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11th International Conference on Information and Communication Technology and System (ICTS)

作者： Pratama, Rayandra Yala Sarno, Riyanarto Sholiq Inst Teknol Sepuluh Nopember Dept Informat Technol Management Surabaya Indonesia Inst Teknol Sepuluh Nopember Dept Informat Surabaya Indonesia Inst Teknol Sepuluh Nopember Dept Informat Syst Surabaya Indonesia

ISBN: (纸本)9781538628270

Cost estimation is a crucial and essential process in software industry. The more accurate cost estimated, the more efficient the project became. This cost estimation become a challenge for software industry to bring accurate result. There are many methods to solve this problem. Constructive Cost Model is usual method that is used to estimate software cost. This model was proposed in 1981 by using regression analysis with 63 types of project data. In 2000, COCOMO II was introduced. This new model of COCOMO use cost drivers, scale factors, and project size that measured by line of code. COCOMO II has 4 parameters A, B, C and D. However, using this parameters are not guarantee accurate result. This paper proposed bee Colony Optimization to calibrate the COCOMO II model parameter to be more accurate for effort estimation. This bee Colony Optimization is applied on Nasa93 dataset that consisted of 93 projects which each project has 22 cost drivers, project's size, effort, and development time. This proposed method gives MMRE result 50.584% on effort and 14.192% on development time.

关键词： bee Colony Optimization COCOMO bee algorithm MRE Software Cost Estimation

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Tourist Itineraries Plan Design Based On the Behavior of bee Colonies 2

Tourist Itineraries Plan Design Based On the Behavior of Bee...

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2nd International Workshop on Modern Machine Learning Technologies and Data Science (MoMLeT+DS)

作者： Uhryn, Dmytro Naum, Oleh Antonyuk, Nataliya Dyyak, Ivan Chyrun, Liliya Demchuk, Andriy Vysotska, Victoria Rybchak, Zoriana Batiuk, Taras Chernivtsi Philosoph & Legal Lyceum Chemivtsi Ukraine Drohobych Ivan Franko State Pedag Univ Drogobych Ukraine Ivan Franko Natl Univ Lviv Lvov Ukraine Univ Opole Opole Poland Lviv Polytech Natl Univ Lvov Ukraine

The article deals with the modified paradigm of the bee colony for tourist routes by solving combinatorial problems on graphs: selection in the graph of an independent subset of vertices, finding the maximum pairing in the graph, coloring the graph, highlighting the click in the graph. Based on the analysis of the behavioral model of bee colony self-organization, methods and mechanisms of formation of corresponding representations of solutions of the considered combinatorial problems on graphs are developed. Methods of search space forming are considered. The position in the search space is presented as an ordered list. The key operation of the bee algorithm is the study of perspective positions and their surroundings in the search space. The paper proposes a method of decision edges forming with an adjustable degree of similarity and closeness between them. Three approaches are proposed to determine the number of foraging agents that are sent to the vicinity of each base position.

关键词： bee Colony Tourist Routes Optimization bee algorithm Self-Organization

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Application of decision theory and bee-inspired method to railway system route optimization

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INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE AND ENGINEERING MANAGEMENT 2020年第1期15卷 59-69页

作者： Leong, Kah Huo Wang, Chen Abdul-Rahman, Hamzah Shavarebi, Kamran Boursier, Patrice Loo, Siaw-Chuing Int Univ Malaya Wales Fac Sci Technol Engn & Math STEM Kuala Lumpur 50408 Malaysia Huaqiao Univ Coll Civil Engn Xiamen Peoples R China Taylors Univ Sch Comp & IT Subang Jaya Selangor Malaysia Univ Malaya Fac Built Environm Ctr Bldg Construct & Trop Architecture Kuala Lumpur Malaysia

Route planning for multiple destinations via a railway system (RS) is challenging, especially in a complex network with hundreds of stations and interchanges, resulting in a railway traveling salesman problem (RTSP), which is a variant of the traveling salesman problem (TSP). Limited attention has been devoted to solving the RTSP, despite the increase in the number of people using RSs and the added complexity of network expansions. An optimization algorithm and mathematical model based on the foraging behavior of bees and decision theory were used in this paper to identify the optimum RS route to multiple destinations before returning to the first station. Data collected from RSs in Japan and Malaysia were used to create 200 test cases (100 cases with each dataset), and the results were compared to specific solutions and the official optimum route planner to prove that the model is a promising approximation method. The solutions were evaluated and verified by comparing results from another 200 brute-force cases generated by the Wiley TSP Solver. The results obtained from the experiments prove the reliability and capability of the route planning and optimization solutions for RSs with different complexities and in different environments without the assistance of information technology.

