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A systematic review of the emerging metaheuristic algorithms on solving complex optimization problems

NEURAL COMPUTING & APPLICATIONS 2023年第19期35卷 14275-14378页

作者： Turgut, Oguz Emrah Turgut, Mert Sinan Kirtepe, Erhan Izmir Bakircay Univ Fac Engn & Architecture Dept Ind Engn Menemen Izmir Turkiye Ege Univ Fac Engn Dept Mech Engn Bornova Izmir Turkiye Sirnak Univ Dept Motor Vehicles & Transportat Technol Sirnak Turkiye

The scientific field of optimization has witnessed an increasing trend in the development of metaheuristic algorithms within the current decade. The vast majority of the proposed algorithms have been proclaimed as superior and highly efficient compared to their contemporary counterparts by their own developers, which should be verified on a set of benchmark cases if it is to give conducive insights into their true capabilities. This study completes a comprehensive investigation of the general optimization capabilities of the recently developed nature-inspired metaheuristic algorithms, which have not been thoroughly discussed in past literature studies due to their new emergence. To overcome this deficiency in the existing literature, optimization benchmark problems with different functional characteristics will be solved by some of the widely used recent optimizers. Unconstrained standard test functions comprised of thirty-four unimodal scalable optimization problems with varying dimensionalities have been solved by these competitive algorithms, and respective estimated solutions have been evaluated relying on the performance metrics defined by the statistical analysis of the predictive results. Convergence curves of the algorithms have been construed to observe the evolution trends of objective function values. To further delve into comprehensive analysis on unconstrained test cases, CEC 2013 problems have been considered for comparison tools since their resemblances of the following features of real-world complex algorithms. The optimization capabilities of eleven metaheuristics algorithms have been comparatively analyzed on twenty-eight multidimensional problems. Finally, fourteen complex engineering problems have been optimized by the algorithms to scrutinize their effectiveness on handling the imposed design constraints.

关键词： Algorithm comparison Algorithm scalability metaheuristic algorithms Real-world design problems

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A survey on binary metaheuristic algorithms and their engineering applications

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ARTIFICIAL INTELLIGENCE REVIEW 2023年第7期56卷 6101-6167页

作者： Pan, Jeng-Shyang Hu, Pei Snasel, Vaclav Chu, Shu-Chuan Shandong Univ Sci & Technol Coll Comp Sci & Engn Qingdao 266590 Shandong Peoples R China Chaoyang Univ Technol Dept Informat Management Taichung 413310 Taiwan Nanyang Inst Technol Sch Comp Sci & Software Engn Nanyang 473004 Henan Peoples R China VSB Tech Univ Ostrava Fac Elect Engn & Comp Sci Ostrava 70032 Morayskoslersks Czech Republic

This article presents a comprehensively state-of-the-art investigation of the engineering applications utilized by binary metaheuristic algorithms. Surveyed work is categorized based on application scenarios and solution encoding, and describes these algorithms in detail to help researchers choose appropriate methods to solve related applications. It is seen that transfer function is the main binary coding of metaheuristic algorithms, which usually adopts Sigmoid function. Among the contributions presented, there were different implementations and applications of metaheuristic algorithms, or the study of engineering applications by different objective functions such as the single- and multi-objective problems of feature selection, scheduling, layout and engineering structure optimization. The article identifies current troubles and challenges by the conducted review, and discusses that novel binary algorithm, transfer function, benchmark function, time-consuming problem and application integration are need to be resolved in future.

关键词： metaheuristic algorithms Engineering applications Binary optimization

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Enhanced hybrid metaheuristic algorithms for optimal sizing of steel truss structures with numerous discrete variables

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STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION 2017年第6期55卷 2159-2180页

作者： Azad, Saeid Kazemzadeh Atilim Univ Dept Civil Engn Ankara Turkey

The advent of modern computing technologies paved the way for development of numerous efficient structural design optimization tools in the recent decades. In the present study sizing optimization problem of steel truss structures having numerous discrete variables is tackled using combined forms of recently proposed metaheuristic techniques. Three guided, and three guided hybrid metaheuristic algorithms are developed by integrating a design oriented strategy to the stochastic search properties of three recently proposed metaheuristic optimization techniques, namely adaptive dimensional search, modified big bang-big crunch, and exponential big bang-big crunch algorithms. The performances of the proposed guided, and guided hybrid metaheuristic algorithms are compared to those of standard variants through optimum design of real-size steel truss structures with up to 728 design variables according to AISC-LRFD specification. The numerical results reveal that the hybrid form of adaptive dimensional search and exponential big bang-big crunch algorithm is the most promising algorithm amongst the other investigated techniques.

