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Cardiac arrest prediction in smokers using enhanced artificial bee colony algorithm with stacked autoencoder model

COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING 2023年第10期26卷 1220-1235页

作者： Banu, Umera Vanjerkhede, Kalpana Khaja Bandanawaz Coll Engn Dept Biomed Engn Kalaburagi India Poojya Doddappa Appa PDA Coll Engn Dept Elect & Instrumentat Engn Kalaburagi India

In the recent times, the cardiac arrest is a severe heart disease, which results in millions of annual casualties. In this article, the heart rate variability (HRV) parameters are used for predicting cardiac arrest in smokers based on the deep learning techniques. First, the input data is collected from MITU Skillogies dataset, which consists of 1562 smoker and non-smoker instances with 19 HRV input attributes/features. After data collection, the enhanced artificial bee colony algorithm (EABC) is developed for feature selection. The EABC algorithm includes two new multi-objective functions for decreasing the number of attributes in the MITU Skillogies dataset. This mechanism superiorly reduces the burden of computational complexity and improves classification accuracy. Further, the selected attributes are given to the stacked autoencoder classifier for non-cardiac arrest and cardiac arrest classification in smokers for early diagnosis. The extensive experiment showed that the EABC with stacked autoencoder model obtained 96.26% of classification accuracy, which is better related to the traditional machine learning models.

关键词： artificial bee colony algorithm cardiac arrest prediction heart rate variability machine learning stacked autoencoder model

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Application of artificial bee colony algorithm on surface wave data

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COMPUTERS & GEOSCIENCES 2015年第Oct.期83卷 219-230页

作者： Song, Xianhai Gu, Hanming Tang, Li Zhao, Sutao Zhang, Xueqiang Li, Lei Huang, Jianquan China Univ Geosci Hubei Subsurface Multiscale Imaging Key Lab Inst Geophys & Geomat Wuhan 430074 Hubei Peoples R China

artificial bee colony (ABC) algorithm is applied to invert surface wave phase velocities. The ABC algorithm, one of swarm intelligence-based algorithms, is inspired from the particular intelligent foraging behavior of a honeybee swarm in nature. To facilitate convergence to an optimal solution, global exploration and local exploitation processes are carried out simultaneously in a robust ABC search process. Using synthetic and observed Rayleigh wave data, we examined the effectiveness and applicability of the ABC scheme in deducing an S-wave velocity profile for near-surface applications. Furthermore, we compared the performance of ABC to those of genetic algorithm (GA) and particle swarm optimization (PSO). We demonstrated that the ABC algorithm outperforms the standard binary-coded GA and the basic PSO, and it can be effectively used to interpret surface wave dispersion data with the great advantage of employing fewer control parameters. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： artificial bee colony algorithm Genetic algorithm Particle swarm optimization Surface waves Dispersion curves

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Combined Heat and Power Units Sizing and Energy Cost Optimization of a Residential Building by Using an artificial bee colony algorithm

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IEEE ACCESS 2020年 8卷 218289-218303页

作者： Habib, Habib Ur Rahman Wang, Shaorong Waqar, Asad Farhan, Bashar Sakeen Kotb, Kotb M. Kim, Yun-Su Huazhong Univ Sci & Technol Sch Elect & Elect Engn State Key Lab Adv Electromagnet Engn & Technol Wuhan 430074 Peoples R China Univ Engn & Technol Taxila Fac Elect & Elect Engn Dept Elect Engn Taxila 47050 Pakistan Bahria Univ Dept Elect Engn Islamabad 44000 Pakistan Al Iraqia Univ Engn Coll Elect Engn Dept Baghdad 10053 Iraq Tanta Univ Fac Engn Elect Power & Machines Engn Dept Tanta 31512 Egypt Budapest Univ Technol & Econ H-1111 Budapest Hungary Gwangju Inst Sci & Technol GIST Grad Sch Energy Convergence Gwangju 61005 South Korea

