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Dynamic Modeling and Optimization of Tension Distribution for a Cable-Driven Parallel Robot

APPLIED SCIENCES-BASEL 2024年第15期14卷 6478页

作者： Wang, Kai Hu, Zhong Hua Zhang, Chen Shuo Han, Zhi Wei Deng, Chao Wen Liaocheng Univ Sch Mech & Automot Engn Liaocheng 252000 Peoples R China

Cable-driven parallel robots (CDPRs) have been gaining much attention due to their many advantages over traditional parallel robots or serial robots, such as their markedly large workspace and lightweight design. However, one of the main issues that needs to be urgently solved is the tension in the distribution of CDPRs due to two reasons. The first is that a cable can only be stretched but not compressed, and the other is the redundancy of the parallel robot. To address the problem, an optimization method for tension distribution is proposed in the paper. The structural design of the parallel robot is first discussed. The dynamics model of the parallel robot is established by the Newton-Euler method. Based on the minimum variance of cables' tension, an optimization method of tension distribution is presented for the parallel robot. Furthermore, the tension extreme average term is introduced in the optimization method, and the firefly algorithm is applied to obtain the optimal solution for tension distribution. Finally, the proposed approach is tested in the simulation case where the end-effector of the robot moves in a circular motion. Simulation results demonstrate that the uniformity and continuity of tension are both outstanding for the proposed method. In contrast with traditional solving methods, the efficiency of this method is largely improved.

关键词： cable-driven parallel robot dynamics modeling cable tension optimization firefly algorithm

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Genetické algoritmy

Genetické algoritmy

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作者： Masárová, Mária Brno University of Technology

Táto práca sa zaoberá genetickými algoritmami, ich terminológiou a využitím. Popisuje rôzne problémy, ktoré sa dajú pomocou genetických algoritmov riešiť. V pr... 详细信息

Táto práca sa zaoberá genetickými algoritmami, ich terminológiou a využitím. Popisuje rôzne problémy, ktoré sa dajú pomocou genetických algoritmov riešiť. V práci sú taktiež predstavené rôzne algoritmy skupinovej inteligencie, pričom algoritmus svetlušiek slúži aj na porovnanie efektivity medzi ním a genetickým algoritmom. Hlavnou úlohou tejto práce je vykonať experimenty s tromi optimalizačnými úlohami, konkrétne sú to, problém obchodného cestujúceho, splniteľnosť logických formúl a hľadanie extrému funkcie.

关键词： genetický algoritmus chromozóm inteligencia skupiny algoritmus svetlušiek problém obchodného cestujúceho problém splniteľnosti logických formúl hľadanie extrému funkcie genetic algorithm chromosome swarm intelligence firefly algorithm travellin

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An ANN-entropy-FA model for prediction and optimization of biodiesel-based engine performance

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APPLIED SOFT COMPUTING 2023年 133卷

作者： Chaki, Sudipto Biswas, Tapas Kumar MCKV Inst Engn Automobile Engn Dept Howrah 711204 India

The present work incorporated full factorial experimentation for performance analysis of Nahar oil-based biodiesel in a four-stroke diesel engine. The controllable input parameters are engine revolution per minute (N), engine load (EL), and blend mixture (BM) respectively. The measured engine performance characteristics are brake thermal efficiency (BTE), brake-specific fuel consumption (BSFC), and exhaust gas temperature (ET). Prediction and optimization of engine output characteristics have been computed through the ANN-entropy-FA hybrid model. In this model, a trained Artificial Neural Network (ANN) is used to compute the objective function value during optimization using the firefly algorithm (FA). The maximum absolute percentage error of trained ANN during the prediction of output parameters is found as less than 3%. The entropy method is employed to determine the weight of output parameters in the combined objective function. ANN-FA optimization determined minimum BSFC = 0.386 kg/kW-hr, maximum BTE = 24.60%, and minimum ET = 163.24 degrees C with corresponding operational input parameter setting of low engine rpm (1250 rpm), medium engine load (15.20 kg), and a high percentage of blend mixture (approximate to 25%) respectively. A significantly low absolute % error of around 1.75% during experimental validation of ANN-FA optimized output indicates the efficacy of the proposed model.(c) 2022 Elsevier B.V. All rights reserved.

