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rao algorithms-Based Structure Optimization for Heterogeneous Wireless Sensor Networks

Computers, Materials & Continua 2024年第1期78卷 873-897页

作者： Shereen K.Refaay Samia A.Ali Moumen T.El-Melegy Louai A.Maghrabi Hamdy H.El-Sayed Faculty of Computers and Artificial Intelligence Sohag UniversitySohag82524Egypt Faculty of Engineering Assiut UniversityAssiut711745Egypt College of Engineering University of Business and TechnologyJeddah23411Saudi Arabia

The structural optimization of wireless sensor networks is a critical issue because it impacts energy consumption and hence the network’s *** studies have been conducted for homogeneous networks,but few have been performed for heterogeneouswireless sensor *** paper utilizes rao algorithms to optimize the structure of heterogeneous wireless sensor networks according to node locations and their initial *** proposed algorithms lack algorithm-specific parameters and metaphorical *** proposed algorithms examine the search space based on the relations of the population with the best,worst,and randomly assigned *** proposed algorithms can be evaluated using any routing protocol,however,we have chosen the well-known routing protocols in the literature:Low Energy Adaptive Clustering Hierarchy(LEACH),Power-Efficient Gathering in Sensor Information Systems(PEAGSIS),Partitioned-based Energy-efficient LEACH(PE-LEACH),and the Power-Efficient Gathering in Sensor Information Systems Neural Network(PEAGSIS-NN)recent routing *** compare our optimized method with the Jaya,the Particle Swarm Optimization-based Energy Efficient Clustering(PSO-EEC)protocol,and the hybrid Harmony Search Algorithm and PSO(HSA-PSO)*** efficiencies of our proposed algorithms are evaluated by conducting experiments in terms of the network lifetime(first dead node,half dead nodes,and last dead node),energy consumption,packets to cluster head,and packets to the base *** experimental results were compared with those obtained using the Jaya optimization *** proposed algorithms exhibited the best *** proposed approach successfully prolongs the network lifetime by 71% for the PEAGSIS protocol,51% for the LEACH protocol,10% for the PE-LEACH protocol,and 73% for the PEGSIS-NN protocol;Moreover,it enhances other criteria such as energy conservation,fitness convergence,packets to cluster head,and packets to the base sta

关键词： Wireless sensor networks rao algorithms optimization LEACH PEAGSIS

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rao algorithms based on elite local search method

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NEURAL COMPUTING & APPLICATIONS 2023年第6期35卷 4435-4465页

作者： Tefek, Mehmet Fatih Osmaniye Korkut Ata Univ Dept Comp Engn Osmaniye Turkey

The rao algorithms, which have been proposed for solving complex and continuous optimization problems lately, are described as metaphor-less optimization algorithms because they do not contain algorithm-specific parameters. The rao algorithms have variants called rao-1, rao-2 and rao-3, respectively, depending on different population updating procedures. In rao 1-3 algorithms, random interactions between candidate solutions and the best and worst solution in the whole population for solving optimizations problems were determined as the basic principle. Although this situation makes the rao 1-3 algorithms increase the speed of convergence, it can cause the diversity of candidate solutions to decrease and the local search capacity to reduce. In this study, a new elite local search procedure was added to the population updating procedure of rao algorithms to expand the capacity of rao 1-3 algorithms and develop their solutions. The proposed method was called ELSrao-1, ELSrao-2 and ELSrao-3. Fifteen unconstrained unimodal, fifteen unconstrained multimodal functions and twenty-nine unconstrained CEC 2017 benchmark test functions were used to analyze the performance of the proposed ELSrao 1-3 algorithms. Jaya, dragonfly algorithm, arithmetic optimization algorithm, whale optimization algorithm and standard rao 1-3 algorithms which are all state-of-the-art algorithms were used to compare the superiority and success of the proposed ELSrao 1-3 algorithms in benchmark functions. Friedman's mean rank test and Tukey-Kramer post hoc test were applied for statistical analysis. According to the experimental studies and statistical analysis, it was concluded that the proposed ELSrao 1-3 algorithms proved to be efficient and robust in the solution to unconstrained optimization problems.