Battery manufacturing and recycling are expensive;combined heat and power (CHP) units are optimal for residential premises. CHP units can enhance energy efficiency and reduce energy costs, but appropriately sized units must be chosen. Here, we optimize CHP unit sizing to minimize the energy costs of residential areas. Sizing is based on both the electricity and heat loads;it is possible to optimally rate the various types of CHP units. We compare an artificial bee colony (ABC) optimization method to a genetic algorithm (GA) when various strategies are adopted. Electricity and heat loads are considered together when sizing CHP units and optimizing costs using the ABC algorithm and the GA. The optimization outcomes are compared to a base case;the ABC method performs better than the GA. The average daily energy cost savings possible using the ABC method were higher for all three seasons (by 25.9, 4.4, and 10.8% respectively) compared to those possible when residential premises lacked CHP units.

关键词： Cogeneration Optimization Genetic algorithms artificial bee colony algorithm Resistance heating Linear programming Buildings artificial bee colony cost-benefit analysis CHP unit size optimization energy conversion genetic algorithm residential building automation

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Chaotic artificial bee colony algorithm based solution of security and transient stability constrained optimal power flow

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2015年 64卷 136-147页

作者： Ayan, Kursat Kilic, Ulas Barakli, Burhan Sakarya Univ Dept Comp Engn Sakarya Turkey Celal Bayar Univ Dept Mechat Engn Manisa Turkey Sakarya Univ Dept Elect & Elect Engn Sakarya Turkey

Due to the increase rapidly of electricity demand and the deregulation of electricity markets, the energy networks are usually run close to their maximum capacity to transmit the needed power. Furthermore, the operators have to run the system to ensure its security and transient stability constraints under credible contingencies. Security and transient stability constrained optimal power flow (STSCOPF) problem can be illustrated as an extended OPF problem with additional line loading and rotor angle inequality constraints. This paper presents a new approach for STSCOPF solution by a chaotic artificial bee colony (CABC) algorithm based on chaos theory. The proposed algorithm is tested on IEEE 30-bus test system and New England 39-bus test system. The obtained results are compared to those obtained from previous studies in literature and the comparative results are given to show validity and effectiveness of proposed method. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： artificial bee colony algorithm Chaos theory Optimal power flow Security Transient stability

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Modified Gbest-guided artificial bee colony algorithm with new probability model

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SOFT COMPUTING 2018年第7期22卷 2217-2243页

作者： Cui, Laizhong Zhang, Kai Li, Genghui Fu, Xianghua Wen, Zhenkun Lu, Nan Lu, Jian Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China Shenzhen Univ Coll Math & Stat Shenzhen Peoples R China

artificial bee colony (ABC) is a very effective and efficient swarm-based intelligence optimization algorithm, which simulates the collective foraging behavior of the honey bees. However, ABC has strong exploration ability but poor exploitation ability because its solution search equation performs well in exploration but badly in exploitation. In order to enhance the exploitation ability and obtain a better balance between exploitation and exploration, in this paper, a novel search strategy which exploits the valuable information of the current best solution and a novel probability model which makes full use of the other good solutions on onlooker bee phase are proposed. To be specific, in the novel search strategy, a parameter P is used to control which search equation to be used, the original search equation of ABC or the new proposed search equation. The new proposed search equation utilizes the useful information from the current best solution. In the novel probability model, the selected probability of the good solution is absolutely significantly larger than that of the bad solution, which makes sure the good solutions can attract more onlooker bees to search. We put forward a new ABC variant, named MPGABC by combining the novel search strategy and probability model with the basic framework of ABC. Through the comparison of MPGABC and some other state-of-the-art ABC variants on 22 benchmark functions, 22 CEC2011 real-world optimization problems and 28 CEC2013 real-parameter optimization problems, the experimental results show that MPGABC is better than or at least comparable to the competitors on most of benchmark functions and real-world problems.

