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Adaptation of metaheuristic algorithms to improve training performance of an ESZSL model

TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2021年第3期29卷 1781-1796页

作者： Ozsari, Sifa Guzel, Mehmet Serdar Bostanci, Gazi Erkan Aydin, Ayhan Ankara Univ Fac Engn Dept Comp Engn Ankara Turkey

Zero-shot learning (ZSL) is a recent promising learning approach that is similar to human vision systems. ZSL essentially allows machines to categorize objects without requiring labeled training data. In principle, ZSL proposes a novel recognition model by specifying merely the attributes of the category. Recently, several sophisticated approaches have been introduced to address the challenges regarding this problem. Embarrassingly simple approach to zero shot learning (ESZSL) is one of the critical of those approaches that basically proposes a simple but efficient linear code solution. However, the performance of the ESZSL model mainly depends on parameter selection. metaheuristic algorithms are considered as one the most sophisticated computational intelligence paradigms that allows to approximate optimization problems with high success. This paper addresses this problem by adapting leading metaheuristic algorithms to automatically train the parameters of a linear ESZSL model. The model is statistically validated by performing a series of experiments with benchmark datasets.

关键词： Zero-shot learning metaheuristic algorithms genetic algorithm particle swarm optimization

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Discrete-binary configuration of metaheuristic algorithms for RFM optimization in the case of limited GCPs

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JOURNAL OF APPLIED REMOTE SENSING 2020年第2期14卷

作者： Gholinejad, Saeid Naeini, Amin Alizadeh Amiri-Simkooei, Alireza Univ Isfahan Fac Civil Engn & Transportat Dept Geomat Engn Esfahan Iran York Univ Dept Earth & Space Sci & Engn Toronto ON Canada

metaheuristic algorithms have been widely used in determining optimum rational polynomial coefficients (RPCs). By eliminating a number of unnecessary RPCs, these algorithms increase the accuracy of geometric correction of high-resolution satellite images. To this end, these algorithms use ordinary least squares and a number of ground control points (GCPs) to estimate RPCs. Due to the cost of GCP collection, using limited GCPs has become an attractive topic in various research studies. A configuration for RPC estimation using metaheuristic algorithms, namely, discrete-binary configuration for rational function model (DBRFM), is presented to find the optimal number and combination of RPCs in the case of limited GCPs. Based on the fact that the maximum number of RPCs is twice the number of GCPs, the particle/chromosome in the proposed configuration is composed of two binary and discrete parts. This configuration not only is compatible with the nature of the metaheuristic algorithms but also significantly reduces the search space. The proposed method has been tested on various types of remotely sensed data sets. The results of the experiments indicate the superiority of the DBRFM in comparison with the traditional binary configuration of metaheuristic algorithms. (C) 2020 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： rational function models metaheuristic algorithms limited number of GCPs discrete-binary configuration

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Application of Heuristic and metaheuristic algorithms in Solving Constrained Weber Problem with Feasible Region Bounded by Arcs

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MATHEMATICAL PROBLEMS IN ENGINEERING 2017年第1期2017卷 1-13页

作者： Stojanovic, Igor Brajevic, Ivona Stanimirovic, Predrag S. Kazakovtsev, Lev A. Zdravev, Zoran Goce Delcev Univ Fac Comp Sci Goce Delcev 89 Stip 2000 North Macedonia Univ Nis Fac Sci & Math Dept Math & Informat Visegradska 33 Nish 18000 Serbia Reshetnev Univ Dept Syst Anal & Operat Res Prosp Krasnoyarskiy Rabochiy 31 Krasnoyarsk 660037 Russia

The continuous planar facility location problem with the connected region of feasible solutions bounded by arcs is a particular case of the constrained Weber problem. This problem is a continuous optimization problem which has a nonconvex feasible set of constraints. This paper suggests appropriate modifications of four metaheuristic algorithms which are defined with the aim of solving this type of nonconvex optimization problems. Also, a comparison of these algorithms to each other aswell as to the heuristic algorithm is presented. The artificial bee colony algorithm, firefly algorithm, and their recently proposed improved versions for constrained optimization are appropriately modified and applied to the case study. The heuristic algorithm based on modified Weiszfeld procedure is also implemented for the purpose of comparison with the metaheuristic approaches. Obtained numerical results show that metaheuristic algorithms can be successfully applied to solve the instances of this problem of up to 500 constraints. Among these four algorithms, the improved version of artificial bee algorithm is the most efficient with respect to the quality of the solution, robustness, and the computational efficiency.

