 Saudi Arabia King Fahd Univ Petr & Minerals Interdisciplinary Res Ctr Smart Mobil & Logist Dhahran 31261 Saudi Arabia

The purpose of this paper is to develop and test three meta-heuristic algorithms to solve large size multi-objective supply chain network design problems. The algorithms are based on tabu search, genetic algorithm, and simulated annealing to find near-optimal global solutions. The three algorithms are designed, coded, and tested, and their parameters are fine tuned. The exact epsilon-constraint algorithm embedded in the General Algebraic Modeling System is used to validate the results of the three algorithms. The algorithms are compared using a typical multi-objective supply chain model utilizing several performance measures. The measures include the mean ideal distance, diversification metric, and percent of domination, inverted generational distance, and computation time. The results show that the tabu search algorithm outperformed the other two algorithms in terms of the percent of domination and computation time. On the other hand, the simulated annealing solutions are the best in terms of their diversity. The work in this paper is expected to help managers to solve large-scale supply chain problems that arise in oil and gas, petrochemical, and food supply chains.

关键词： meta-heuristic algorithms Multi-objective supply chain Tabu search Genetic algorithm Simulated annealing

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