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Prediction of lake water-level fluctuations using adaptive neuro-fuzzy inference system hybridized with metaheuristic optimization algorithms

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APPLIED WATER SCIENCE 2023年第1期13卷 1-17页

作者： Quoc Bao Pham Mohammadi, Babak Moazenzadeh, Roozbeh Heddam, Salim Zola, Ramiro Pillco Sankaran, Adarsh Gupta, Vivek Elkhrachy, Ismail Khedher, Khaled Mohamed Duong Tran Anh Thu Dau Mot Univ Inst Appl Technol Thu Dau Mot Binh Duong Prov Vietnam Lund Univ Dept Phys Geog & Ecosyst Sci Solvegatan 12 SE-22362 Lund Sweden Shahrood Univ Technol Fac Agr Dept Water Engn Shahrood Iran Univ 20 Aout 1955 Fac Sci Agron Dept Hydraul DivLab Res Biodivers Interact Ecosyst & Route El HadaikBP 26 Skikda Algeria Univ Mayor San Andres Inst Hidraul & Hidrol La Paz Bolivia TKM Coll Engn Kollam Dept Civil Engn Kollam 691005 India Indian Inst Technol Roorkee Dept Hydrol Roorkee Uttar Pradesh India Najran Univ Coll Engn Civil Engn Dept Najran Saudi Arabia King Khalid Univ Coll Engn Dept Civil Engn Abha 61421 Saudi Arabia High Inst Technol Studies Dept Civil Engn Mrezgua Univ Campus Nabeul 8000 Tunisia Van Lang Univ Inst Computat Sci & Artificial Intelligence Lab Environm Sci & Climate Change Ho Chi Minh City Vietnam Van Lang Univ Fac Environm Ho Chi Minh City Vietnam

Lakes help increase the sustainability of the natural environment and decrease food chain risk, agriculture, ecosystem services, and leisure recreational activities locally and globally. Reliable simulation of monthly lake water levels is still an ongoing demand for multiple environmental and hydro-informatics engineering applications. The current research aims to utilize newly developed hybrid data-intelligence models based on the ensemble adaptive neuro-fuzzy inference system (ANFIS) coupled with metaheuristics algorithms for lake water-level simulation by considering the effect of seasonality on Titicaca Lake water-level fluctuations. The classical ANFIS model was trained using three metaheuristics nature-inspired optimization algorithms, including the genetic algorithm (ANFIS-GA), particle swarm optimizer (ANFIS-PSO), and whale optimization algorithm (ANFIS-WOA). For determining the best set of the input variables, an evolutionary approach based on several lag months has been utilized prior to the lake water-level simulation process using the hybrid models. The proposed hybrid models were investigated for accurately simulating the monthly water levels at Titicaca Lake. The ANFIS-WOA model exhibited the best prediction performance for lake water-level pattern measurement in this study. For the best scenario (the inputs were Xt-1, Xt-2, Xt-3, Xt-4, Xt-12) the ANFIS-WOA model attained root mean square error (RMSE approximate to 0.08 m), mean absolute error (MAE approximate to 0.06 m), and coefficient of determination (R-2 approximate to 0.96). Also, the results showed that long-term seasonal memory for this lake is suitable input for lake water- level models so that the long-term dynamic memory of 1-year time series for lake water-level data is the best input for estimating the water level of Titicaca Lake.

关键词： metaheuristics algorithms Lake water-level prediction Surface water Hybrid model South America Freshwater management

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Optimal operation of reservoir systems using the Wolf Search Algorithm (WSA)

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WATER SUPPLY 2019年第5期19卷 1396-1404页

作者： Ahmadebrahimpour, Edris Islamic Azad Univ Sardasht Branch Young Researchers & Elite Club Sardasht Iran Islamic Azad Univ Urmia Branch Young Researchers & Elite Club Orumiyeh Iran

Optimizing hydropower plants is complex due to nonlinearity, complexity, and multidimensionality. This study introduces and evaluates the performance of the Wolf Search Algorithm (WSA) for optimizing the operation of a four-reservoir system and a single hydropower system in Iran. Results indicate WSA could reach 99.95 and 99.91 percent of the global optimum for the four-reservoir system and single reservoir system, respectively. Comparing the results of WSA with a genetic algorithm (GA) also indicates WSA's supremacy over GA. Thus, due to its simple structure and high capability, WSA is recommended for use in other water resources management problems.

