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AUTOMATIC BOILING WATER REACTOR CONTROL ROD PATTERN DESIGN USING ant colony optimization algorithm

NUCLEAR TECHNOLOGY 2011年第1期176卷 72-82页

作者： Wang, Cheng-Der Lin, Chaung Natl Tsing Hua Univ Dept Engn & Syst Sci Hsinchu 30013 Taiwan Inst Nucl Energy Res Nucl Engn Div Longtan Township 32546 Taoyuan County Taiwan

A method for the automatic design of a boiling water reactor (BWR) control rod (CR) pattern (CRP) was developed using the rank-based ant system, which is an effective optimization algorithm for a combinatorial optimization problem. The designed BWR CRP followed either the A2-B1-A1-B2 or the A1-B2-A2-B1 sequence in this study. After the CRP was determined, the SIMULATE-3 code was used to calculate the axial power distribution, the effective multiplication factor k(eff), the shutdown margin, and three thermal limits which were then used to evaluate the CRP and update the pheromone concentration. The developed methodology was demonstrated using design CRPs for several fuel loading patterns, showing that the designed patterns can be obtained within a reasonable computation time and with an acceptable cycle length.

关键词： boiling water reactor control rod pattern rank-based ant system ant colony optimization algorithm

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The hybrid method based on ant colony optimization algorithm in multiple factor analysis of the environmental impact of solar cell technologies

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2020年第6期17卷 6342-6354页

作者： Dong, Bo Luzin, Alexey Gura, Dmitry Liaoning Univ Sch Innovat & Entrepreneurship Chongshan Rd 66 Shenyang 110000 Liaoning Peoples R China Sechenov First Moscow State Med Univ Dept Chem Trubetskaya St 8 Moscow 119991 Russia Kuban State Technol Univ Dept Cadastre & Geoengn Moscow Str2 Bldg B Krasnodar 350072 Russia Kuban State Agr Univ Dept Geodesy 13 Kalinina Str Krasnodar 350044 Russia

The increasing demand for solar energy drives the mass production of diverse photovoltaic (PV) systems and, consequently, the growth of used solar panels and their environmental footprint. This study applied a new hybrid optimization method based on particle swarm and ant colony optimization algorithms to solve the problems of PV module toxicity. The Weibull distribution function was used to measure the service life of PV modules under a variety of failure scenarios. The simulation results show that PV modules that were guaranteed to have the service life of 25-30 years mostly last 20-25 years. The toxicity coefficient and the use of a hybrid method suggest that the time period when a solar module exhibits a maximum efficiency with a minimal environmental footprint ranges from 15 to 20 years. It was established that this interval corresponds to the level at which the amount of waste does not exceed the amount of energy generated with a minimum number of failures. The proposal will be effective in predicting the performance of solar systems. This approach can be improved in terms of cost and benefit and employed in the future research on renewable energy and ecosystems.

关键词： ant colony optimization algorithm new technologies Particle Swarm optimization algorithm PV panel waste solar energy

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An ant colony optimization algorithm for partner selection in Virtual Enterprises

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INTERNATIONAL JOURNAL OF MATERIALS & PRODUCT TECHNOLOGY 2009年第3期34卷 227-240页

作者： Cheng, Fangqi Ye, Feifan Yang, Jianguo Ningbo Univ Fac Engn Ningbo 315211 Zhejiang Peoples R China Zhejiang Business Technol Inst Dept Mechatron Ningbo Zhejiang Peoples R China Shanghai Jiao Tong Univ Sch Mech Engn Shanghai 200030 Peoples R China

Partner selection problem seeks to find a best combination of enterprises by optimising a nonlinear objective over the given constraints. In this paper the partner selection problem is modelled as a nonlinear integer programming problem and an ant colony optimization (ACO) algorithm embedded project scheduling is presented for solving the problem with the lead time, subproject cost and risk factor constraints in Virtual Enterprises (VE). Genetic algorithm (GA) and enumeration algorithm are introduced for comparison to check the effectiveness of the ACO algorithm. A case study is implemented to verify the feasibility of the proposed approach and the computational results are satisfactory.