关键词： Discrete sizing optimization Steel trusses metaheuristic algorithms Adaptive dimensional search Big bang-big crunch algorithm AISC-LRFD

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Modified metaheuristic algorithms for the Optimal Design of Multiplier-Less Non-uniform Channel Filters

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CIRCUITS SYSTEMS AND SIGNAL PROCESSING 2014年第3期33卷 815-837页

作者： Bindiya, T. S. Elias, Elizabeth Natl Inst Technol Dept Elect & Commun Engn Calicut 673601 Kerala India

Reconfigurable non-uniform channel filters are now being widely used in software define radio (SDR). The hardware implementation of these filters requires low complexity, low chip area and low power consumption. The frequency response masking (FRM) approach is proved to be a good candidate for the realization of a sharp digital finite impulse response (FIR) filter with low complexity. To reduce the complexity further, this paper gives an optimal design method which makes the channel filters totally multiplier-less. This is done in two steps. The channel filters are designed using the FRM approach with continuous filter coefficients. To obtain multiplier-less design, these filter coefficients are converted to finite-precision coefficients using signed power of two (SPT) space and the filter coefficients are synthesized in the canonic signed-digit (CSD) format. But this may lead to degradation of the filter performance. Hence the filter coefficients synthesis in the CSD format is formulated as an optimization problem. Several meta-heuristic algorithms like Differential Evolution (DE), Artificial Bee Colony (ABC), Harmony Search Algorithm (HSA) and Gravitational Search Algorithm (GSA) are modified and deployed and the best one is selected.

关键词： Frequency response masking Canonic signed digit metaheuristic algorithms Artificial bee colony Harmony search algorithm Gravitational search algorithm

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Improving metaheuristic algorithms With Information Feedback Models

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IEEE TRANSACTIONS ON CYBERNETICS 2019年第2期49卷 542-555页

作者： Wang, Gai-Ge Tan, Ying Ocean Univ China Dept Comp Sci & Technol Qingdao 266100 Peoples R China Jiangsu Normal Univ Sch Comp Xuzhou 221116 Jiangsu Peoples R China Northeast Normal Univ Inst Algorithm & Big Data Anal Changchun 130117 Jilin Peoples R China Northeast Normal Univ Sch Comp Sci & Informat Technol Changchun 130117 Jilin Peoples R China Peking Univ Sch Elect Engn & Comp Sci Key Lab Machine Percept Beijing 100871 Peoples R China Peking Univ Sch Elect Engn & Comp Sci Dept Machine Intelligence Beijing 100871 Peoples R China

In most metaheuristic algorithms, the updating process fails to make use of information available from individuals in previous iterations. If this useful information could be exploited fully and used in the later optimization process, the quality of the succeeding solutions would be improved significantly. This paper presents our method for reusing the valuable information available from previous individuals to guide later search. In our approach, previous useful information was fed back to the updating process. We proposed six information feedback models. In these models, individuals from previous iterations were selected in either a fixed or random manner. Their useful information was incorporated into the updating process. Accordingly, an individual at the current iteration was updated based on the basic algorithm plus some selected previous individuals by using a simple fitness weighting method. By incorporating six different information feedback models into ten metaheuristic algorithms, this approach provided a number of variants of the basic algorithms. We demonstrated experimentally that the variants outperformed the basic algorithms significantly on 14 standard test functions and 10 CEC 2011 real world problems, thereby, establishing the value of the information feedback models.

关键词： Benchmark evolutionary algorithms (EAs) evolutionary computation information feedback metaheuristic algorithms optimization algorithms swarm intelligence

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Spatial prediction of landslide susceptibility using hybrid support vector regression (SVR) and the adaptive neuro-fuzzy inference system (ANFIS) with various metaheuristic algorithms

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SCIENCE OF THE TOTAL ENVIRONMENT 2020年 741卷 139937-139937页

作者： Panahi, Mahdi Gayen, Amiya Pourghasemi, Hamid Reza Rezaie, Fatemeh Lee, Saro Kangwon Natl Univ Div Sci Educ Chuncheon Si 24341 Gangwon Do South Korea Korea Inst Geosci & Mineral Resources KICAM Geosci Platform Div 124 Gwahak Ro Daejeon 34132 South Korea Univ Calcutta Dept Geog Kama India Shiraz Univ Coll Agr Dept Nat Resources & Environm Engn Shiraz Iran Korea Univ Sci & Technol Dept Geophys Explorat 217 Gajeong Ro Daejeon 34113 South Korea