关键词： four-reservoir system Karun-4 metaheuristics algorithms reservoir operation optimization Wolf Search Algorithm (WSA)

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Using Multi-objective Optimization and Finite Element Method to Reduce Cogging Torque in a Brushless DC Motor

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IETE JOURNAL OF RESEARCH 2024年第7期70卷 6350-6360页

作者： Salarian, Mina Azari, Milad Niaz Hajiaghaei- Keshteli, Mostafa Univ Sci & Technol Mazandaran Dept Elect Engn Behshahr Iran Univ Sci & Technol Mazandaran Dept Ind Engn Behshahr Iran

A significant issue in the design of the Brushless Direct Current (BLDC) motors is the cogging torque reduction that leads to negative effects on the BLDC motor's performance such as vibration and noise. However, most methods proposed to reduce cogging torque influence the output torque. Hence, this research aims to reduce cogging torque without having a significant effect on the output torque. For achieving the desired aim which is minimizing the cogging torque concerning the value of output torque, multi-objective optimization is a reliable approach. In this paper, some well-known multi-objective optimization including Multi-Objective Particle Swarm Optimization (MOPSO), Multi-Objective Simulated Annealing (MOSA), and Multi-Objective Red Deer Algorithm (MORDA), are employed to obtain the optimal design of a BLDC motor. In all used optimization algorithms, Simulation results are satisfying and display a significant reduction in the cogging torque, as well as the output torque increases.

关键词： Brushless DC (BLDC) motors Cogging torque metaheuristics algorithms Optimization Output torque Reduction

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A survey of symbiotic organisms search algorithms and applications

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NEURAL COMPUTING & APPLICATIONS 2020年第2期32卷 547-566页

作者： Abdullahi, Mohammed Ngadi, Md Asri Dishing, Salihu Idi Abdulhamid, Shafi'i Muhammad Usman, Mohammed Joda Ahmadu Bello Univ Dept Comp Sci Zaria Nigeria Univ Teknol Malaysia Fac Comp Dept Comp Sci Johor Baharu 81310 Malaysia Fed Univ Technol Minna Dept Cyber Secur Sci Minna Nigeria Bauchi State Univ Gadau Dept Math PMB 068 Bauchi Bauchi State Nigeria

Nature-inspired algorithms take inspiration from living things and imitate their behaviours to accomplish robust systems in engineering and computer science discipline. Symbiotic organisms search (SOS) algorithm is a recent metaheuristic algorithm inspired by symbiotic interaction between organisms in an ecosystem. Organisms develop symbiotic relationships such as mutualism, commensalism, and parasitism for their survival in ecosystem. SOS was introduced to solve continuous benchmark and engineering problems. The SOS has been shown to be robust and has faster convergence speed when compared with genetic algorithm, particle swarm optimization, differential evolution, and artificial bee colony which are the traditional metaheuristic algorithms. The interests of researchers in using SOS for handling optimization problems are increasing day by day, due to its successful application in solving optimization problems in science and engineering fields. Therefore, this paper presents a comprehensive survey of SOS advances and its applications, and this will be of benefit to the researchers engaged in the study of SOS algorithm.

关键词： Symbiotic organisms search metaheuristics algorithms Optimization Bio-inspired algorithms Local search Global search

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A modified firefly algorithm for the inverse kinematics solutions of robotic manipulators

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INTEGRATED COMPUTER-AIDED ENGINEERING 2021年第3期28卷 257-275页

作者： Hernandez-Barragan, Jesus Lopez-Franco, Carlos Arana-Daniel, Nancy Alanis, Alma Y. Lopez-Franco, Adriana Univ Guadalajara Comp Sci Dept CUCEI Marcelino Garcia Barragan 1421 Guadalajara 44430 Jalisco Mexico

The inverse kinematics of robotic manipulators consists of finding a joint configuration to reach a desired end-effector pose. Since inverse kinematics is a complex non-linear problem with redundant solutions, sophisticated optimization techniques are often required to solve this problem;a possible solution can be found in metaheuristic algorithms. In this work, a modified version of the firefly algorithm for multimodal optimization is proposed to solve the inverse kinematics. This modified version can provide multiple joint configurations leading to the same end-effector pose, improving the classic firefly algorithm performance. Moreover, the proposed approach avoids singularities because it does not require any Jacobian matrix inversion, which is the main problem of conventional approaches. The proposed approach can be implemented in robotic manipulators composed of revolute or prismatic joints of n degrees of freedom considering joint limits constrains. Simulations with different robotic manipulators show the accuracy and robustness of the proposed approach. Additionally, non-parametric statistical tests are included to show that the proposed method has a statistically significant improvement over other multimodal optimization algorithms. Finally, real-time experiments on five degrees of freedom robotic manipulator illustrate the applicability of this approach.

