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Optimum design of pin-jointed aluminum structures to AA-ASD using three Meta-heuristic algorithms

STRUCTURES 2023年 55卷 1406-1422页

作者： Aydogdu, Ibrahim Kilic, Vahide Akin, Alper Akdeniz Univ Dept Civil Engn Dumlupinar Blvd TR-07058 Antalya Turkiye Georgia Inst Technol Dept Comp Sci Atlanta GA USA

The main motivation for the current study is to investigate the weight advantage of the aluminum material on pin-jointed (truss) structures. For this purpose, optimization algorithms that find the most suitable structural designs with aluminum and steel materials have been developed. The criterion for finding optimum designs is to minimize the weight of the structure and met the design constraints. The constraint functions are derived according to the "American Institute of Steel Structures - Allowable Stress Design" (AISC-ASD) and "Aluminum Association - Allowable Stress Design" (AA-ASD) design codes. To increase the reliability of the obtained results, three robust methods named the artificial bee colony (ABC), Biogeography Based Optimization (BBO), and Brainstorming Optimization (BSO) algorithms are utilized for the proposed optimization problem. Four real-scale pin-jointed structures are used to research the weight advantage of aluminum structures. Three of these structures are real-scale truss structures that were previously optimized as made of steel elements by other metaheuristics. The last pin-jointed structure is the real case project which is modeled and optimized for the first time in this study. According to the obtained results, significant optimal design differences are determined between the construction of the tackled pin-jointed structures with aluminum or steel structural elements. Considering other advantages such as transportation and maintenance, using aluminum material in pin-jointed structures is recommended.

关键词： Optimum structural design Aluminum structures Meta-heuristic techniques artificial bee colony algorithm Brain Storming algorithm Biogeography Based Optimization

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Binary artificial bee colony algorithms for {0-1} Advertisement Problem 6

Binary Artificial Bee Colony Algorithms for {0-1} Advertisem...

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6th International Conference on Electrical and Electronics Engineering (ICEEE)

作者： Aytimur, Asuman Babayigit, Bilal Erciyes Univ Grad Sch Nat & Appl Sci Kayseri Turkey Erciyes Univ Dept Comp Engn Kayseri Turkey

ISBN: (纸本)9781728139104

A company wants to select magazine publishers for advertising. It is vital for the company to maximize the number of subscribers reached by advertising while not exceeding its advertising budget. To determine which publishers to select can be considered as an advertisement optimization problem and can be solved by using binary metaheuristics. artificial bee colony (ABC) is one of the most popular metaheuristics in the field of swarm intelligence and has been widely used in numerical optimization and engineering applications. To solve binary advertisement problem, three binary variants of ABC algorithm are proposed in this paper. The first binary variant is based on Sigmoid transfer function and indicated as sigABC, while the second and the third binary variants are based on exclusive OR (xor) and crossover genetic operators, respectively, and are indicated as xorABC and crossoverABC. The proposed binary ABC variants are evaluated using 20 instance-dataset. The comparative experimental results show the superior performance of the crossoverABC and sigABC methods compared to xorABC technique.

关键词： optimization binary optimization artificial bee colony algorithm advertising problem performance evaluation

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Optimization of parallel test task scheduling with constraint satisfaction

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JOURNAL OF SUPERCOMPUTING 2023年第7期79卷 7206-7227页

作者： Gao, Jinsheng Zhu, Xiaomin Zhang, Runtong Beijing Jiaotong Univ Sch Mech Elect & Control Engn 3 Shangyuancun Beijing 100044 Peoples R China Beijing Jiaotong Univ Sch Econ & Management 3 Shangyuancun Beijing 100044 Peoples R China

Parallel test task scheduling is an efficient way to shorten the final makespan of several huge test projects. Put simply, a set of test tasks should be processed on several unrelated resources, and several test tasks must satisfy the predetermined technological test order. The objective of the investigated problem is to minimize the makespan. To tackle the problem, a recursive search artificial bee colony algorithm (RS-ABC) is proposed. The recursive search procedure is developed to obtain a series of implied sequences of the predetermined technological test order on the recursive tree. The artificial bee colony (ABC) algorithm is devised to find the schedule with minimum makespan by utilizing the implied sequences. To evaluate the performance of RS-ABC, small and large size instance problems are solved, and the results are compared with those of the latest algorithm and one state-of-the-art solver. The experimental results show that RS-ABC is encouraging in solving the parallel test task scheduling problem. This work can help users design an effective test plan for the shortest completion time.