关键词： Route optimization Travelling salesman problem railway system bee algorithm Decision theory railway traveling salesman problem route management swarm intelligence

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Lake Eutrophic Evaluation Based on bee Immune Evolutionary algorithm

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Agricultural Science & Technology 2010年第4期11卷 156-158,188页

作者：党媛李祚泳邹艳玲成都信息工程学院四川成都610225

In order to establish the lake eutrophic evaluation model for multiple indices,based on the gauge transformation,an index formula in the form of a logarithmic power function was proposed to design an eutrophic evaluation model for the ＂ normalized values＂ of *** parameters in the formula were also optimized by bee immune evolutionary algorithm（BEIEA）.The universal index formula was suitable to multiindices items for eutrophic *** the same time,the formula was applied to practical eutrophic evaluations in 10 regions of Dong *** evaluation results were coincident with those obtained from the power function of weighted sums and also with actual *** was shown that the bee immune evolutionary algorithm was suitable to the parameter optimization in the eutrophic evaluation model.

关键词： Lake Eutrophic evaluation bee algorithm bee immune evolutionary algorithm Parameter optimization

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Controlling chaos based on a novel intelligent integral terminal sliding mode control in a rod-type plasma torch

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Chinese Physics B 2016年第5期25卷 11-22页

作者： Safa Khari Zahra Rahmani Behrooz Rezaie Intelligent Systems Research Group Faculty of Electrical and Computer EngineeringBabol Noshrivani University of Technology

An integral terminal sliding mode controller is proposed in order to control chaos in a rod-type plasma torch *** this method, a new sliding surface is defined based on a combination of the conventional sliding surface in terminal sliding mode control and a nonlinear function of the integral of the system states. It is assumed that the dynamics of a chaotic system are unknown and also the system is exposed to disturbance and unstructured uncertainty. To achieve a chattering-free and high-speed response for such an unknown system, an adaptive neuro-fuzzy inference system is utilized in the next step to approximate the unknown part of the nonlinear dynamics. Then, the proposed integral terminal sliding mode controller stabilizes the approximated system based on Lyapunov＇s stability theory. In addition, a bee algorithm is used to select the coefficients of integral terminal sliding mode controller to improve the performance of the proposed method. Simulation results demonstrate the improvement in the response speed, chattering rejection, transient response,and robustness against uncertainties.

关键词： chaos rod-type plasma torch intelligent integral terminal sliding mode control bee algorithm

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Efficient operation of residential solar panels with determination of the optimal tilt angle and optimal intervals based on forecasting model

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IET RENEWABLE POWER GENERATION 2017年第10期11卷 1261-1267页

作者： Akhlaghi, Shahrokh Sangrody, Hossein Sarailoo, Morteza Rezaeiahari, Mandana SUNY Binghamton Elect & Comp Engn Dept Binghamton NY 13902 USA SUNY Binghamton Syst Sci & Ind Engn Dept Binghamton NY 13902 USA

A solar panel tilt angle plays a great role in the performance of the solar panel which is either fixed at an optimal tilt angle or continuously adjusted using a solar tracking system. Solar tracking systems are not cost efficient especially for residential usage. On the other hand, a fixed tilt angle results in a huge loss of solar energy. One resort to solve this problem is to adjust the tilt angle a limited number of times. In this paper, a novel procedure is proposed to select the number of intervals and their durations by solving an optimisation problem. The proposed algorithm is consisted of four major steps. First, the solar radiation of the next year is predicted using historical data. Second, using a bee algorithm the optimal tilt angle of each interval is computed. Third, an optimisation problem is solved to get new periods for each interval. Finally, a stopping criterion is checked to decide whether the previous step should be repeated or the algorithm has been converged. The effectiveness of the proposed approach is studied at nine different locations across the US. The results show improvement of the solar power generation by using the optimal intervals.

关键词： solar power stations load forecasting optimisation sunlight residential solar panels optimal tilt angle determination optimal intervals forecasting model solar panel tilt angle solar tracking system solar energy optimisation problem solar radiation bee algorithm stopping criterion solar power generation

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A new hybrid multi-agent-based particle swarm optimisation technique

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INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 2009年第4期1卷 259-269页

作者： Kumar, Rajesh Sharma, Devendra Kumar, Anupam Malaviya Natl Inst Technol Dept Elect Engn Jaipur Rajasthan India Malaviya Natl Inst Technol Dept Elect & Commun Engn Jaipur Rajasthan India

This paper presents a multi-agent based hybrid particle swarm optimisation technique. The algorithm integrates the deterministic, the multi-agent system (MAS) and the particle swarm optimisation (PSO) algorithm. An agent in hybrid multi-agent PSO (HMAPSO) represents a particle to PSO and a candidate solution to optimisation problem. All agents search parallel in an equally distributed lattice-like structure to save energy and computational time. The best solution is obtained through bee decision making process. Thus making use of deterministic search, multi-agent and bee PSO, the HMAPSO realises the purpose of optimisation. The proposed algorithm has been tested on various optimisation problems. The experimental results obtained show the robustncss and accuracy of proposed HMAPSO. It also has been concluded that the proposed HMAPSO is able to generate a unique and optimal solution than the earlier reported approaches and hence can be a better option for real-time online optimisation problems.