关键词： Denavit-Hartenberg Inverse kinematics metaheuristics algorithms Multimodal optimization

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A modified Henry gas solubility optimization for solving motif discovery problem

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NEURAL COMPUTING & APPLICATIONS 2020年第14期32卷 10759-10771页

作者： Hashim, Fatma A. Houssein, Essam H. Hussain, Kashif Mabrouk, Mai S. Al-Atabany, Walid Helwan Univ Fac Engn Cairo Egypt Menia Univ Fac Comp & Informat El Minya Egypt Univ Elect Sci & Technol China Inst Fundamental & Frontier Sci Chengdu Sichuan Peoples R China Misr Univ Sci & Technol Fac Engn Cairo Egypt

The DNA motif discovery (MD) problem is the main challenge of genome biology, and its importance is directly proportional to increasing sequencing technologies. MD plays a vital role in the identification of transcription factor binding sites that help in learning the mechanisms for regulation of gene expression. Metaheuristic algorithms are promising techniques for eliciting motif from DNA genomic sequences, but often fail to demonstrate robust performance by overcoming the inherent challenges in complex gene sequences, making search environment extremely non-convex for optimization methods. This paper proposes a novel modified Henry gas solubility optimization (MHGSO) algorithm for motif discovery which elicits a functional motif in DNA genomic sequences. In our approach, a new stage that captures the main characteristics of the motifs in DNA sequences is proposed, and MHGSO imitates the motifs characteristics for accurate detection of target motif. The performance of the MHGSO algorithm is validated using both synthetic and real datasets. Results confirm the stability and superiority of the proposed algorithm compared to state-of-the-art algorithms including MEME, DREME, XXmotif, PMbPSO, and MACS. Based on several evaluation matrices, MHGSO outperforms the competitor techniques in terms of nucleotide-level correlation coefficient, recall, precision,F-score, Cohen's Kappa, and statistical validation measures.

关键词： Henry gas solubility optimization (HGSO) Motif discovery (MD) Bioinformatics DNA metaheuristics algorithms

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An efficient symbiotic organisms search algorithm with chaotic optimization strategy for multi-objective task scheduling problems in cloud computing environment

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JOURNAL OF NETWORK AND COMPUTER APPLICATIONS 2019年第May期133卷 60-74页

作者： Abdullahi, Mohammed Ngadi, Md Asri Dishing, Salihu Idi Abdulhamid, Shafi'i Muhammad Ahmad, Barroon Isma'eel Ahmadu Bello Univ Zaria Dept Comp Sci Zaria Nigeria Fed Univ Technol Minna Dept Cyber Secur Sci Minna Nigeria Univ Teknol Malaysia Dept Comp Sci Fac Comp Johor Baharu 81310 Malaysia

In Cloud Computing model, users are charged according to the usage of resources and desired Quality of Service (QoS). Multi-objective task scheduling problem based on desired QoS is an NP-Complete problem. Due to the NP-Complete nature of task scheduling problems and huge search space presented by large scale problem instances, many of the existing solution algorithms cannot effectively obtain global optimum solutions. In this paper, a chaotic symbiotic organisms search (CMSOS) algorithm is proposed to solve multi-objective large scale task scheduling optimization problem on IaaS cloud computing environment. Chaotic optimization strategy is employed to generate initial ecosystem (population), and random sequence based components of the phases of SOS are replaced with chaotic sequence to ensure diversity among organisms for global convergence. In addition, chaotic local search strategy is applied to Pareto Fronts generated by SOS algorithms to avoid entrapment in local optima. The performance of the proposed CMSOS algorithm is evaluated on CloudSim simulator toolkit, using both standard workload traces and synthesized workloads for larger problem instances of up to 5000. Moreover, the performance of the proposed CMSOS algorithm was found to be competitive with the existing with the existing multi-objective task scheduling optimization algorithms. The CMSOS algorithm obtained significant improved optimal trade-offs between execution time (makespan) and financial cost (cost) with no computational overhead. Therefore, the proposed algorithms have potentials to improve the performance of QoS delivery.

关键词： Symbiotic organisms search metaheuristics algorithms Optimization NP-Complete Multi-objective task scheduling Cloud computing

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A hybrid metaheuristic algorithm for identification of continuous-time Hammerstein systems

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APPLIED MATHEMATICAL MODELLING 2021年 95卷 339-360页

作者： Jui, Julakha Jahan Ahmad, Mohd Ashraf Univ Malaysia Pahang Fac Elect & Elect Engn Technol Pekan 26600 Pahang Malaysia