关键词： Test task scheduling problem Constraint satisfaction artificial bee colony algorithm Recursive search

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Multi-objective energy saving optimization of residential buildings based on MABC-BP

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ENERGY REPORTS 2023年 9卷 922-929页

作者： Wang, Ning Xian Siyuan Coll 28 Shuian Rd Xian Shaanxi Peoples R China

In order to better satisfy the energy saving requirements of residential buildings in China, this paper proposes a model based on MABC-BP, which performs multi-objective optimization of residential building energy saving to further improve the energy-saving effect. Firstly, in view of the limitations of the traditional ABC algorithm, it was proposed to utilize the particle swarm algorithm to improve the artificial bee colony algorithm, so as to improve the optimal search performance of the traditional bee colony. Then, the parameters of back propagation (BP) neural network were optimized by the improved artificial bee colony algorithm. Finally, using the parameters of a building's exterior wall, roof and ground as a base, the BP neural network was trained, and the trained BP neural network was utilized to realize the multi-objective prediction of residential building energy saving. Simulation results show that the BP neural network with optimized parameters has good prediction results with an average error value of 0.005. Compared with the original BP neural network, the optimized BP network has better convergence and solution. Compared with the ABC-BP algorithm model, the optimized BP algorithm model achieves better optimization results when optimizing the optimization target selected by the experiment. The above simulation results verify the superiority of the design and the necessity of optimization in this paper, which has certain application value. (c) 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Energy saving of buildings Multi-objective optimization artificial bee colony algorithm BP neural network

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Drone-as-a-Service (DaaS) for COVID-19 self-testing kits delivery in smart healthcare setups: A technological perspective

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ICT EXPRESS 2023年第4期9卷 748-753页

作者： Munawar, Hafiz Suliman Akram, Junaid Khan, Sara Imran Ullah, Fahim Choi, Bong Jun Univ New South Wales Sch Built Environm Sydney NSW 2052 Australia Univ Sydney Sch Comp Sci Sydney NSW 2006 Australia Univ Southern Queensland Sch Surveying & Built Environm Springfield Cent Qld 4300 Australia Soongsil Univ Coll Informat Technol Sch Comp Sci & Engn Seoul South Korea

Drones have gained increasing attention in the healthcare industry for mobility and accessibility to remote areas. This perspective-based study proposes a drone-based sample collection system whereby COVID-19 self-testing kits are delivered to and collected from potential patients. This is achieved using the drone as a service (DaaS). A mobile application is also proposed to depict drone navigation and destination location to help ease the process. Through this app, the patient could contact the hospital and give details about their medical condition and the type of emergency. A hypothetical case study for Geelong, Australia, was carried out, and the drone path was optimized using the artificial bee colony (ABC) algorithm. The proposed method aims to reduce person-to-person contact, aid the patient at their home, and deliver any medicine, including first aid kits, to support the patients until further assistance is provided. artificial intelligence and machine learning-based algorithms coupled with drones will provide state-of-the-art healthcare systems technology.& COPY;2022 The Author(s). Published by Elsevier B.V. on behalf of The Korean Institute of Communications and Information Sciences. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Drone as a Service (DaaS) artificial bee colony algorithm Path planning Drone delivery Smart healthcare COVID-19

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The Problem of Premature Convergence in Engineering Optimization Problems 12th

The Problem of Premature Convergence in Engineering Optimiza...

