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A combinatorial optimization approach for geometric tolerance design considering various taxonomies

ENGINEERING optimization 2024年

作者： Mahesh, G. Saravanan, A. Saranathan Coll Engn Dept Mech Engn Thiruchirappalli India Sri Venkateswara Coll Engn Dept Mech Engn Chennai India

Design engineers play a vital role in conceptualizing and designing an assembly. The assembly is then disintegrated into several subassemblies and geometric tolerance symbols and values are allocated. Often, the allocated symbols and values are not realistic and conflict occurs as a result of improper assembly function, difficulty in manufacturing, and increased time and production costs. This article proposes a new design methodology. First, the functional behaviour of the assembly is predicted through finite element analysis, and the deformed geometry is expressed as a geometric tolerance zone constraint. Then, the functional assembly requirement is mathematically defined as a fit constraint. Simultaneously, rotational and planar machining constraints are developed. Finally, a combinatorial optimization problem is formulated to minimize the manufacturing cost. For optimal trade-offs, the bacterial foraging algorithm is applied to solve the combinatorial optimization problem;when it is applied to a mounted disc brake assembly, promising results are obtained.

关键词： bacterial foraging optimization algorithm combinatorial optimization finite element analysis geometric dimensioning and tolerancing (GD&T)

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optimization Design of Leg Structure Parameters of Bionic Quadruped Robot

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INTEGRATED FERROELECTRICS 2024年第8-9期240卷 1165-1178页

作者： Wu, Shenli Li, Yuhai Liu, Ling Wang, Yulong Yang, Peng Cheng, Zhen Ju, Jiapeng Wang, Junjie Yang, Lupeng He, Yangqian Xian Univ Sch Mech & Mat Engn Xian Peoples R China Shaanxi Key Lab Surface Engn & Remfg Xian Peoples R China Xian Key Lab Intelligent Addit Mfg Technol Xian Peoples R China

In order to improve the performance of bionic quadruped robot, some parameters of leg structure need to be optimized. The kinematic modeling of the leg mechanism of the bionic quadruped robot is analyzed, and the optimization goal is determined. The simulation analyses showed that the improved bacterial foraging algorithm had higher convergence accuracy, and was not easy to fall into local extremum. The improved multi-strategy bacteria foraging optimization algorithm was used to find the optimal leg structure parameters under the original working space. Numerical simulation verifies that the leg structure designed runs smoothly after optimization, which lays a foundation for further research.

关键词： Bionic quadruped robot leg mechanism parameter optimization bacterial foraging optimization algorithm multi-strategy fusion

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An ant approach to define product families and remanufacturing cells 10th

An ant approach to define product families and remanufacturi...

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10th IFAC Triennial Conference on Manufacturing Modelling, Management and Control (MIM)

作者： Mejia-Moncayo, Camilo Rojas, Alix E. Kenne, Jean-Pierre Hof, Lucas A. Ecole Technol Super Montreal PQ Canada Univ Ean Bogota Colombia

Cellular manufacturing has the potential to reduce the complexity of a manufacturing system, decoupling a complex factory in mini-factories or cells. This concept has an immense potential to be implemented in remanufacturing, which is a promising circular economy strategy which allows to recover the value added to used products. A remanufacturing system is complex, and this creates a barrier to its implementation. This study introduces an ant-based algorithm which addresses the formation of product/part families and remanufacturing cells. A novel approach used in this study mimics ant behaviour by using an artificial pheromone that reinforces similarities between machines and parts, allowing the definition of cells and families with superior performance over Genetic algorithms (GA) and bacterial foraging optimization algorithm (BFOA) in the solution of benchmark problems. Copyright (C) 2022 The Authors.

关键词： Ant Colony optimization Genetic algorithm bacterial foraging optimization algorithm Cell formation Remanufacturing

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Fuzzy Cognitive Map to Classify Plantar Foot Alterations

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IEEE LATIN AMERICA TRANSACTIONS 2021年第7期20卷 1992-2000页