This paper presents a new hybrid identification algorithm called the Average Multi-Verse Optimizer and Sine Cosine Algorithm for identifying the continuous-time Hammerstein system. In this paper, two modifications were employed on the conventional Multi-Verse Optimizer. Our first modification was an average design parameter updating mechanism to solve the local optima issue. The second modification was the hybridization of Multi-Verse Optimizer with Sine Cosine Algorithm that will balance the exploration and exploitation processes and thus improve the poor searching capability. The proposed hybrid method was used for identifying the parameters of linear and nonlinear subsystems in the Hammerstein model using the given input and output data. A continuous-time linear subsystem was considered in this study, while there were a few methods that utilize such models. Furthermore, various nonlinear subsystems such as the quadratic and hyperbolic functions had been used in those experiments. The efficiency of the novel technique is illustrated using a numerical example and two real-world applications, which are a twin rotor system and a flexible manipulator system. The numerical and experimental results analysis were observed with respect to the convergence curve of the fitness function, the parameter deviation index, time-domain and frequency-domain responses of the identified model, and the Wilcoxon's rank test. The results showed that the proposed method was efficient in identifying both the Hammerstein model subsystems in terms of the quadratic output estimation error and parameter deviation index. The proposed hybrid method also achieved better performance in modeling of the twin-rotor system as well as the flexible manipulator system and provided better solutions compared to other optimization methods including Particle Swarm Optimizer, Grey Wolf Optimizer, Multi-Verse Optimizer and Sine Cosine Algorithm. (c) 2021 Elsevier Inc. All rights reserved.

关键词： Hammerstein system identification metaheuristics algorithms Multi-verse optimizer Sine cosine algorithm Twin rotor system Flexible manipulator system

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A new hybrid optimization technique based on antlion and grasshopper optimization algorithms

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EVOLUTIONARY INTELLIGENCE 2023年第4期16卷 1383-1422页

作者： Amaireh, Anas Atef Al-Zoubi, Asem S. Dib, Nihad, I Univ Oklahoma Sch Elect & Comp Engn Norman OK 73071 USA Yarmouk Univ Telecommun Engn Dept POB 35 Irbid 21163 Jordan Jordan Univ Sci & Technol Elect Engn Dept POB 3030 Irbid 22110 Jordan

This paper proposes a new hybrid algorithm that merges the main features of two well-known metaheuristic algorithms;Grasshopper Optimization Algorithm (GOA) and Antlion Optimization (ALO) algorithm. ALO is strong in exploitation due to the mechanism of antlions in hunting other insects. On the other hand, the social forces in GOA represent the strong capability of exploration all over the search space. So, these features give the chance to combine ALO and GOA in one hybrid algorithm that significantly enhances the performance of both methods. The proposed hybrid algorithm is tested on 32 well-known benchmark test functions, 13 functions of the challenging CEC2015 functions, and two real problems in antenna array synthesis where the elements' excitation amplitudes and phases are optimized to minimize the maximum sidelobe level and impose nulls at specific angles. Comparisons show that the proposed algorithm outperforms 18 well-known optimization methods, including ALO and GOA, in the majority of these tests, with huge differences in some of them, which prove the stability, robustness, and efficiency of the proposed method over other robust algorithms.

关键词： Optimization metaheuristics algorithms Evolutionary algorithms Benchmark algorithm Antlion optimization algorithm Grasshopper optimization algorithm

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A metaheuristic approach to optimal morphology in reconfigurable tiling robots

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COMPLEX & INTELLIGENT SYSTEMS 2023年第5期9卷 5831-5850页

作者： Kalimuthu, Manivannan Pathmakumar, Thejus Hayat, Abdullah Aamir Elara, Mohan Rajesh Wood, Kristin Lee Singapore Univ Technol & Design ROAR Lab Engn Prod Dev 8 Somapah Rd Singapore 487372 Singapore Univ Colorado Denver Coll Engn Design & Comp 1200 Larimer StSte 3034 Denver CO 80204 USA

Reconfigurable robots are suitable for cleaning applications due to their high flexibility and ability to change shape according to environmental needs. However, continuous change in morphology is not an energy-efficient approach, with the limited battery capacity. This paper presents a metaheuristic-based framework to identify the optimal morphology of a reconfigurable robot, aiming to maximize the area coverage and minimize the energy consumption in the given map. The proposed approach exploits three different metaheuristic algorithms, namely, SMPSO, NSGA-II, and MACO, to generate the optimal morphology for every unique layout of a two-dimensional grid map by considering the path-length as the energy consumption. The novel feature of our approach is the implementation of the footprint-based Complete Coverage Path Planning (CCPP) adaptable for all possible configurations of reconfigurable robots. We demonstrate the proposed method in simulations and experiments using a Tetris-inspired robot with four blocks named Smorphi, which can reconfigure into an infinite number of configurations by varying its hinge angle. The optimum morphologies were identified for three settings, i.e., 2D indoor map with obstacles and free spaces. The optimum morphology is compared with the standard Tetris shapes in the simulation and the real-world experiment. The results show that the proposed framework efficiently produces non-dominated solutions for choosing the optimal energy-efficient morphologies.

关键词： Reconfigurable robots Path planning Area coverage metaheuristics algorithms Design principles

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