关键词： artificial bee colony algorithm Search strategy Probability model Global optimization

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Wind speed prediction using the hybrid model of wavelet decomposition and artificial bee colony algorithm-based relevance vector machine

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2015年 73卷 625-631页

作者： Fei, Sheng-wei He, Yong Donghua Univ Sch Mech Engn Shanghai 201620 Peoples R China

In this paper, the hybrid model of wavelet decomposition and artificial bee colony algorithm-based relevance vector machine (WABCRVM) is presented for wind speed prediction. Here, wind speed can be regarded as a signal and decomposed into four decomposed signals with different frequency range, which can be obtained by 2-layer wavelet decomposition for wind speed data, and the prediction models of the four decomposed signals can be established by RVM with their each appropriate embedding dimension. artificial bee colony algorithm (ABC) is used to select the appropriate kernel parameters of their RVM models. Thus, each decomposed signal's RVM model of wind speed has appropriate embedding dimension and kernel parameter. Finally, the experimental results show that it is feasible for the proposed combination scheme to improve the prediction ability of RVM for wind speed. (C) 2015 Published by Elsevier Ltd.

关键词： Speed wind prediction Wavelet decomposition Embedding dimension artificial bee colony algorithm

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A parallel artificial bee colony algorithm for the job shop scheduling problem with a dynamic migration strategy

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COMPUTERS & INDUSTRIAL ENGINEERING 2016年 102卷 359-367页

作者： Asadzadeh, Leila Payame Noor Univ Dept Comp Engn & Informat Technol Tehran Iran

The job shop scheduling problem (ISSP) has attracted much attention in the field of both information sciences and operations research. Many heuristic algorithms such as genetic algorithms, particle swarm optimization, artificial bee colony algorithm, and ant colony optimization have been presented to solve it. Parallelization is one of the best approaches that can be used to enhance the performance of these heuristic algorithms. In this paper, we propose a parallel artificial bee colony algorithm to solve the job shop scheduling problem. In this method, artificial bee colony algorithm consists of several colonies that locate on different hosts of the network and algorithm is carried out in various colonies in parallel manner. The communication between colonies is carried out by exchanging migrants. A dynamic migration strategy is used to determine when a colony must communicate by its neighbors. Benchmark instances are used to investigate the performance of the proposed approach. The results show that the proposed parallel artificial bee colony algorithm improves the efficiency. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Job shop scheduling problem Heuristic algorithms artificial bee colony algorithm Local search procedure Parallel artificial bee colony algorithm

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A Modified artificial bee colony algorithm Using Accept-Reject Method: Theory and Application in Virtual Power Plant Planning

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IETE JOURNAL OF RESEARCH 2023年第8期69卷 5364-5379页

作者： Farahbakhsh, Hossein Pourfar, Iman Ara, Afshin Lashkar Islamic Azad Univ Dept Elect Engn Dezful Branch Dezful Iran Jundi Shapur Univ Technol Dept Elect Engn Dezful Iran

In this paper, the modified artificial bee colony optimization algorithm, in which the accept-reject method is used as a part of the solution process in order to limit the random search space, is proposed for solving the optimization problems. The accept-reject method can find points of desirable quality and high accuracy with the help of probabilistic functions and limited the random search space, which increases the speed of the algorithm for the next steps and also reduces the number of iterations of the artificial bee algorithm. Then, the next phases of the bee algorithm will be executed in the interval determined by the accept-reject method. One of the most important issues in intelligent algorithms, especially the bee algorithm, is that in problems with several local optimal points, sometimes the algorithm gets stuck in the trap of local optimal points and does not reach the global optimal points. By using the motion step determination parameter in the employed bee phase, the algorithm can be guided to reach the global optimal points while correctly adjusting the movement of the bees for searching in random food sources, which will help improving the problem-solving process. This algorithm is tested by the basic benchmark functions as well as the benchmark functions of CEC 2005 and CEC2017 and CEC2019 and an engineering problem related to the planning of a virtual power plant. The results show that the proposed algorithm is superior in comparison to other algorithms.