关键词： rao algorithms Elite local search procedure Benchmark functions Optimization

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Optimizing Multiobjective Time-Cost-Quality Problems in Construction Projects: Efficacy of Strength Pareto-Based rao algorithms

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JOURNAL OF CONSTRUCTION ENGINEERING AND MANAGEMENT 2025年第5期151卷

作者： Yilmaz, Mehmet Dede, Tayfun Erzincan Binali Yildirim Univ Dept Construct Technol TR-24100 Erzincan Turkiye Karadeniz Tech Univ Dept Civil Engn TR-61080 Trabzon Turkiye

In construction project management, the optimization of time, cost, and quality trade-off is essential for maximizing overall project benefits. While various algorithms have been proposed to address this multiobjective optimization problem, there remains a significant research gap in the efficiency and effectiveness of selection operators in guiding the search process toward optimal solutions. Additionally, many existing methods are computationally expensive, making them impractical for large-scale projects. Therefore, this study proposes use of strength Pareto-based rao algorithms (SP2-rao-1 and -2) as an approach to solving multiobjective time-cost-quality problems in construction projects with better optimization performance at lower computation costs. Furthermore, to explore the impact of the selection operators that determine the candidates for subsequent iterations in multiobjective optimization tasks, rao algorithms with nondominated sorting and a crowding distance operator (NDSII-rao-1 and -2) were also implemented. The performance of the strength Pareto-based rao algorithms was evaluated using two case studies with 18 and 60 activities, respectively. The developed SP2-rao series were compared with opposition-based multiple objective differential evolution (OMODE) and multiobjective artificial bee colony with differential evolution (MOABCDE) algorithms, which have previously been used to solve similar problems. The findings indicated that the SP2-rao series surpassed earlier techniques in addressing multiobjective construction time-cost-quality problems, achieving greater efficiency and effectiveness with reduced function evaluations. Moreover, the SP2-rao algorithms produced significantly better results compared to the NDSII-rao algorithms.

关键词： Time-cost-quality Strength Pareto rao algorithms

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Modeling and Forecasting of Water Demand in the City of Istanbul Using Artificial Neural Networks Optimized with rao algorithms

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2024年第10期49卷 13477-13490页

作者： Uzlu, Ergun Karadeniz Tech Univ Fac Engn Dept Civil Engn TR-61080 Trabzon Turkiye

In this study, a hybrid artificial neural network (ANN)-rao series (rao_1, rao_2, and rao_3) algorithm model was developed to analyze water consumption in Istanbul province, Turkey. A multiple linear regression (MLR) model was developed and an ANN was also trained with back-propagation (BP) artificial bee colony (ABC) algorithms for comparison. Gross domestic product and population data were treated as independent variables. To test the accuracy of the presently developed hybrid model, its outputs were compared with those of ANN-BP, ANN-ABC, and MLR models. Error values calculated for the test set indicated that the ANN-rao_3 algorithm outperformed the MLR, ANN-BP, and ANN-ABC reference models as well as ANN-rao_1 and ANN-rao_2 algorithms. Therefore, using the ANN-rao_3 model, water consumption forecasts for Istanbul province were generated out to 2035 for low-, expected-, and high-water demand conditions. The model-generated forecasts indicate that the water requirements of Istanbul in 2035 will be between 1182.95 and 1399.54 million m3, with the upper-range estimates outpacing supplies. According to low and expected scenarios, there will be no problem in providing the water needs of Istanbul until 2035. However, according to high scenario, water needs of Istanbul will not be provided as of ***, water conservation policies should be enacted to ensure provision of the water needs of Istanbul province from 2033 onward.

关键词： Water demand forecasting rao algorithms Neural networks Istanbul

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Design optimization of cam-follower mechanisms using rao algorithms and their variants

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EVOLUTIONARY INTELLIGENCE 2024年第2期17卷 745-770页

作者： rao, R., V Pawar, R. B. SV Natl Inst Technol Dept Mech Engn Surat 395007 Gujarat India

This paper presents the design optimization of cam-follower mechanisms (CFM) with eccentric roller type follower using recently developed advanced optimization algorithms, namely rao, SAMP-rao, and QO-rao algorithms. Four types of follower motion law i.e., cycloidal, modified harmonic, 3-4-5 degree polynomial, and 4-5-6-7 degree polynomial motion, are considered. The CFM is optimized to minimize three objectives such as the input torque needed to rotate the cam, the radius of the pitch circle of the cam, and the maximum contact stress. The problem has five continuous design variables, namely the roller radius (R-g), the radius of cam base circle (R-b), the distance between the follower bearing and the center of the cam (q), the eccentricity of the follower (e), and the length of follower bearing (b). Eight design constraints related to the geometry of the cam mechanism, the pressure angle, the efficiency of the mechanism, the curvature radius of the pitch curve, and the maximum contact stress, are considered. The computational results obtained using rao algorithms and their variants are compared with other advanced optimization algorithms such as the salp swarm algorithm (SSA), ant lion optimizer (ALO), moth-flame optimization (MFO), multi verse optimizer (MVO), evaporation rate water cycle algorithm (ER-WCA), grey wolf optimizer (GWO), and mine blast algorithm (MBA). The comparison of optimization results reveals that the optimum value of a fitness function obtained using rao algorithms and their variants is superior to GWO, ALO, MFO, SSA, ER-WCA, MBA, and MVO for all four cases considered. The optimum fitness function value of the CFM with case III is reduced by 3.14%, 4.13%, and 7.61% compared to the CFM's fitness function value with the case I, case II, and case IV, respectively. Hence, the 3-4-5 degree polynomial motion of the follower is effective for better performance of the CFM. Also, the average time required for rao algorithms and their variants is compar