Landslides are natural and sometimes quasi-natural hazards that are destructive to natural resources and cause loss of human life every year. Hence, preparing susceptibility maps for landslide monitoring is essential to minimizing their negative effects. The main aim of the current research was to develop landslide susceptibility maps for Icheon Township, South Korea, using hybrid Machin learning and metaheuristic algorithms, that is, the bee algorithm (Bee), the adaptive neuro-fuzzy inference system (ANFIS), support vector regression (SVR), and the grey wolf optimizer (GWO), and to compare their predictive accuracy. Based on identified landslide locations, an inventory map was prepared and divided into training and validation data sets (70%/30%). the predicated model outcomes were validated with root mean square error (RMSE), and area under receiver operating characteristic curve (AUC), and pairwise comparison values for the ANFIS, ANFIS-Bee, ANFIS-GWO, SVR, SVR-Bee, and SVR-GWO models were obtained. The area under the curve was obtained with the training and validation data sets. Based on the training data sets, AUC of 80%, 83%, 83%, 69%, 81%, and 80% were obtained for the SVR, SVR-GWO, SVR-Bee, ANFIS, ANFIS-GWO, and ANFIS-Bee models, respectively. For the validation data sets, values of 79%, 82%, 82%, 68%, 79%, and 79%, respectively, were obtained. The SVR-GWO and SVR-Bee models were the most predictive models in terms of constructing the exceptionally focused landslide susceptibility map, with little spatial variation in the highly susceptible classes. Furthermore, the MSE, RMSE, and pairwise comparisons indicated that the SVR-GWO and SVR-Bee models were superior models for this study township. In addition, ANFIS individually was not superior to the ensembles of ANFIS-GWO and ANFIS-Bee for landslide assessment. These landslide susceptibility maps provide a platform for land use planning with an eye toward sustainable development of infrastructure and damage redu

关键词： Landslide susceptibility Support vector regression (SVR) Adaptive neuro-fuzzy inference system (ANFIS) metaheuristic algorithms

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Improved metaheuristic algorithms for Optimal Parameters Selection of Proton Exchange Membrane Fuel Cells: A Comparative Study

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IEEE ACCESS 2023年 11卷 7369-7397页

作者： Abdel-Basset, Mohamed Mohamed, Reda Abdel-Fatah, Laila Sharawi, Marwa Sallam, Karam M. Zagazig Univ Fac Comp & Informat Zagazig 44519 Egypt Amer Univ Kuwait Coll Engn & Appl Sci Salmiya 13034 Kuwait Univ Canberra Sch IT & Syst Canberra ACT 2601 Australia

As a new attempt to design a precise mathematical model for the proton exchange membrane fuel cell (PEMFC), in this paper, three recently-proposed well-established optimizers: horse herding optimization algorithm (HOA), seagull optimization algorithm (SOA) and gradient-based optimizer (GBO) integrated with two newly-proposed effective strategies, namely self-adaptive strategy, and ranking-based updating strategy, have been extensively investigated to accurately estimate the unknown parameters of this model for accomplishing a better output voltage of the simulated PEMFC stacks. Those hybridized algorithms were briefly named HHOA, HSOA, and HGBO. To assess the performance of those proposed algorithms, six common PEMFC stacks were used and their outcomes were extensively compared with the standard algorithms and some of the state-of-the-arts under various performance metrics and the Wilcoxon rank-sum test. The experimental findings show the effectiveness of both HGBO and HSOA in terms of convergence speed and final accuracy;However, HSOA could be more stable. The source code of this study is publicly available at https://***/sharing/d9263036-9f80-4a40-bad9-ad476ed19c69.

关键词： Optimization Cathodes Voltage metaheuristics Mathematical models Fuel cells Optimization methods Steady-state steady-state characterization PEMFC metaheuristic algorithms self-adaptive strategy modeling

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Optimal Placement and Capacity of Battery Energy Storage System in Distribution Networks Integrated With PV and EVs Using metaheuristic algorithms

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IEEE ACCESS 2023年 11卷 68379-68394页

作者： Pompern, Natsawat Premrudeepreechacharn, Suttichai Siritaratiwat, Apirat Khunkitti, Sirote Chiang Mai Univ Fac Engn Dept Elect Engn Chiang Mai 50200 Thailand Khon Kaen Univ Fac Engn Dept Elect Engn Khon Kaen 40002 Thailand