关键词： HEURISTIC programming DIFFERENTIAL evolution (Computer science) metaheuristic algorithms PROBABILITY theory MACHINE theory

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Using modified metaheuristic algorithms to solve a hazardous waste collection problem considering workload balancing and service time windows

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SOFT COMPUTING 2021年第3期25卷 1885-1912页

作者： Rabbani, Masoud Nikoubin, Alireza Farrokhi-Asl, Hamed Univ Tehran Coll Engn Sch Ind Engn POB 11155-4563 Tehran Iran Iran Univ Sci & Technol Sch Ind Engn Tehran Iran

Hazardous wastes' volume produced by human activities has increased in recent years. Consequently, associated risks involved in the treatment, recycling, disposing, and transportation of these hazardous materials have become more attractive for the researchers. In this study, we propose a new model for hazardous waste location routing problem. Appending the service time window and workload balance to the previous mathematical models can be taken into account as the major contributions of this study. Three objective functions including two systematic goals (cost and risk) and one social goal (workload balancing) have been considered for the model. Compatibility between wastes and a heterogeneous fleet of vehicles, which are rarely investigated in the literature, is discussed in this paper. Since the proposed model is classified as a multi-objective model, three multi-objective evolutionary algorithms, namely Non-dominated Sorting Genetic Algorithm II (NSGA-II), Pareto Envelope-based Selection Algorithm II (PESA-II), and Strength Pareto Evolutionary Algorithm II (SPEA-II) are employed. As two other innovations, an adaptive penalty function is developed and the PESA-II is modified by removing replicated solutions from its archive and their obtained results are discussed. Finally, by experimenting a number of test problems in different sizes, it is demonstrated that proposed modified PESA-II and SPEA-II perform better than NSGA-II in most of comparison metrics including feasible answers exploration, CPU time, spacing metric, inverted generational distance, quality metric, etc., whereas, NSGA-II creates more spread Pareto frontiers which are suitable for decision-maker to choose, from among a range of different options.

关键词： Hazardous waste Location routing problem Workload balancing Multi-objective optimization metaheuristic algorithms

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Application of a hybrid ANFIS with metaheuristic algorithms to estimate the aeration design parameters

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WATER SUPPLY 2023年第6期23卷 2249-2266页

作者： Hekmat, Mohsen Sarkardeh, Hamed Jabbari, Ebrahim Samadi, Mehrshad Iran Univ Sci & Technol Sch Civil Engn Tehran Iran Hakim Sabzevari Univ Fac Engn Dept Civil Engn Sabzevar Iran

Cavitation is a common and complex hydraulic phenomenon on the chute spillways and may cause damage to the structure. Aeration in the water flow is one of the best ways to prevent cavitation. To design an aerator, estimation of aeration coefficient (beta), jet length (L/h(0)), and jet impact angle on chute (tan.) are important in this study. The potential of a hybrid Adaptive Neuro-Fuzzy Interface System (ANFIS) with metaheuristic algorithms was investigated to estimate the required parameters to design an aerator. The ANFIS was combined with four metaheuristic algorithms, including Differential Evolution (DE), Ant Colony Optimization (ACO), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO). Experimental data and dimensionless parameters were used to develop the proposed hybrid ANFIS models. Three statistical indicators, including Root Mean Square Error (RMSE), Mean Average Error (MAE), and coefficient of determination (R-2), were employed to compare the proposed methods with empirical relations. According to the statistical indicators, among the data-driven methods, the ANFIS-DE method had the best prediction in estimating beta (RMSE = 0.018, R-2 = 0.984, MAE = 0.013), L/h(0) (RMSE = 1.293, R-2 = 0.963, MAE = 1.082), and tan gamma (RMSE = 0.009, R-2 = 0.939, MAE = 0.007).