关键词： particle swarm optimisation PSO multi-agent system MAS bee algorithm

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Heuristic-based neural networks for stochastic dynamic lot sizing problem

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APPLIED SOFT COMPUTING 2013年第3期13卷 1332-1339页

作者： Senyigit, Ercan Dugenci, Muharrem Aydin, Mehmet E. Zeydan, Mithat Erciyes Univ Dept Ind Engn Kayseri Turkey Karabuk Univ Dept Ind Engn Karabuk Turkey Univ Bedfordshire Dept Comp Sci & Technol Luton Beds England

Multi-period single-item lot sizing problem under stochastic environment has been tackled by few researchers and remains in need of further studies. It is mathematically intractable due to its complex structure. In this paper, an optimum lot-sizing policy based on minimum total relevant cost under price and demand uncertainties was studied by using various artificial neural networks trained with heuristic-based learning approaches;genetic algorithm (GA) and bee algorithm (BA). These combined approaches have been examined with three domain-specific costing heuristics comprising revised silver meal (RSM), revised least unit cost (RLUC), cost benefit (CB). It is concluded that the feed-forward neural network (FF-NN) model trained with BA outperforms the other models with better prediction results. In addition, RLUC is found the best operating domain-specific heuristic to calculate the total cost incurring of the lot-sizing problem. Hence, the best paired heuristics to help decision makers are suggested as RLUC and FF-NN trained with BA. (C) 2012 Elsevier B. V. All rights reserved.

关键词： Stochastic lot-sizing Feed-forward neural networks bee algorithm Genetic algorithms Taguchi methods

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Comparative study between different optimisation techniques for finding precise switching angle for SHE-PWM of three-phase seven-level cascaded H-bridge inverter

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IET POWER ELECTRONICS 2018年第3期11卷 600-609页

作者： Kundu, Sourabh Burman, Arka Deb Giri, Santu K. Mukherjee, Sarbani Banerjee, Subrata Natl Inst Technol NIT Durgapur Dept Elect Engn Durgapur 713209 India CSIR Cent Mech Engn Res Inst SET Grp Durgapur 713209 India

Selective harmonic elimination pulse-width modulation (SHE-PWM) works at low-frequency switching, which reduces switching losses, device stress, and increases energy conversion efficiency. So, it can be an effective control strategy for multilevel inverter working on medium-voltage, high-power industrial energy conversion application. It provides desired output voltage by retaining the requested fundamental component as well as eliminating some low-order harmonics. The application of SHE-PWM in industries is having an influence on precise solvability of complex and non-linear equations. This study presents two recently reported optimisation techniques, namely backtracking search algorithm and differential search algorithm (DSA) for obtaining a more accurate solution of the harmonics elimination problem. The superiority of the proposed optimisation algorithms over the well known ancient algorithm such as genetic algorithm, bee algorithm and particle swarm optimisation have been established by a comparative study with respect to the possibility of attaining global minima, the rank of convergence rate, and inverter performance analysis. Simulation and experimental results validate the efficacy of the DSA optimisation technique for calculating more precise switching angles that totally eliminate 5th- and 7th-order harmonics with fulfilling the requested fundamental component.

关键词： PWM invertors switching convertors harmonics suppression search problems genetic algorithms particle swarm optimisation SHE-PWM three-phase seven-level cascaded H-bridge inverter selective harmonic elimination pulse-width modulation multilevel inverter medium-voltage high-power industrial energy conversion application energy conversion efficiency low-order harmonic elimination complex equation nonlinear equation backtracking search algorithm DSA differential search algorithm genetic algorithm bee algorithm particle swarm optimisation 5th-order harmonic elimination 7th-order harmonic elimination

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Optimized features selection for gender classification using optimization algorithms

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TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2013年第5期21卷 1479-1494页

作者： Khan, Salid Ali Nazir, Muhammad Riaz, Naveed Szabist Univ Dept Comp Sci Islamabad Pakistan Natl Univ Comp & Emerging Sci FAST Islamabad Pakistan

Optimized feature selection is an important task in gender classification. The optimized features not only reduce the dimensions, but also reduce the error rate. In this paper, we have proposed a technique for the extraction of facial features using both appearance-based and geometric-based feature extraction methods. The extracted features are then optimized using particle swarm optimization (PSO) and the bee algorithm. The geometric-based features are optimized by PSO with ensemble classifier optimization by the genetic algorithm. Using this approach, we have obtained promising results in terms of the classification error rate and computation time minimization. Moreover, our optimized feature-based method is robust to illumination, noise, and occlusion changes.

关键词： Gender classification facial features particle swarm optimization genetic algorithm bee algorithm

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