作者： Andres Ramirez-Bautista, Julian Chaparro-Cardenas, Silvia L. Hernandez-Zavala, Antonio Magdalena Gallegos-Torres, Ruth Zequera, Martha Tovar-Barrera, Yosabad Pradilla-Gomez, Juan M. Adalberto Huerta-Ruelas, Jorge Fdn Univ San Gil UNISANGIL Res Dept Km2 Via San Gil Charala Santander Colombia Inst Politecn Nacl Ctr Invest Ciencia Aplicada & Tecnol Avanzada Av Cerro Blanco 141 Queretaro Mexico Univ Autonoma Queretaro Dept Nursing Queretaro 76010 Mexico Pontificia Univ Javeriana Elect Dept BASPI FootLaB Cra 7 40-62 Bogota Colombia Yosafisio Clin Queretaro Queretaro Mexico Pontificia Univ Javeriana Sch Med Cra 7 40-62 Bogota Colombia

The function of the back, hip, knee, ankle and other orthopedic alterations of the human body can be analyzed through plantar pressure distribution. The development of Clinical Decision Support Systems (CDSS) can handle the uncertainties present in biological data using different Artificial Intelligence techniques to obtain accurate and easy-to-use systems. This paper presents the application of a Fuzzy Cognitive Map (FCM) formulation, for knowledge extraction in the classification of human plantar foot alterations, with a relatively small and transparent model. The FCM is trained using the bacterial Search optimization algorithm (BFOA). One hundred and twenty-five volunteer subjects (aged 20-68 years) participated in the study. Classification of the foot into normal (n=31), flat (n=32), cavus type III (n=31) and cavus type IV (n=31) to train the system was performed by specialized physicians. The test was performed by walking on a FreeMed platform. The proposed method shows an accuracy rate of about 89% in the classification task and allows extracting information related to the important factors that the system considers to make a decision.

关键词： Foot Microorganisms optimization Medical services Fuzzy cognitive maps Behavioral sciences Biological system modeling Clinical decision support systems bacterial foraging optimization algorithm fuzzy cognitive maps optimization algorithms plantar data analysis

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Design, implementation, control and optimization of single stage pilot scale reverse osmosis process

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WATER SCIENCE AND TECHNOLOGY 2021年第10-11期84卷 2923-2942页

作者： Guna, G. Prabhakaran, D. Thirumarimurugan, M. Coimbatore Inst Technol Dept Chem Engn Coimbatore Tamil Nadu India

The closed loop configuration of a RO control system is depicted. The controller FOPID takes the input and calculates the frequency input of the final control element. The final control element is variable frequency drive whose flow rate changes with the change in frequency. When the flow rate changes pH and the turbidity of the RO process changes which is measured using the feedback mechanism in this case the pH meter and the turbidity sensors are the feedback mechanisms.

关键词： bacterial foraging optimization algorithm multi input multi output particle swarm optimization reverse osmosis various industrial applications

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An Efficient Genetic Hybrid PAPR Technique for 5G Waveforms

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CMC-COMPUTERS MATERIALS & CONTINUA 2021年第3期67卷 3283-3292页

作者： Kumar, Arun Albreem, Mahmoud A. Alsharif, Mohammed H. Jahid, Abu Uthansakul, Peerapong Nebhen, Jamel JECRC Univ Dept Elect & Commun Engn Jaipur 303905 Rajasthan India ASharqiyah Univ Dept Elect & Commun Engn Ibra 400 Oman Sejong Univ Coll Elect & Informat Engn Dept Elect Engn Seoul 05006 South Korea Univ Ottawa Dept Elect & Comp Engn Ottawa ON K1N 6N5 Canada Suranaree Univ Technol Sch Telecommun Engn Nakhon Ratchasima Thailand Prince Sattam bin Abdulaziz Univ Coll Comp Engn & Sci Alkharj 11942 Saudi Arabia

Non-orthogonal multiple access (NOMA) is a strong contender multicarrier waveform technique for the fifth generation (5G) communication system. The high peak-to-average power ratio (PAPR) is a serious concern in designing the NOMA waveform. However, the arrangement of NOMA is different from the orthogonal frequency division multiplexing. Thus, traditional reduction methods cannot be applied to NOMA. A partial transmission sequence (PTS) is commonly utilized to minimize the PAPR of the transmitting NOMA symbol. The choice phase aspect in the PTS is the only non-linear optimization obstacle that creates a huge computational complication due to the respective non-carrying sub-blocks in the unitary NOMA symbol. In this study, an efficient phase factor is proposed by presenting a novel bacterial foraging optimization algorithm (BFOA) for PTS (BFOA-PTS). The PAPR minimization is accomplished in a two-stage process. In the initial stage, PTS is applied to the NOMA signal, resulting in the partition of the NOMA signal into an act of sub-blocks. In the second stage, the best phase factor is generated using BFOA. The performance of the proposed BFOA-PTS is thoroughly investigated and compared to the traditional PTS. The simulation outcomes reveal that the BFOA-PTS efficiently optimizes the PAPR performance with inconsequential complexity. The proposed method can significantly offer a gain of 4.1 dB and low complexity compared with the traditional OFDM.