关键词： Accept-reject method artificial bee colony algorithm motion step optimization random search virtual power plant

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A multiple colonies artificial bee colony algorithm for a capacitated vehicle routing problem and re-routing strategies under time-dependent traffic congestion

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COMPUTERS & INDUSTRIAL ENGINEERING 2017年 109卷 151-168页

作者： Ng, K. K. H. Lee, C. K. M. Zhang, S. Z. Wu, Kan Ho, William Hong Kong Polytech Univ Dept Ind & Syst Engn Hong Kong Hong Kong Peoples R China Nanyang Technol Univ Sch Mech & Aerosp Engn Div Syst & Engn Management 50 Nanyang AveN3-20251 Singapore Singapore Univ Melbourne Management & Mkt Level 10198 Berkeley St Melbourne Vic 3010 Australia

An Online Vehicle Routing Problem is a formation of Capacitated Vehicle Routing Problem with rerouting strategy to resolve the problem of inefficient vehicle routing caused by traffic congestion. A flexible delivery rerouting strategy is proposed, which aims at reducing the risk of late delivery. The method of terminating an exploration in a solution by the original ABC algorithm, when the solution is trapped in local optima, is to abandon the solution after specific tolerance limits are set. The phenomenon of local optimal traps will be repeated rapidly after a lengthy recursive process and will eventually result in a low quality solution, with a more complex combinatorial problem when the capability of the exploration is restricted by an inflexible termination criterion. Therefore, this paper proposes a novel scheme using a Multiple Colonies artificial bee colony algorithm. The designs of the outstanding bee selection for colony communication show it to be superior in exploitation. The performance of the proposed algorithm is examined through by Capacitated Vehicle Routing instances and a case study, and the results indicate the potential of using real time information for data-driven vehicle scheduling. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Online vehicle routing problem Swarm intelligence artificial bee colony algorithm Multiple colony strategy

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An improved artificial bee colony algorithm based on the strategy of global reconnaissance

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SOFT COMPUTING 2016年第12期20卷 4825-4857页

作者： Ma, Wei Sun, Zhengxing Li, Junlou Song, Mofei Lang, Xufeng Nanjing Univ State Key Lab Novel Software Technol Nanjing Jiangsu Peoples R China Nanjing Inst Tourism & Hospitality Nanjing Jiangsu Peoples R China

The artificial bee colony (ABC) algorithm is a recently introduced swarm intelligence optimization algorithm based on the foraging behavior of a honeybee colony. However, many problems are encountered in the ABC algorithm, such as premature convergence and low solution precision. Moreover, it can easily become stuck at local optima. The scout bees start to search for food sources randomly and then they share nectar information with other bees. Thus, this paper proposes a global reconnaissance foraging swarm optimization algorithm that mimics the intelligent foraging behavior of scouts in nature. First, under the new scouting search strategies, the scouts conduct global reconnaissance around the assigned subspace, which is effective to avoid premature convergence and local optima. Second, the scouts guide other bees to search in the neighborhood by applying heuristic information about global reconnaissance. The cooperation between the honeybees will contribute to the improvement of optimization performance and solution precision. Finally, the prediction and selection mechanism is adopted to further modify the search strategies of the employed bees and onlookers. Therefore, the search performance in the neighborhood of the local optimal solution is enhanced. The experimental results conducted on 52 typical test functions show that the proposed algorithm is more effective in avoiding premature convergence and improving solution precision compared with some other ABCs and several state-of-the-art algorithms. Moreover, this algorithm is suitable for optimizing high-dimensional space optimization problems, with very satisfactory outcomes.

关键词： Scout bees Global reconnaissance artificial bee colony algorithm Function optimization Chaotic sequence

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