关键词： VE virtual enterprise partner selection nonlinear integer programming ACO ant colony optimization algorithm

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Multi-Label Feature Selection Based on Improved ant colony optimization algorithm with Dynamic Redundancy and Label Dependence

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Computers, Materials & Continua 2024年第10期81卷 1157-1175页

作者： Ting Cai Chun Ye Zhiwei Ye Ziyuan Chen Mengqing Mei Haichao Zhang Wanfang Bai Peng Zhang School of Computer Hubei University of TechnologyWuhan430068China Xining Big Data Service Administration Xining810000China Wuhan Fiberhome Technical Services Co. Ltd.Wuhan430205China

The world produces vast quantities of high-dimensional multi-semantic ***,extracting valuable information from such a large amount of high-dimensional and multi-label data is undoubtedly arduous and *** selection aims to mitigate the adverse impacts of high dimensionality in multi-label data by eliminating redundant and irrelevant *** ant colony optimization algorithm has demonstrated encouraging outcomes in multi-label feature selection,because of its simplicity,efficiency,and similarity to reinforcement ***,existing methods do not consider crucial correlation information,such as dynamic redundancy and label *** tackle these concerns,the paper proposes a multi-label feature selection technique based on ant colony optimization algorithm(MFACO),focusing on dynamic redundancy and label ***,the dynamic redundancy is assessed between the selected feature subset and potential ***,the ant colony optimization algorithm extracts label correlation from the label set,which is then combined into the heuristic factor as label *** results demonstrate that our proposed strategies can effectively enhance the optimal search ability of ant colony,outperforming the other algorithms involved in the paper.

关键词： Multi-label feature selection ant colony optimization algorithm dynamic redundancy high-dimensional data label correlation

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A method based on the ant colony optimization algorithm for dynamic economic dispatch with valve-point effects

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INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS 2015年第2期25卷 262-287页

作者： Secui, Dinu Calin Univ Oradea Dept Energy Engn Oradea Romania

This article proposes the use of a heuristic method based on the ant colony optimization algorithm (MACO) to solve the dynamic economic dispatch (DED) problem with taking into consideration the valve-point effects. The suggested method takes into account both static and dynamic constraints of generator operation, the transmission losses and non-smooth cost functions that shape the valve-point effects. Also, a strategy for the DED imposed equality constraint handling is suggested. The proposed method's feasibility is proven for three different systems that have 10, 13 and 30 thermal generating units. The simulation results show that the suggested approach is very efficient having a capacity to obtain quality solutions in solving the DED problem with valve-points effects. The numerical results obtained through the suggested method are superior to other results recently reported in literature, for the same systems. Copyright (c) 2013 John Wiley & Sons, Ltd.

关键词： dynamic economic dispatch ant colony optimization algorithm valve-point effects transmission losses constraint handling

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Improved ant colony optimization algorithm based on islands type for mobile robot path planning

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INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS 2024年第5期21卷

作者： Li, Qian Li, Qipeng Cui, Baoling Zhejiang Sci Tech Univ Key Lab Fluid Transmiss Technol Zhejiang Prov Hangzhou Peoples R China Zhejiang Univ Sci & Technol Sch Mech & Energy Engn 318 Liuhe Rd Hangzhou Zhejiang Peoples R China

According to the characteristics of ant colony optimization (ACO) algorithm in mobile robot path planning, such as local optimal solution, slow convergence speed, low search efficiency, and propensity to produce numerous deadlock ants, an improved ACO algorithm based on island type (insular ACO (INACO)) is introduced. In this algorithm, firstly, several islands are established between the starting and the ending position of environment, serving as intermediate nodes for the paths searched by the ants. This greatly reduces the number of deadlock ants. Additionally, rectangular areas are initialized with non-uniform pheromone levels between adjacent islands, while other areas are set to a constant minimum pheromone value. This prevents blind searches during the initial stages of path planning. Furthermore, an adaptive volatilization coefficient is introduced into the global pheromone update rules to balance the convergence and global search ability. Finally, optimal parameter combinations of INACO are determined by simulation. INACO algorithm is simulated in various grid maps and compared with other improved ACO algorithms. Results demonstrate its superior global optimal search capability and rapid convergence speed. The average iterative times is 2.7 in a 20 x 20 grid environment and 4.7 in a 30 x 30 grid environment. Notably, INACO produces very few lost ants, with mean values of 50.1 and 95.2 in 20 x 20 and 30 x 30 grid environments, respectively.

关键词： Path planning ant colony optimization algorithm deadlock ant non-uniform pheromone

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AUTOMATIC POWER ASCENSION SEARCH FOR A BOILING WATER REACTOR USING ant colony optimization algorithm

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NUCLEAR TECHNOLOGY 2010年第2期170卷 281-289页

作者： Lin, Chaung Yen, Wei-Tinh Natl Tsing Hua Univ Dept Engn & Syst Sci Hsinchu 30013 Taiwan

The ant colony optimization algorithm was used to search for a feasible power ascension path for a boiling water reactor. The control action of start-up operation includes control rod movements and core flow rate variations. During the process, the core status should satisfy some constraints such as thermal limits, preconditioning interior operating management recommendation guideline, and power flow operation domain. In addition, the control objective is to reach full power as soon as possible and approach the target control rod pattern as close as possible. The results show that the developed method can obtain the satisfactory power ascension path, which is also comparable to the operation record.