关键词： Wireless networks 5G non-orthogonal multiple access peak to average power ratio partial transmission sequence bacterial foraging optimization algorithm

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Clustering-based Optimized HEED protocols for WSNs using bacterial foraging optimization and fuzzy logic system

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SOFT COMPUTING 2019年第2期23卷 507-526页

作者： Gupta, Prateek Sharma, Ajay K. Dr BR Ambedkar Natl Inst Technol Dept Comp Sci & Engn Jalandhar Punjab India Natl Inst Technol Dept Comp Sci & Engn New Delhi India

Proficient clustering method has a vital role in organizing sensor nodes in wireless sensor networks (WSNs), utilizing their energy resources efficiently and providing longevity to network. Hybrid energy-efficient distributed (HEED) protocol is one of the prominent clustering protocol in WSNs. However, it has few shortcomings, i.e., cluster heads (CHs) variation in consecutive rounds, more work load on CHs, uneven energy dissipation by sensor nodes, and formation of hot spots in network. By resolving these issues, one can enhance HEED capabilities to a greater extent. We have designed variants of Optimized HEED (OHEED) protocols named as HEED-1 Tier chaining (HEED1TC), HEED-2 Tier chaining (HEED2TC), ICHB-based OHEED-1 Tier chaining (ICOH1TC), ICHB-based OHEED-2 Tier chaining (ICOH2TC), ICHB-FL-based OHEED-1 Tier chaining (ICFLOH1TC), and ICHB-FL-based OHEED-2 Tier chaining (ICFLOH2TC) protocols. In HEED1TC and HEED2TC protocols, we have used chain-based intra-cluster and inter-cluster communication in HEED, respectively, for even load balancing among sensor nodes and to avoid more work load on CHs. Furthermore, for appropriate cluster formation, minimizing CHs variation in consecutive rounds and reducing complex uncertainties, we have used bacterial foraging optimization algorithm (BFOA)-inspired proposed intelligent CH selection based on BFOA (ICHB) algorithm for CH selection in ICOH1TC and ICOH2TC protocols. Likewise, in ICFLOH1TC and ICFLOH2TC protocols, we have used novel fuzzy set of rules additionally for CH selection to resolve the hot spots problem, proper CH selection covering whole network, and maximizing the network lifetime to a great extent. The simulation results showed that proposed OHEED protocols are able to handle above-discussed issues and provided far better results in comparison to HEED.

关键词： Clustering Wireless sensor networks Load balancing Network lifetime HEED bacterial foraging optimization algorithm Fuzzy logic system

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A Multi-Objective Improved Cockroach Swarm algorithm Approach for Apartment Energy Management Systems

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INFORMATION 2023年第10期14卷 521页

作者： Alhasnawi, Bilal Naji Jasim, Basil H. Jasim, Ali M. Bures, Vladimir Alhasnawi, Arshad Naji Homod, Raad Z. Alsemawai, Majid Razaq Mohamed Abbassi, Rabeh Sedhom, Bishoy E. Al Furat Al Awsat Tech Univ Al Samawah Tech Inst Dept Elect Tech Kufa 66001 Iraq Basrah Univ Elect Engn Dept Basrah 61001 Iraq Univ Hradec Kralove Fac Informat & Management Hradec Kralove 50003 Czech Republic Al Muthanna Univ Coll Educ Pure Sci Dept Biol Samawah 66001 Iraq Basra Univ Oil & Gas Dept Oil & Gas Engn Basra 61004 Iraq Imam Jaafar Al Sadiq Univ Coll Informat Technol Dept Comp Tech Engn Al Muthanna 66002 Iraq Univ Hail Coll Engn Dept Elect Engn Hail City 81451 Saudi Arabia Mansoura Univ Fac Engn Elect Engn Dept Mansoura 35516 Egypt

The electrical demand and generation in power systems is currently the biggest source of uncertainty for an electricity provider. For a dependable and financially advantageous electricity system, demand response (DR) success as a result of household appliance energy management has attracted significant attention. Due to fluctuating electricity rates and usage trends, determining the best schedule for apartment appliances can be difficult. As a result of this context, the Improved Cockroach Swarm optimization algorithm (ICSOA) is combined with the Innovative Apartments Appliance Scheduling (IAAS) framework. Using the proposed technique, the cost of electricity reduction, user comfort maximization, and peak-to-average ratio reduction are analyzed for apartment appliances. The proposed framework is evaluated by comparing it with BFOA and W/O scheduling cases. In comparison to the W/O scheduling case, the BFOA method lowered energy costs by 17.75%, but the ICSA approach reduced energy cost by 46.085%. According to the results, the created ICSA algorithm performed better than the BFOA and W/O scheduling situations in terms of the stated objectives and was advantageous to both utilities and consumers.