关键词： Artificial neural networks firefly algorithm Entropy method Bio-diesel Nahar oil Engine performance optimization

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Bi-level planning method of urban electric vehicle charging station considering multiple demand scenarios and multi-type charging piles

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JOURNAL OF ENERGY STORAGE 2022年 48卷

作者： Liu, Xiaoou Tianjin Elect Power Design Inst CO LTD China Energy Engn Grp 437 Beijing Tianjin Highway Tianjin 300400 Peoples R China

The planning of electric vehicle (EV) charging stations with a comprehensive consideration of the multi-type charging demands and the acceptance capacities of the distribution network is of great significance to the development of EVs and the transformation of the distribution network. At present, most studies determine EV charging needs based on user travel paths, but they have not taken the behavior uncertainty of EV owners and the diversity of their demands into account. The probability statistics on the massive data of EV travels can help excavating the travel and charging rules of the EVs. Based on user travel data, the Markov chain and the roulette method are used to simulates EV charging requirements in three scenarios considering various uncertain factors, and then a K-means clustering algorithm is used to obtain charging requirements in three scenarios. On this basis, a bi-level planning model of charging station with maximum annual profit of construction and operation and minimum comprehensive charging cost of EVs as multi-objective function is proposed. This model also considers many constraints, such as cooperative service of multi-type charging piles, acceptance capacity of distribution network and so on. The upper model takes the location and capacity of charging station planning as control variables. The lower model was established to minimize the comprehensive charging cost of EVs. The upper and lower models interact and couple with each other. The solution process of bi-level planning model was designed through using firefly algorithm. Finally, taking the urban area of a city in China as the planning area, and the transportation network coupled with the IEEE 69 node distribution network as an example, the validity and correctness of the proposed model are verified.

关键词： Electric vehicle Uncertainty of charging demand Multi-type charging piles Bi-level charging station planning firefly algorithm

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A Review of Nature-Inspired algorithms

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JOURNAL OF BIONIC ENGINEERING 2010年第sup1期7卷 S232-S237页

作者： Zang, Hongnian Zhang, Shujun Hapeshi, Kevin Univ Gloucestershire Dept Comp Cheltenham GL50 2RH Glos England

The study of bionics bridges the functions, biological structures and organizational principles found in nature with our modern technologies, and numerous mathematical and metaheuristic algorithms have been developed along with the knowledge transferring process from the lifeforms to the human technologies. Output of bionics study includes not only physical products, but also various computation methods that can be applied in different areas. People have learnt from biological systems and structures to design and develop a number of different kinds of optimisation algorithms that have been widely used in both theoretical study and practical applications. In this paper, a number of selected nature-inspired algorithms are systematically reviewed and analyzed. Though the paper is mainly focused on the original principle behind each of the algorithm, their applications are also discussed.

关键词： bionic optimization algorithms review Ant Colony Optimization Bees algorithm Genetic algorithm firefly algorithm

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Submerged Arches Optimal Design With a Multi-Method Ensemble Meta-Heuristic Approach

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IEEE ACCESS 2020年 8卷 215057-215072页

作者： Perez-Aracil, Jorge Camacho-Gomez, Carlos Hernandez-Diaz, Alejandro Manuel Pereira, Emiliano Salcedo-Sanz, Sancho Catholic Univ Murcia UCAM Civil Engn Dept Murcia 30107 Spain Univ Politecn Madrid Dept Comp Syst Engn Madrid 28040 Spain Univ La Laguna Continuum Mech & Struct Anal Santa Cruz De Tenerife 38205 Spain Univ Alcala Dept Signal Proc & Commun Alcala De Henares 28871 Spain

This paper discusses the performance of a multi-method ensemble meta-heuristic for optimization in a problem of submerged arches design. Specifically, the Coral Reefs Optimization algorithm with Substrate Layers (CRO-SL) is proposed. It is a multi-method evolutionary ensemble which combines different searching structures in a single population. In this case, some novel searching strategies such as the firefly algorithm and the Water Wave Optimization have been proposed to be included in the CRO-SL algorithm. In addition, some other traditional searching structures such as classical crossovers, Gaussian mutation operators, Harmony search and Differential Evolution search strategies have also been implemented in the algorithm. The proposed CRO-SL has been applied to the optimal design of submerged arches in deep waters, considering several design aspects such as the bending moment of the beam and the airspace of the arch. The evaluation of the submerged arches quality is obtained after the simulation of the arches by means of a finite elements computational software, which has been hybridized with the CRO-SL algorithm in the process of fitness calculation. It will be shown that the CRO-SL is able to obtain excellent solutions to submerged arches design problems. For this, the computational performance of each substrate (different searching strategies) in this optimization problem will be discussed and the results obtained will be compared with those in alternative works for similar experiments.

