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Dynamic optimisation by a modified bees algorithm

PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART I-JOURNAL OF SYSTEMS AND CONTROL ENGINEERING 2012年第I7期226卷 956-971页

作者： Castellani, Marco Pham, Q. Tuan Pham, Duc T. Univ Bergen Dept Biol Theoret Ecol Grp N-5020 Bergen Norway Univ New S Wales Sch Chem Engn Sydney NSW 2052 Australia Univ Birmingham Sch Mech Engn Birmingham B15 2TT W Midlands England

A modified bees algorithm was applied to dynamic optimisation problems in chemical engineering. A two-level factorial experiment was used to tune the settings of the population parameters, based on the premise that it is most important to avoid those configurations that cause the worst performances than to look for those that reach the best performance. Tested on eight well known benchmark problems, the tuned algorithm outperformed the standard bees algorithm and other two well known optimisation methods. The performance of the proposed algorithm was also competitive with that of the state-of-the-art in the literature, and the solutions produced were very close to the known optima of the benchmarks. The results demonstrate the efficacy of the modified bees algorithm as a tool for the solution of dynamic optimisation problems. The results also proved the effectiveness of the proposed statistical parameter tuning algorithm, and indicated its competitiveness as an alternative to the standard complex and subjective trial-and-error methods.

关键词： Evolutionary optimisation tuning dynamic optimisation factorial experiment parameter setting swarm intelligence bees algorithm

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Vehicle Safety Monitoring System Based on Internet of Things and Identification with Physiological Characteristics 1

Vehicle Safety Monitoring System Based on Internet of Things...

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1st IEEE International Conference on Knowledge Innovation and Invention (ICKII)

作者： Wang, Zhi-Hao Hendrick Wang, Chih-Min Jong, Gwo-Jia Natl Kaohsiung Univ Sci & Technol 415 Jiangong Rd Kaohsiung 80778 Taiwan

ISBN: (纸本)9781538652671

In general, it is a Very dangerous condition for a driver driving a car while his mental state is not clear. In this paper, a vehicle safety monitoring system is designed based on the Internet of Things and physiological characteristics identification. This system is divided into two parts, the first part is the network-coordinator and router which were installed on the road, and the second part is the network-end device that installed on the vehicle.

关键词： Internet of Things Vehicle Safety Monitoring System Identification of physiological characteristics Zigbee Networking bees algorithm

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To Solve the Hydropower Station Optimal Scheduling With Time-Varying Prices Based on bees algorithm

To Solve the Hydropower Station Optimal Scheduling With Time...

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International Conference on Energy, Environment and Sustainable Development (ICEESD 2011)

作者： Jun, Rui Jing, Xue Liang, Ming State Grid Elect Power Res Inst Nanjing 210003 Peoples R China

ISBN: (纸本)9783037852651

A new kind of intelligence optimization algorithm - bees algorithm (BA) is introduced, which is developed in recent years and is easy to realize, extremely quickly to convergence. Using the algorithm, the article solves optimal regulation of hydropower stations with time-varying price. Actual example proves the validity and fast astringency of BA. Thus, an additional method is proposed for solving optimal regulation of cascade hydropower stations.

关键词： hydropower station optimal regulation bees algorithm swarm intelligence

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Optimal Location and Sizing of Solar Farm on Japan East Power System Using Multiobjective bees algorithm

Optimal Location and Sizing of Solar Farm on Japan East Powe...

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IEEE Energytech

作者： Prakomchai Phonrattanasak Masafumi Miyatake Orie Sakamoto Department of Electrical Engineering North Eastern University Department of Electrical Engineering Sophia University

ISBN: (纸本)9781467344456

Nowadays some nuclear power plant in Japan is shut down due to problem of security in earthquake disaster. Consequently, Japan will encounter a lack of electrical energy in the future if new electrical generation is not constructed. Solar energy is one of appropriate renewable energy for Japan. Solar panel produces electrical energy by using the natural solar radiation from sun. PV Solar farm consists of multiple components, including the photovoltaic modules, mechanical and electrical connections. PV solar farm with grid-connected can directly connect to the existing electricity grid by using inverter circuit toward transformer. In this paper, PV solar farm is utilized as a power generation which injects active power into a Japan east power system. An optimal location and sizing of solar farm by using multiobjective bees optimization (MBO) is proposed to minimize fuel and emission costs of overall system with considering solar radiation energy in each area. The results show that the proposed method found the optimal position of solar farm with minimum cost of fuel and environmental pollution.