关键词： aerator ANFIS cavitation data-driven methods metaheuristic algorithms

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A comparative study of several metaheuristic algorithms for optimizing complex 3-D well-path designs

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JOURNAL OF PETROLEUM EXPLORATION AND PRODUCTION TECHNOLOGY 2018年第4期8卷 1487-1503页

作者： Khosravanian, Rassoul Mansouri, Vahid Wood, David A. Alipour, Masood Reza Univ Amirkabir Dept Petr Engn Tehran Iran DWA Energy Ltd Lincoln England

Considering the importance of cost reduction in the petroleum industry, especially in drilling operations, this study focused on the minimization of the well-path length, for complex well designs, compares the performance of several metaheuristic evolutionary algorithms. Genetic, ant colony, artificial bee colony and harmony search algorithms are evaluated to seek the best performance among them with respect to minimizing well-path length and also minimizing computation time taken to converge toward global optima for two horizontal wellbore cases: (1) a real well offshore Iran;(2) a well-studied complex trajectory with several build and hold sections. A primary aim of the study is to derive less time-consuming algorithms that can be deployed to solve a range of complex well-path design challenges. This has been achieved by identifying flexible control parameters that can be successfully adjusted to tune each algorithm, leading to the most efficient performance (i.e., rapidly locating global optima while consuming minimum computational time), when applied to each well-path case evaluated. The comparative analysis of the results obtained for the two case studies suggests that genetic, artificial bee colony and harmony search algorithms can each be successively tuned with control parameters to achieve those objectives, whereas the ant colony algorithm cannot.

关键词： metaheuristic algorithms Well-path designing Well-path optimization Genetic algorithm Harmony search Artificial bee colony

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Daily scale river flow simulation: hybridized fuzzy logic model with metaheuristic algorithms

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HYDROLOGICAL SCIENCES JOURNAL 2021年第15期66卷 2155-2169页

作者： Dodangeh, Esmaeel Ewees, Ahmed A. Shahid, Shamsuddin Yaseen, Zaher Mundher Sari Agr Sci & Nat Resources Univ Dept Watershed Management Sari Iran Damietta Univ Comp Dept Dumyat Egypt Univ Teknol Malaysia UTM Fac Engn Sch Civil Engn Johor Baharu Malaysia Al Ayen Univ Sci Res Ctr New Era & Dev Civil Engn Res Grp Thi Qar 64001 Iraq Asia Univ Coll Creat Design Taichung Taiwan

Novel data-intelligence models developed through hybridization of an adaptive neuro-fuzzy inference system (ANFIS) with different metaheuristic algorithms, namely grey wolf optimizer (GWO), particle swarm optimizer (PSO) and whale optimization algorithm (WOA), are proposed for daily river flow prediction of the Taleghan River, which is the major source of potable water for Tehran, the capital of Iran. Gamma test (GT) was used for the determination of input variables for the models. The ANFIS-WOA model was found to exhibit the best performance in prediction of river flow according to root mean square error (RMSE approximate to 3.75 m(3).s(-1)) and Nash-Sutcliffe efficiency (NSE approximate to 0.93). It improved the prediction performance of the classical ANFIS model by 6.5%. The convergence speed of ANFIS-WOA was also found to be higher compared to other hybrid models. The success of the ANFIS-WOA model indicates its potential for use in the simulation of highly nonlinear daily rainfall-runoff relationships.

关键词： metaheuristic algorithms ANFIS river flow prediction catchment management hybrid model

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Performance comparison of metaheuristic algorithms using a modified Gaussian fitness landscape generator

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SOFT COMPUTING 2020年第10期24卷 7383-7393页

作者： Lee, Ho Min Jung, Donghwi Sadollah, Ali Kim, Joong Hoon Korea Univ Res Inst Mega Construct Seoul South Korea Keimyung Univ Dept Civil Engn Daegu South Korea Univ Sci & Culture Dept Mech Engn Tehran Iran Korea Univ Sch Civil Environm & Architectural Engn 306 Engn Bldg Seoul 02841 South Korea