关键词： boiling water reactor power ascension ant colony optimization algorithm

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Improved ant colony optimization algorithm Based on RNA Computing

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AUTOMATIC CONTROL AND COMPUTER SCIENCES 2017年第5期51卷 366-375页

作者： Zhang, Liyi Xiao, Chao Fei, Teng Tianjin Univ Commerce Sch Informat Engn Tianjin 300134 Peoples R China Tianjin Univ Commerce Sch Econ Tianjin 300134 Peoples R China

RNA computing is a new intelligent optimization algorithm, which combines computer science and molecular biology. Aiming at the weakness of slow convergence rate and poor global search ability in the basic ant colony optimization algorithm due to the unreasonable selection of parameters, this paper utilizes the combination of RNA computing and basic ant colony optimization algorithm to overcome the defects. An improved ant colony optimization algorithm based on RNA computing is proposed. In the iterative process of ant colony optimization algorithm, transformation operation, recombination operation and permutation operation in RNA computing are introduced to optimize the initial parameters including importance factor of pheromone trail a, importance factor of heuristic function beta and pheromone evaporation rate. to improve the convergence efficiency and global search ability. The performance of the algorithm is evaluated on five instances of the library of traveling salesman problems (TSPLIB) and six typical test functions. The experimental results demonstrate that the proposed RNA-ant colony optimization algorithm is superior than basic ant colony optimization algorithm in optimization ability, reliability, convergence efficiency, stability and robustness.

关键词： ant colony optimization algorithm RNA computing RNA-ACO parameters optimization traveling salesman problem

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Improved ant colony optimization algorithm and its application to solve pipe routing design

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ASSEMBLY AUTOMATION 2019年第1期39卷 45-57页

作者： Wu, Lei Tian, Xue Wang, Hongyan Liu, Qi Xiao, Wensheng China Univ Petr Coll Mech & Elect Engn Qingdao Shandong Peoples R China Nanyang Technol Univ Sch Civil & Environm Engn Maritime Inst Singapore Singapore Marine Design & Res Inst China Shanghai Peoples R China Qingdao Univ Sci & Technol Coll Elect Engn Qingdao Shandong Peoples R China

Purpose As a kind of NP-hard combinatorial optimization problem, pipe routing design (PRD) is applied widely in modern industries. In the offshore oil and gas industry, a semi-submersible production platform is an important equipment for oil exploitation and production. PRD is one of the most key parts of the design of semi-submersible platform. This study aims to present an improved ant colony algorithm (IACO) to address PRD for the oil and gas treatment system when designing a semi-submersible production platform. Design/methodology/approach First, to simplify PRD problem, a novel mathematical model is built according to real constraints and rules. Then, IACO, which combines modified heuristic function, mutation mechanism and dynamical parameter mechanism, is introduced. Findings Based on a set of specific instances, experiments are carried out, and the experimental results show that the performance of IACO is better than that of two variants of ACO, especially in terms of the convergence speed and swarm diversity. Finally, IACO is used to solve PRD for the oil and gas treatment system of semi-submersible production platform. The simulation results, which include nine pipe paths, demonstrate the practicality and high-efficiency of IACO. Originality/value The main contribution of this study is the development of method for solving PRD of a semi-submersible production platform based on the novel mathematical model and the proposed IACO.

关键词： ant colony optimization algorithm Pipe layout space Pipe path Pipe routing design Semi-submersible production platform

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Routing Technology in Wireless Sensor Network Based on ant colony optimization algorithm

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WIRELESS PERSONAL COMMUNICATIONS 2017年第3期95卷 1911-1925页

作者： Zheng Enxing Liu Ranran Jiangsu Univ Technol Sch Elect & Informat Engn Inst Bioinformat & Med Engn Changzhou 213001 Jiangsu Peoples R China Jiangsu Univ Technol Sch Automot & Traff Engn Changzhou 213001 Jiangsu Peoples R China

With rapid development of wireless communication, sensor, micro power system and electronic technology, the research on wireless sensor network has attracted more and more attention. The work proposed routing algorithm in wireless sensor network based on ant colony optimization by analyzing routing protocol and utilizing advanced idea. ant colony optimization algorithm has advantages in implementing local work, supporting multiple paths and integrating link quality into pheromone formation. In routing selection, the work calculated probability that node is selected as the next hop according to pheromone concentration on the path. With characteristics including self-organization, dynamic and multipath, ant colony optimization algorithm is suitable for routing in wireless sensor network. With low routing cost, good adaptability and multipath, the algorithm balanced energy consumption to prolong network lifetime. In terms of simulation and experiments, ant colony algorithm was proved to be suitable for finding optimal routing in wireless sensor network, thus achieving design goal of routing algorithm.

关键词： Wireless sensor network Routing algorithm ant colony optimization algorithm

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