In this research, the optimal placement and capacity of battery energy storage systems (BESS) in distribution networks integrated with photovoltaics (PV) and electric vehicles (EVs) have been proposed. The main objective function is to minimize the system costs including installation, replacement, and operation and maintenance costs of the BESS. The replacement cost has been considered over 20 years while the operation and maintenance costs are the costs incurred by transmission line loss, voltage regulation, and peak demand. To solve the problem, three metaheuristic algorithms, namely particle swarm optimization (PSO), african vultures optimization algorithm (AVOA), and salp swarm algorithm (SSA), are employed. The proposed approach is evaluated on the IEEE 33- and 69-bus distribution systems integrated with PV and EVs. The results provided by the considered algorithms are compared in terms of the objective function, system efficiency enhancement, payback period, and statistical analysis. The simulation results show that after the BESS installation, the voltage profile can be improved, transmission loss is reduced, and peak demand is decreased where PSO provides the best objective values and AVOA achieves the fastest payback periods in both systems.

关键词： ~Battery energy storage systems photovoltaics electric vehicles metaheuristic algorithms particle swarm optimization African vultures optimization algorithm salp swarm algorithm

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Using metaheuristic algorithms for parameter estimation in generalized Mallows models

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APPLIED SOFT COMPUTING 2016年 38卷 308-320页

作者： Aledo, Juan A. Gamez, Jose A. Molina, David Univ Castilla La Mancha Dept Matemat Albacete 02071 Spain Univ Castilla La Mancha Dept Sistemas Informat Albacete 02071 Spain

This paper deals with the problem of parameter estimation in the generalized Mallows model (GMM) by using both local and global search metaheuristic (MH) algorithms. The task we undertake is to learn parameters for defining the GMM from a dataset of complete rankings/permutations. Several approaches can be found in the literature, some of which are based on greedy search and branch and bound search. The greedy approach has the disadvantage of usually becoming trapped in local optima, while the branch and bound approach, basically A* search, usually comes down to approximate search because of memory requirements, losing in this way its guaranteed optimality. Here, we carry out a comparative study of several MH algorithms (iterated local search (ILS) methods, variable neighborhood search (VNS) methods, genetic algorithms (GAs) and estimation of distribution algorithms (EDAs)) and a tailored algorithm A* to address parameter estimation in GMMs. We use 22 real datasets of different complexity, all but one of which were created by the authors by preprocessing real raw data. We provide a complete analysis of the experiments in terms of accuracy, number of iterations and CPU time requirements. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Generalized Mallows model Parameter estimation metaheuristic algorithms Search methods

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Comparison of various metaheuristic algorithms to extract the optimal PEMFC modeling parameters

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2024年第PartD期51卷 1402-1420页

作者： Korkmaz, Suleyman Aykut Cetinkaya, Sera Ayten Yuksel, Onur Konur, Olgun Erginer, K. Emrah Colpan, C. Ozgur Dokuz Eylul Univ Grad Sch Nat & Appl Sci Izmir Turkiye Dokuz Eylul Univ Maritime Fac Dept Marine Engn Izmir Turkiye Zonguldak Bulent Ecevit Univ Dept Marine Engn Maritime Fac Zonguldak Turkiye Dokuz Eylul Univ Maritime Fac Dept Maritime Educ Izmir Turkiye Dokuz Eylul Univ Fac Engn Dept Mech Engn Izmir Turkiye

In this study, operating parameters of a semi-empirical mathematical model of a proton-exchange membrane fuel cell (PEMFC) are extracted and compared using various meta-heuristic algorithms to analyze its behavior in a fast and economical manner before setting it up for application. Sum-squared error is employed as the objective function to find the values of seven unknown modeling parameters. The parameter extraction is carried out by using the polarization curve data of a 1000 W commercial PEMFC. Three different multi-attribute decision-making methods are used to rank the metaheuristic algorithms in terms of performance. The findings indicate that Moth Flame Algorithm is ranked first considering the sum-squared error (0.4389) and runtime (1054 s);whereas Grey Wolf Optimizer gives the best objective function efficiency (98.6034%) and lowest error rate (0.1096). It was also found that JAYA and Cuckoo Search are not convenient for parameter extraction of the specified fuel cell. Overall, the distinctive features of the study provide valuable insights and advancements in the field of fuel cell modeling and optimization.(c) 2023 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

关键词： PEMFC Parameter extraction metaheuristic algorithms MADM Optimization Marine vessel

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