关键词： Power system Optimization bees algorithm PV solar and Japan power system

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Improvement of Scatter Search Using bees algorithm

Improvement of Scatter Search Using Bees Algorithm

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6th International Conference on Signal Processing and Communication Systems (ICSPCS)

作者： Sagheer, Ali Makki Sadiq, Ahmed T. Ibrahim, Mohammed S. Univ Anbar Coll Comp Dept Informat Syst Al Anbar Iraq Univ Technol Baghdad Dept Comp Sci Baghdad Iraq Univ Anbar Coll Comp Dept Comp Sci Al Anbar Iraq

ISBN: (纸本)9781467323932;9781467323925

The Scatter Search is one of the population-based metaheuristics, which can be used to solve a wide range of problems including Traveling Salesman Problem (TSP), Routing Protocol, and End-to-End QoS, while bees algorithm can be applied to optimize the solution of these problems. In this paper, an improved Scatter Search algorithm based on bees algorithm has been presented. The improvement provides Scatter Search with random exploration for search space of problem and more of intensification for promising solutions. The original and improved Scatter Search has been tested on Traveling Salesman Problem. A computational experiment with benchmark instances is reported. The results demonstrate that the improved Scatter Search algorithm produces better performance than original Scatter Search algorithm. The developed algorithm has been compared with other algorithms for the same problem, and the result was competitive with some algorithm and insufficient with another.

关键词： component Metaheuristic Scatter Search bees algorithm Combinatorial Problems Traveling Salesman Problem

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Identifying Gene Knockout Strategies Using a Hybrid of bees algorithm and Flux Balance Analysis for in Silico Optimization of Microbial Strains

Identifying Gene Knockout Strategies Using a Hybrid of Bees ...

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9th International Symposium on Distributed Computing and Artificial Intelligence (DCAI 2012)

作者： Choon, Yee Wen Mohamad, Mohd Saberi Deris, Safaai Chong, Chuii Khim Chai, Lian En Ibrahim, Zuwairie Omatu, Sigeru Univ Teknologi Malaysia Fac Comp Sci & Informat Syst Artificial Intelligence & Bioinformat Grp Skudai 81310 Johor Malaysia Univ Teknol Malaysia Fac Engn CAIRO Skudai 81310 Malaysia Osaka Inst Technol Informat & Commun Engn Dept Elect Osaka 5358585 Japan

ISBN: (纸本)9783642287640;9783642287657

Genome-scale metabolic networks reconstructions from different organisms have become popular in recent years. Genetic engineering is proven to be able to obtain the desirable phenotypes. Optimization algorithms are implemented in previous works to identify the effects of gene knockout on the results. However, the previous works face the problem of falling into local minima. Thus, a hybrid of bees algorithm and Flux Balance Analysis (BAFBA) is proposed in this paper to solve the local minima problem and to predict optimal sets of gene deletion for maximizing the growth rate of certain metabolite. This paper involves two case studies that consider the production of succinate and lactate as targets, by using E-coli as model organism. The results from this experiment are the list of knockout genes and the growth rate after the deletion. BAFBA shows better results compared to the other methods. The identified list suggests gene modifications over several pathways and may be useful in solving challenging genetic engineering problems.

关键词： Evolutionary Programming Metabolic Engineering bees algorithm Gene Knockout Optimization

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An Optimization Framework of Multiobjective Artificial Bee Colony algorithm Based on the MOEA Framework

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COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2018年第1期2018卷 5865168-5865168页

作者： Huo, Jiuyuan Liu, Liqun Lanzhou Jiaotong Univ Sch Elect & Informat Engn Lanzhou 730070 Gansu Peoples R China Chinese Acad Sci Northwest Inst Ecoenvironm & Resources Lanzhou 730000 Gansu Peoples R China Gansu Agr Univ Coll Informat Sci & Technol Lanzhou 730070 Gansu Peoples R China

The artificial bee colony (ABC) algorithm has become one of the popular optimization metaheuristics and has been proven to perform better than many state-of-the-art algorithms for dealing with complex multiobjective optimization problems. However, the multiobjective artificial bee colony (MOABC) algorithm has not been integrated into the common multiobjective optimization frameworks which provide the integrated environments for understanding, reusing, implementation, and comparison of multiobjective algorithms. Therefore, a unified, flexible, configurable, and user-friendly MOABC algorithm framework is presented which combines a multiobjective ABC algorithm named RMOABC and the multiobjective evolution algorithms (MOEA) framework in this paper. The multiobjective optimization framework aims at the development, experimentation, and study of metaheuristics for solving multiobjective optimization problems. The framework was tested on the Walking Fish Group test suite, and a many-objective water resource planning problem was utilized for verification and application. The experiment's results showed the framework can deal with practical multiobjective optimization problems more effectively and flexibly, can provide comprehensive and reliable parameters sets, and can complete reference, comparison, and analysis tasks among multiple optimization algorithms.