关键词： Optimization Shape Bending Substrates Search problems Signal processing algorithms Heuristic algorithms Submerged arches design ensemble methods evolutionary algorithms firefly algorithm water wave optimization algorithm

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Uncertainty assessment of the multilayer perceptron (MLP) neural network model with implementation of the novel hybrid MLP-FFA method for prediction of biochemical oxygen demand and dissolved oxygen: a case study of Langat River

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ENVIRONMENTAL EARTH SCIENCES 2017年第14期76卷 503-503页

作者： Raheli, Bahare Aalami, Mohammad Taghi El-Shafie, Ahmed Ghorbani, Mohammad Ali Deo, Ravinesh C. Univ Tabriz Dept Civil Engn Tabriz Iran Univ Malaya Dept Civil Engn Fac Engn Kuala Lumpur Malaysia Univ Tabriz Dept Water Engn Tabriz Iran Near East Univ Fac Engn Mersin 10 TR-99138 Nicosia North Cyprus Turkey Univ Southern Queensland Inst Agr & Environm Sch Agr Computat & Environm Sci Springfield Qld 4300 Australia

Accurate prediction of the chemical constituents in major river systems is a necessary task for water quality management, aquatic life well-being and the overall healthcare planning of river systems. In this study, the capability of a newly proposed hybrid forecasting model based on the firefly algorithm (FFA) as a metaheuristic optimizer, integrated with the multilayer perceptron (MLP-FFA), is investigated for the prediction of monthly water quality in Langat River basin, Malaysia. The predictive ability of the MLP-FFA model is assessed against the MLP-based model. To validate the proposed MLP-FFA model, monthly water quality data over a 10-year duration (2001-2010) for two different hydrological stations (1L04 and 1L05) provided by the Irrigation and Drainage Ministry of Malaysia are used to predict the biochemical oxygen demand (BOD) and dissolved oxygen (DO). The input variables are the chemical oxygen demand (COD), total phosphate (PO4), total solids, potassium (K), sodium (Na), chloride (Cl), electrical conductivity (EC), pH and ammonia nitrogen (NH4-N). The proposed hybrid model is then evaluated in accordance with statistical metrics such as the correlation coefficient (r), root-mean-square error, % root-mean-square error and Willmott's index of agreement. Analysis of the results shows that MLP-FFA outperforms the equivalent MLP model. Also, in this research, the uncertainty of a MLP neural network model is analyzed in relation to the predictive ability of the MLP model. To assess the uncertainties within the MLP model, the percentage of observed data bracketed by 95 percent predicted uncertainties (95PPU) and the band width of 95 percent confidence intervals (d-factors) are selected. The effect of input variables on BOD and DO prediction is also investigated through sensitivity analysis. The obtained values bracketed by 95PPU show about 77.7%, 72.2% of data for BOD and 72.2%, 91.6% of data for DO related to the 1L04 and 1L05 stations, respectively. The d-fa

关键词： BOD and DO Multilayer perceptron (MLP) firefly algorithm Hybrid model Uncertainty assessment River water quality

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Mean-variance portfolio optimization using machine learning-based stock price prediction

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APPLIED SOFT COMPUTING 2021年 100卷 106943-106943页

作者： Chen, Wei Zhang, Haoyu Mehlawat, Mukesh Kumar Jia, Lifen Capital Univ Econ & Business Sch Management & Engn Beijing Peoples R China Univ Delhi Dept Operat Res Delhi India

The success of portfolio construction depends primarily on the future performance of stock markets. Recent developments in machine learning have brought significant opportunities to incorporate prediction theory into portfolio selection. However, many studies show that a single prediction model is insufficient to achieve very accurate predictions and affluent returns. In this paper, a novel portfolio construction approach is developed using a hybrid model based on machine learning for stock prediction and mean-variance (MV) model for portfolio selection. Specifically, two stages are involved in this model: stock prediction and portfolio selection. In the first stage, a hybrid model combining eXtreme Gradient Boosting (XGBoost) with an improved firefly algorithm (IFA) is proposed to predict stock prices for the next period. The IFA is developed to optimize the hyperparameters of the XGBoost. In the second stage, stocks with higher potential returns are selected, and the MV model is employed for portfolio selection. Using the Shanghai Stock Exchange as the study sample, the obtained results demonstrate that the proposed method is superior to traditional ways (without stock prediction) and benchmarks in terms of returns and risks. (c) 2020 Elsevier B.V. All rights reserved.