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12th International Conference on Mathematical Modeling in Physical Sciences (IC-MSQUARE)

作者： Ponomareva, K. A. Rozhnov, I. P. Kazakovtsev, L. A. Siberian Fed Univ Krasnoyarsk Russia Reshetnev Siberian State Univ Sci & Technol Krasnoyarsk Russia

ISBN: (纸本)9783031529672;9783031529658;9783031529641

Optimization is a complex problem in mathematics and engineering applications, which has so far been solved using a variety of approaches based on heuristic methods, methods inspired by nature, methods based on the principles of fuzzy logic, and methods based on rigorous mathematical tools. The most important task is to combine various methods of modeling, optimization, design and management of complex systems within the framework of an integrated approach for a holistic description of the phenomena under study. The objective function of optimization problems combines continuous and discrete variables and various constraints, which indicates its complexity, as well as difficulties in solving such problems. algorithms that are used in solving optimization problems may suffer from premature convergence when they stop at the optimal solution earlier than required. Based on the problem of premature convergence, a hybrid approach combining a sine-cosine algorithm (SCA) and an artificial bee colony algorithm (ABC) was proposed in the paper. In the proposed algorithm, called the hybrid sine-cosine algorithm (HSCA), both algorithms are executed alternately until the convergence criterion is satisfied.

关键词： Optimization Premature convergence Hybrid approach Sine-cosine algorithm artificial bee colony algorithm

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Brain Effective Connectivity Analysis Facilitates the Treatment Outcome Expectation of Sound Therapy in Patients With Tinnitus

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IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING 2023年 31卷 1158-1166页

作者： Lv, Han Liu, Jinduo Chen, Qian Zhang, Zuozhen Wang, Zhaodi Gong, Shusheng Ji, Junzhong Wang, Zhenchang Capital Med Univ Beijing Friendship Hosp Dept Radiol Beijing 100050 Peoples R China Beijing Univ Technol Coll Comp Sci & Technol Fac Informat Technol Beijing Municipal Key Lab Multimedia & Intelligent Beijing 100124 Peoples R China Capital Med Univ Beijing Friendship Hosp Dept Otolaryngol Head & Neck Surg Beijing 100050 Peoples R China

Tinnitus is associated with abnormal functional connectivity of multiple regions of the brain. However, previous analytic methods have disregarded information on the direction of functional connectivity, leading to only a moderate efficacy of pretreatment planning. We hypothesized that the pattern of directional functional connectivity can provide key information on treatment outcomes. Sixty-four participants were enrolled in this study: eighteen patients with tinnitus were categorized into the effective group, twenty-two patients into the ineffective group, and twenty-four healthy participants into the healthy control group. We acquired resting-state functional magnetic resonance images prior to sound therapy and constructed an effective connectivity network of the three groups using an artificial bee colony algorithm and transfer entropy. The key feature of patients with tinnitus was the significantly increased signal output of the sensory network, including the auditory, visual, and somatosensory networks, and parts of the motor network. This provided critical insights into the gain theory of tinnitus development. The altered pattern of functional information orchestration, represented by a higher degree of hypervigilance-driven attention and enhanced multisensory integration, may explain poor clinical outcomes. The activated gating function of the thalamus is one of the key factors for a good prognosis in tinnitus treatment. We developed a novel method for analyzing effective connectivity, facilitating an understanding of the tinnitus mechanism and treatment outcome expectation based on the direction of information flow.

关键词： Functional magnetic resonance imaging Medical treatment artificial bee colony algorithm Biomedical imaging Time series analysis Measurement Hospitals Tinnitus sound therapy fMRI brain network effective connectivity

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Stock price prediction using improved extreme learning machine methods during the Covid-19 pandemic and selection of appropriate prediction method

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KYBERNETES 2023年第10期52卷 4081-4109页

作者： Boru Ipek, Asli Adana Alparslan Turkes Sci & Technol Univ Dept Ind Engn Adana Turkey

Purpose Coronavirus disease (Covid-19) has created uncertainty in all countries around the world, resulting in enormous human suffering and global recession. Because the economic impact of this pandemic is still unknown, it would be intriguing to study the incorporation of the Covid-19 period into stock price prediction. The goal of this study is to use an improved extreme learning machine (ELM), whose parameters are optimized by four meta-heuristics: harmony search (HS), social spider algorithm (SSA), artificial bee colony algorithm (ABCA) and particle swarm optimization (PSO) for stock price prediction. Design/methodology/approach In this study, the activation functions and hidden layer neurons of the ELM were optimized using four different meta-heuristics. The proposed method is tested in five sectors. Analysis of variance (ANOVA) and Duncan's multiple range test were used to compare the prediction methods. First, ANOVA was applied to the test data for verification and validation of the proposed methods. Duncan's multiple range test was used to identify a suitable method based on the ANOVA results. Findings The main finding of this study is that the hybrid methodology can improve the prediction accuracy during the pre and post Covid-19 period for stock price prediction. The mean absolute percent error value of each method showed that the prediction errors of the proposed methods were all under 0.13106 in the worst case, which appears to be a remarkable outcome for such a difficult prediction task. Originality/value The novelty of this study is the use of four hybrid ELM methods to evaluate the automotive, technology, food, construction and energy sectors during the pre and post Covid-19 period. Additionally, an appropriate method was determined for each sector.