关键词： bees algorithm METAHEURISTIC algorithms VERIFICATION (Logic) MEDICINE BIOINFORMATICS

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New Hybrids of ANFIS with Several Optimization algorithms for Flood Susceptibility Modeling

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WATER 2018年第9期10卷 1210-1210页

作者： Dieu Tien Bui Khosravi, Khabat Li, Shaojun Shahabi, Himan Panahi, Mahdi Singh, Vijay P. Chapi, Kamran Shirzadi, Ataollah Panahi, Somayeh Chen, Wei Bin Ahmad, Baharin Ton Duc Thang Univ Geog Informat Sci Res Grp Ho Chi Minh City Vietnam Ton Duc Thang Univ Fac Environm & Labour Safety Ho Chi Minh City Vietnam Sari Agr Sci & Nat Resources Univ Fac Nat Resources Dept Watershed Management Engn Sari *** Iran Chinese Acad Sci Inst Rock & Soil Mech State Key Lab Geomech & Geotech Engn Wuhan 430071 Hubei Peoples R China Univ Kurdistan Fac Nat Resources Dept Geomorphol Sanandaj *** Iran Islamic Azad Univ North Tehran Branch Young Researchers & Elites Club Tehran 19585466 Iran Texas A&M Univ Dept Biol & Agr Engn College Stn TX 77843 USA Texas A&M Univ Zachry Dept Civil Engn College Stn TX 77843 USA Univ Kurdistan Fac Nat Resources Dept Rangeland & Watershed Management Sanandaj *** Iran Xian Univ Sci & Technol Coll Geol & Environm Xian 710054 Shaanxi Peoples R China Univ Teknol Malaysia Fac Geoinformat & Real Estate Dept Geoinformat Skudai 81310 Malaysia

This study presents three new hybrid artificial intelligence optimization modelsnamely, adaptive neuro-fuzzy inference system (ANFIS) with cultural (ANFIS-CA), bees (ANFIS-BA), and invasive weed optimization (ANFIS-IWO) algorithmsfor flood susceptibility mapping (FSM) in the Haraz watershed, Iran. Ten continuous and categorical flood conditioning factors were chosen based on the 201 flood locations, including topographic wetness index (TWI), river density, stream power index (SPI), curvature, distance from river, lithology, elevation, ground slope, land use, and rainfall. The step-wise weight assessment ratio analysis (SWARA) model was adopted for the assessment of relationship between flood locations and conditioning factors. The ANFIS model, based on SWARA weights, was employed for providing FSMs with three optimization models to enhance the accuracy of prediction. To evaluate the model performance and prediction capability, root-mean-square error (RMSE) and receiver operating characteristic (ROC) curve (area under the ROC (AUROC)) were used. Results showed that ANFIS-IWO with lower RMSE (0.359) had a better performance, while ANFIS-BA with higher AUROC (94.4%) showed a better prediction capability, followed by ANFIS0-IWO (0.939) and ANFIS-CA (0.921). These models can be suggested for FSM in similar climatic and physiographic areas for developing measures to mitigate flood damages and to sustainably manage floodplains.

关键词： flood susceptibility modeling ANFIS cultural algorithm bees algorithm invasive weed optimization Haraz watershed

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A MULTI-OBJECTIVE APPROACH TO THE ASSIGNMENT OF STOCK KEEPING UNITS TO UNIDIRECTIONAL PICKING LINES

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SOUTH AFRICAN JOURNAL OF INDUSTRIAL ENGINEERING 2017年第1期28卷 190-209页

作者： le Roux, G. J. Visagie, S. E. Univ Stellenbosch Dept Logist Stellenbosch South Africa

An order picking system in a distribution centre consisting of parallel unidirectional picking lines is considered. The objectives are to minimise the walking distance of the pickers, the largest volume of stock on a picking line over all picking lines, the number of small packages, and the total penalty incurred for late distributions. The problem is formulated as a multi-objective multiple knapsack problem that is not solvable in a realistic time. Population-based algorithms, including the artificial bee colony algorithm and the genetic algorithm, are also implemented. The results obtained from all algorithms indicate a substantial improvement on all objectives relative to historical assignments. The genetic algorithm delivers the best performance.

关键词： PICKERS (Weaving) STOCKS (Finance) bees algorithm GENETIC algorithms PROBLEM solving

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Bee-Inspired algorithms Applied to Vehicle Routing Problems: A Survey and a Proposal

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MATHEMATICAL PROBLEMS IN ENGINEERING 2017年第1期2017卷 1-20页

作者： Masutti, Thiago A. S. de Castro, Leandro N. Univ Prebiteriana Mackenzie Nat Comp & Machine Learning Lab LCoN Grad Program Elect Engn & Comp R Consolacao 930 BR-01302000 Sao Paulo SP Brazil

Vehicle routing problems constitute a class of combinatorial optimization tasks that search for optimal routes (e.g., minimal cost routes) for one or more vehicles to attend a set of nodes (e.g., cities or customers). Finding the optimal solution to vehicle routing tasks is an NP-hard problem, meaning that the size of problems that can be solved by exhaustive search is limited. From a practical perspective, this class of problems has a wide and important set of applications, from the distribution of goods to the integrated chip design. Rooted on the use of collective intelligence, swarm-inspired algorithms, more specifically bee-inspired approaches, have been used with good performance to solve such problems. In this context, the present paper provides a broad review on the use of bee-inspired methods for solving vehicle routing problems, introduces a new approach to solve one of the main tasks in this area (the travelling salesman problem), and describes open problems in the field.

关键词： bees algorithm VEHICLE routing problem MATHEMATICAL optimization PHYSICAL distribution of goods PROBLEM solving

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