Various metaheuristic optimization algorithms are being developed to obtain optimal solutions to real-world problems. metaheuristic algorithms are inspired by various metaphors, resulting in different search mechanisms, operators, and parameters, and thus algorithm-specific strengths and weaknesses. Newly developed algorithms are generally tested using benchmark problems. However, for existing traditional benchmark problems, it is difficult for users to freely modify the characteristics of a problem. Thus, their shapes and sizes are limited, which is a disadvantage. In this study, a modified Gaussian fitness landscape generator is proposed based on a probability density function, to make up for the disadvantages of traditional benchmark problems. The fitness landscape developed in this study contains a total of six features and can be employed to easily create various problems depending on user needs, which is an important advantage. It is applied to quantitatively evaluate the performance and reliability of eight reported metaheuristic algorithms. In addition, a sensitivity analysis is performed on the population size for population-based algorithms. Furthermore, improved versions of the metaheuristic algorithm are considered, to investigate which performance aspects are enhanced by applying the same fitness landscape. The modified Gaussian fitness landscape generator can be employed to compare the performances of existing optimization algorithms and to evaluate the performances of newly developed algorithms. In addition, it can be employed to develop methods of improving algorithms by evaluating their strengths and weaknesses.

关键词： metaheuristic algorithms Modified Gaussian fitness landscape generator Optimization Performance measurement

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Spatio-temporal modelling of asthma-prone areas using a machine learning optimized with metaheuristic algorithms

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GEOCARTO INTERNATIONAL 2022年第25期37卷 9917-9942页

作者： Razavi-Termeh, Seyed Vahid Sadeghi-Niaraki, Abolghasem Choi, Soo-Mi KN Toosi Univ Technol Fac Geodesy & Geomat Engn Geoinformat Technol Ctr Excellence Tehran Iran Sejong Univ Dept Comp Sci & Engn Seoul South Korea

The main aim of this study was to use two metaheuristic optimization algorithms-a genetic algorithm (GA) and a teaching-learning-based optimization (TLBO) algorithm-to determine the optimal parameters of a support vector regression (SVR) model for Spatio-temporal modelling of asthma-prone areas in Tehran, Iran. First, a spatial-temporal database consisting of dependent (872 patients with asthma) and independent data (air pollution, meteorology, distance to park, and street parameters) was created. In the next step, Spatio-temporal modelling and mapping of asthma-prone areas were performed using three models: SVR, SVR-GA, and SVR-TLBO. The highest accuracy of the area under the curve (AUC) of the receiver operating characteristic (ROC) was for SVR-GA (0.806, 0.801, 0.823, and 0.811), SVR-TLBO (0.8, 0.797, 0.81, and 0.803), and SVR (0.786, 0.78, 0.796, and 0.791) models in spring, summer, autumn, and winter, respectively. Autumn, winter, spring, and summer were most accurate in modelling asthma occurrence, respectively.

关键词： Asthma spatio-temporal modelling machine learning model metaheuristic algorithms geographic information system (GIS)

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Multi-objective metaheuristic algorithms for the mixed model assembly line sequencing problem with a bypass sub-line

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JOURNAL OF INDUSTRIAL AND PRODUCTION ENGINEERING 2021年第6期38卷 431-451页

作者： Rabbani, Masoud Tavassoli, Sara Rezaei, Aida Manavizadeh, Neda Univ Tehran Coll Engn Sch Ind Engn POB 11155-4563 Tehran Iran KHATAM Univ Dept Ind Engn Tehran Iran

Nowadays, mixed-model assembly lines (MMALs) are extensively applied to manufacture different products with no need for the changeover of the whole lines to satisfy the diversified preferences of consumers. In some assembly lines, there is a considerable variation in cycle times, which reduces production efficiency. However, a bypass sub-line that undertakes a portion of product assembly operations reduces the variation in the assembly times. In the present paper, the following three objective functions are considered simultaneously: (1) minimizing the variation in the actual and required production capacity, (2) minimizing total utility work, and (3) minimizing total variation in production rate. The formulated sequencing problem is solved by two different Multi-Objective Evolutionary Algorithm (MOEAs) including the Non-dominated Sorting Genetic Algorithm (NSGA-II) and Multi-Objective Particle Swarm Optimization (MOPSO). Then, some numerical examples are conducted, and the efficiency of the two proposed algorithms is measured based on some comparison metric.

关键词： Mixed model assembly line multi-objective optimization sequencing problem bypass sub-line metaheuristic algorithms

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