关键词： Extreme learning machine Harmony search Social spider algorithm artificial bee colony algorithm Particle swarm optimization

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Pharmaceutical R &D portfolio optimization with minimum borrowed capital based on fuzzy set theory

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SOFT COMPUTING 2023年第6期27卷 2809-2826页

作者： Zhang, Mingyu Liu, Yong-Jun Southern Med Univ Affiliated Hosp 3 Dept Cardiovasc Med Guangzhou 510063 Guangdong Peoples R China South China Univ Technol Sch Business Adm Guangzhou 510641 Peoples R China

Due to long lead times, uncertain outcomes and lack of enough historical data, pharmaceutical research and development (R &D) portfolio selection is a often very complex decision issue. The aim of this paper is to investigate pharmaceutical R &D portfolio selection with unavailable and unreliable project information, where the borrowed capital is allowed. Based on fuzzy set theory, we propose a pharmaceutical R &D portfolio optimization model with minimum borrowed capital by taking into account corporate strategy in developing new products, scarcity of resources, lack of investment budget and cardinality constraint. In the proposed model, the pharmaceutical R &D company is assumed to achieve the objectives of maximizing terminal wealth and minimizing the cumulative borrowed capital over the whole investment horizon. Then, we transform the proposed bi-objective model into the corresponding single-objective model by using the weighted sum approach and employ the modified artificial bee colony (MABC) algorithm to solve the transformed model. Finally, we provide a numerical example to illustrate the application of our model.

关键词： Pharmaceutical project RD portfolio selection Minimum borrowed capital Fuzzy programming model artificial bee colony algorithm

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Performance analysis of vehicle-to-everything communication using internet of LoRa computing for intelligent transportation system

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INTELLIGENT DECISION TECHNOLOGIES-NETHERLANDS 2023年第2期17卷 577-594页

作者： Vinodhini, M. Rajkumar, Sujatha Vellore Inst Technol Sch Elect Engn Vellore Tamil Nadu India

Intelligent Transportation System (ITS) is an emerging field nowadays that is widely utilized to improve safety measures, avoid abnormalities, and traffic flow control, and also develops the environment without hassle. So far, the deployment of sensors into vehicles and the analyzing the vehicular parameters towards the smart city applications have been achieved by the integration of LoRa-based vehicular communication. However, trust in previous design architecture should need efficient transmission, robustness, and energy efficiency. To overcome the challenges, the proposed system designed the Internet of LoRa computing enabled vehicular communication with high reliability by offering the optimization technique namely an Enhanced artificial bee colony (EABC) algorithm for the localization scheme. The proposed framework consists of two sections. First, observe the objects nearby vehicles using an ultrasonic sensor that is equipped in the Arduino module with a LoRa shield. The second work contributes to the evaluation of performance metrics of vehicular communication in the sensing region with a minimum delay of two seconds using MathWorks simulation. The article designed the VANET, which utilized the LoRa architecture for Vehicle to Everything communication, and pointed out the position of the sensor nodes using a localization scheme (EABC algorithm), comparing the proposed EABC algorithm with the other optimization techniques viz Particle Swarm Optimization and Genetic algorithms in the dense nodes and it achieves 25% variation in minimizing the position error at a certain speed. Further, find the system performance by calculating the BER (Bit Error Rate) in both coherent and non-coherent with varying speeds of the vehicle and router connections and it achieves 40% variation in efficiency and realizes the network coverage in terms of the position of the vehicle in the way the proposed framework achieves the high accuracy in overall system throughput.

关键词： artificial bee colony algorithm bit error rate LoRa Computing vehicle to everything communication (V2X) Localization Error optimization technique vehicular parameter

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