关键词： rao algorithms Optimization algorithms Metaheuristics Engineering design problems Cam-follower mechanism

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Multi-Objective Optimization of Welding Processes Using Modified rao algorithms

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JOURNAL OF ADVANCED MANUFACTURING SYSTEMS 2024年第2期23卷 479-508页

作者： rao, Ravipudi Venkata Araya, Alok Sardar Vallabhbhai Natl Inst Technol Dept Mech Engn Surat 395007 Gujarat India

The welding community faces a challenging problem in choosing the best welding methods since they are multi-input processes. Modern manufacturing industries have requirements to get fast and optimum process parameters to utilize complete resources in an optimum way. This work attempts to improve the performance of submerged arc welding (SAW), friction welding (FW), and gas tungsten arc welding (GTAW) processes by optimizing their parameters. The newly developed rao algorithms and their modified versions known as quasi-oppositional rao (QO rao), self-adaptive multi-population elite rao (SAMPE rao), and improved rao (I-rao) are used. This paper contains four multi-objective optimization case studies of SAW, FW, and GTAW processes. A weighted approach is employed to tackle the multi-objective optimization problems effectively. The outcomes achieved using the rao, QO rao, SAMPE rao, and I-rao algorithms are compared with those obtained by the established optimization algorithms such as accelerated cuckoo optimization algorithm (ACCOA), cuckoo optimization algorithm (COA), plant propagation algorithm (PPA), teaching-learning-based optimization (TLBO) algorithm, Jaya algorithm, quasi-oppositional Jaya (QO Jaya) algorithm, genetic algorithm (GA), particle swarm optimization (PSO) algorithm, and heat transfer search (HTS) algorithm. The effectiveness of the rao algorithms and their modified versions has been clearly demonstrated as these algorithms have provided superior solutions while requiring fewer generations to achieve them.

关键词： Optimization rao algorithms submerged arc welding friction welding gas tungsten arc welding

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Optimization of spatial truss towers based on rao algorithms

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STRUCTURAL ENGINEERING AND MECHANICS 2022年第3期81卷 367-378页

作者： Grzywinski, Maksym Czestochowa Tech Univ Fac Civil Engn PL-42200 Czestochowa Poland

In this study, combined size and shape optimization of spatial truss tower structures are presented by using new optimization algorithms named rao-1, and rao-2. The nodal displacements, allowable stress and buckling for compressive members are taken into account as structural constraints for truss towers. The discrete and continuous design variables are used as design variables for size and shape optimization. To show the efficiency of the proposed optimization algorithm, 25-bar, and 39-bar 3D truss towers are solved for combined size and shape optimization. The 72-bar, and 160-bar 3D truss towers are solved only by size optimization. The optimal results obtained from this study are compared to those given in the literature to illustrate the efficiency and robustness of the proposed algorithm. The structural analysis and the optimization process are coded in MATLAB programming.

关键词： allowable stress buckling rao algorithms size and shape optimization tower structures

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rao algorithms for multi-objective optimization of selected thermodynamic cycles

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ENGINEERING WITH COMPUTERS 2021年第4期37卷 3409-3437页

作者： rao, R. Venkata Keesari, Hameer Singh Sardar Vallabhbhai Natl Inst Technol Dept Mech Engn Surat 395007 India

This work proposes multi-objective rao algorithms. The basic rao algorithms are modified for solving multi-objective optimization problems. The proposed algorithms have no algorithm-specific parameters and no metaphorical meaning. Based on the interaction of the population with best, worst, and randomly selected solutions, the proposed algorithms explore the search space. The proposed algorithms handle multiple objectives simultaneously based on dominance principles and crowding distance evaluation. In addition, multi-attribute decision-making method-based selection scheme for identifying the best solutions from the Pareto fronts is included. The proposed algorithm performances are investigated on a case study of solar-assisted Brayton heat engine system and a case study of Stirling heat engine system to see whether there can be any improvement in the performances of the considered systems. Furthermore, the efficiencies of the rao algorithms are evaluated in terms of spacing, hypervolume, and coverage metrics. The results obtained by the proposed algorithms are compared with those obtained by the latest advanced optimization algorithms. It is observed that the results obtained by the proposed algorithms are superior. The performances of the considered case studies are improved by the application of the proposed optimization algorithms. The proposed optimization algorithms are simple, robust, and can be easily implemented to solve different engineering optimization problems.