关键词： Portfolio selection Stock prediction eXtreme Gradient Boosting firefly algorithm Mean-variance model

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Solving constrained economic electrical energy generation and CO2 emission dispatch using hybrid algorithm

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ENVIRONMENTAL TECHNOLOGY & INNOVATION 2021年 24卷

作者： Jebaseelan, S. D. Sundarsingh Selvan, N. B. Muthu Kumar, C. Kalaimurugan, A. Srujana, A. Ravi, C. N. Sathyabama Inst Sci & Technol Dept EEE Chennai Tamil Nadu India SSN Coll Engn Dept Elect & Elect Engn Chennai Tamil Nadu India M Kumarasamy Coll Engn Dept Elect & Elect Engn Karur 639113 India Agni Coll Technol Dept Elect & Elect Engn Chennai Tamil Nadu India Vidya Jyothi Inst Technol Dept Elect & Elect Engn Hyderabad India

Carbon foot print is latest hot discussion and all the countries are started the initiation to minimize it. One of the main carbon emission industries is thermal electrical power generating company. For the social welfare the emission has to minimize to maximum extend. The electrical power generation company as well needs to minimize the generation cost for the better operation. Minimizing carbon foot print and minimal power generation cost are opposite to each other. When the optimization focused to minimize the generation cost it lead to increase the carbon foot print and vice versa. This paper addresses the constraint the optimization problem which needs the objective of reducing both emission and generation cost. Electrical power generating cost of thermal power plant is nonlinear and non-convex in nature. Likewise the emission produced by the thermal power plant is complex mathematical problem. Intelligent algorithms are best suitable to solve these types of practical problems. In this paper hybrid of firefly algorithm and differential evolution technique is adopted to find the constrained emission minimization and cost minimization. firefly algorithm works based on brightness, attractiveness and movement and there is no mutation process. Differential evolution technique has better mutation process and hence this mutation operation of differential evolution technique is introduced with firefly algorithm for the hybrid operation and to get better result. The research work is aimed to reduce the carbon foot print as well the generation cost of the power system. (C) 2021 The Authors. Published by Elsevier B.V.

关键词： Economic dispatch Emission dispatch Intelligent algorithm Hybrid algorithm firefly algorithm Differential evaluation Constraint optimization

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Comparing the performance of metaheuristics on the Transit Network Frequency Setting Problem

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JOURNAL OF INTELLIGENT TRANSPORTATION SYSTEMS 2024年

作者： Aksoy, Ilyas Cihan Mutlu, Mehmet Metin Karamanoglu Mehmetbey Univ Civil Engn Dept Karaman Turkiye Aydin Adnan Menderes Univ Civil Engn Dept Aydin Turkiye

The Transit Network Frequency Setting Problem (TNFSP), an NP-Hard combinatorial optimization problem, has been frequently addressed in previous investigations, most of which employ metaheuristics. However, previous studies have not investigated to determine the best metaheuristic and the most effective parameter values for the TNFSP. This study aims to fill this research gap by comprehensively comparing five well-known metaheuristics, namely, Artificial Bee Colony, Differential Evolution, firefly algorithm, Genetic algorithm, and Particle Swarm Optimization, on the TNFSP. The best parameter configurations for employed metaheuristics are determined using the brute-force method. The comparative results not only highlight the significance of metaheuristic selection in addressing the TNFSP but also reveal the importance of choosing the most suitable parameter values for these metaheuristics. Furthermore, the metaheuristics are performed on the transit networks obtained from previously published studies for comparison purposes. The results demonstrate that the metaheuristics achieve better frequency sets than the original frequency sets of the used transit networks.

关键词： artificial bee colony differential evolution firefly algorithm genetic algorithm particle swarm optimization transit network frequency setting problem

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