关键词： multiple apartments loads cockroach swarm algorithm bacterial foraging optimization algorithm energy storage systems solar energy

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Parallel Job Scheduling Using bacterial foraging optimization for Heterogeneous Multi-cluster Environment 3rd

Parallel Job Scheduling Using Bacterial Foraging Optimizatio...

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3rd International Conference on Advanced Informatics for Computing Research (ICAICR)

作者： Kaur, Navjot Jaryal, Shikha Sharma, Sahil Chandigarh Univ Dept Comp Sci & Engn Ajitgarh India Lovely Profess Univ Dept Comp Sci & Engn Phagwara India

ISBN: (纸本)9789811501081;9789811501074

A variety of clusters are grouped together to form a multi-cluster environment which can tackle the computational needs of a system which cannot be addressed by a single cluster. Studying multi-cluster frameworks is turning challenging day by day as it requires contemporary tools to move alongside with rapidly development and enhanced complexity of one system. Job scheduling in considered as NP hard problem in parallel and distributed computing environments such as cluster, grid and clouds. The way jobs are scheduled by the scheduler is dependent on various factors like number of jobs, processor availability, arrival time etc. Metaheuristics techniques like Genetic algorithms, Ant Colony optimization, Artificial Bee Colony, Cuckoo Search, Firefly algorithm, Bat algorithm etc. are used by researchers to get near optimal solutions to job scheduling problems. This work addresses a scheduling problem with multiple objectives. The makespan and flowtime are minimized simultaneously solving the issue of optimal job allocation. This work also includes the detailed description of parallel computing and various types scheduling as well as scheduling environments. The performance of the multi-cluster environment is optimized by applying a novel meta-heuristic technique named bacterial foraging optimization algorithm. This algorithm has better convergence and is not affected by the size of problem. The proposed algorithm was evaluated for different job sets on 3 types of processor configurations. And the final values were compared to those of the existing algorithm. The results show that the proposed algorithm has performed better than the existing one and it can be concluded that the proposed algorithm is feasible and effective for optimal allocation of jobs.

关键词： Scheduling Metaheuristics Multi-cluster Co-allocation Genetic algorithm bacterial foraging optimization algorithm

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Analysis influence factors and forecast energy-related CO2emissions: evidence from Hebei

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ENVIRONMENTAL MONITORING AND ASSESSMENT 2020年第10期192卷 665-665页

作者： Sun, Wei Zhang, Junjian North China Elect Power Univ Dept Econ & Management Baoding 071000 Peoples R China

With economic development and the acceleration of urbanization, China's energy demand has gradually increased and brought a lot of energy-related CO(2)emissions. Energy-related CO(2)emissions are affected by a variety of factors. Quantifying the correlation between energy-related CO(2)and driving factors and constructing the driving factor system are conducive to predict the future energy-related CO(2)emissions and analyze the impact of driving factors. In this paper, the improved grey relational analysis (IGRA) was proposed to screen the influencing factors of energy-related CO(2)emissions considering the sample difference, and the factor analysis (FA) was used to reduce dimensionality of the influencing factors. Then, a carbon dioxide emission forecasting model based on the bacterial foraging optimization algorithm (BFO) and the least square support vector machine (LSSVM) was proposed. Empirical analysis results of Hebei show that the LSSVM optimized BFO significantly improves the accuracy of energy-related CO(2)emissions forecasting, and IGRA-FA-BFOLSSVM model is significantly better than BP, PSOBP, SVM, and LSSVM models. The mean absolute percentage error (MAPE) of the proposed model is 0.374%. The forecasting results of the supplementary case show that the model has better generalization ability. In addition, education and technological progress have proven to be important drivers of energy-related CO(2)emissions. Simultaneously, the research results can also offer more breakthrough points for policy makers to control carbon emissions.

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