关键词： rao algorithms Multi-objective optimization Solar-dish Stirling heat engine Solar-assisted Brayton heat engine Decision making in multi-objective optimization

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Towards Crafting Optimal Functional Link Artificial Neural Networks with rao algorithms for Stock Closing Prices Prediction

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COMPUTATIONAL ECONOMICS 2022年第1期60卷 1-23页

作者： Das, Subhranginee Nayak, Sarat Chandra Sahoo, Biswajit KIIT Univ Sch Comp Engn Bhubaneswar 751024 India CMR Coll Engn & Technol Dept Comp Sci & Engn Hyderabad 501401 India

Quite a good number of population-based meta-heuristics based on mimicking natural phenomena are observed in the literature in resolving varieties of complex optimization problems. They are widely used in search of the optimal model parameters of artificial neural networks (ANNs). However, efficiencies of these are mostly dependent on fine tuning algorithm-specific parameters. rao algorithms are metaphor-less meta-heuristics which do not need any algorithm-specific parameters. Functional link artificial neural network (FLANN) is a flat network and possesses the ability of mapping input-output nonlinear relationships by using amplification in input vector dimension. This article attempts to observe the efficacy of rao algorithms on searching the most favorable parameters of FLANN, thus forming hybrid models termed as rao algorithm-based FLANNs (RAFLANNs). The models are evaluated on forecasting five stock markets such as NASDAQ, BSE, DJIA, HSI, and NIKKEI. The RAFLANNs performances are compared with that of variations of FLANN (i.e., FLANN based on gradient descent, multi-verse optimizer, monarch butterfly optimization and genetic algorithm) and conventional models (i.e., MLP, SVM and ARIMA). The proposed models are found better in terms of prediction accuracy, computation time and statistical significance test.

关键词： Stock market forecasting rao algorithms Functional link artificial neural network Genetic algorithm Monarch butterfly optimization Financial time series forecasting

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Optimization of roofs with solar panels using rao algorithms

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APPLIED SOFT COMPUTING 2024年 165卷

作者： Yakak, Busra Atmaca, Barbaros Kinali, Nur Sena Dede, Tayfun Grzywinski, Maksym rao, Ravipudi Venkata Karadeniz Tech Univ Dept Civil Engn Trabzon Turkiye Czestochowa Tech Univ Fac Civil Engn Czestochowa Poland Sardar Vallabhbhai Natl Inst Technol Dept Mech Engn Surat Gujarat India

Steel space truss roof (SSTR) systems are widely used in structures with large spans such as stadiums, sports halls, and shopping malls, as well as in industrial buildings such as factories, workshops, and production facilities. Solar panels are integrated into SSTR systems to enable these structures to generate energy and to increase the use of sustainable energy sources. With the installation of the solar panels, an additional load was placed on the roofs. However, this raises questions regarding whether existing roofs can withstand this additional weight without compromising their design. This study can be an important reference source for future truss roof designs by increasing sustainable energy use and functionally optimizing the SSTR. Another contribution of this study is that it directly contributes to real-world applications by ensuring that SSTR designs are more efficient and economical for engineering projects. For these purposes, this study presents the size optimization of the SSTR with and without solar panels using the rao-1 and rao-2 algorithms, which are metaheuristic algorithms known as the rao algorithms. Thus, information can be provided regarding whether existing roofs can safely carry solar panels. To optimize the SSTR system, a computer code was created in MATLAB, which works effectively with rao algorithms and SAP2000-s Open Applicable Programming Interface (OAPI) features and allows repetitive analysis. For size optimization of the SSTR, which consists of 1728 elements, the roof system was divided into three and six groups. Changes in the weight of the SSTR system in the different groups were investigated. The optimum design of both the three-group and six-group SSTR systems with and without solar panels was performed. Based on the results obtained from this study, it was concluded that the rao-1 algorithm achieved more robust and stable results than the rao-2 algorithm in both three and six-group SSTR. The SSTR system divided into six group

关键词： Steel space truss roof (SSTR) SAP2000-OAPI rao algorithms Size optimization Solar panels

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