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Improved artificial bee colony algorithm for global optimization

INFORMATION PROCESSING LETTERS 2011年第17期111卷 871-882页

作者： Gao, Weifeng Liu, Sanyang Xidian Univ Dept Appl Math Xian 710071 Peoples R China

The artificial bee colony algorithm is a relatively new optimization technique. This paper presents an improved artificial bee colony (IABC) algorithm for global optimization. Inspired by differential evolution (DE) and introducing a parameter M. we propose two improved solution search equations, namely "ABC/best/1" and "ABC/rand/1". Then, in order to take advantage of them and avoid the shortages of them, we use a selective probability p to control the frequency of introducing "ABC/rand/1" and "ABC/best/1" and get a new search mechanism. In addition, to enhance the global convergence speed, when producing the initial population, both the chaotic systems and the opposition-based learning method are employed. Experiments are conducted on a suite of unimodal/multimodal benchmark functions. The results demonstrate the good performance of the IABC algorithm in solving complex numerical optimization problems when compared with thirteen recent algorithms. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Randomized algorithms artificial bee colony algorithm Initial population Solution search equation Search mechanism

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Modified artificial bee colony algorithm for Multiple-Choice Multidimensional Knapsack Problem

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IEEE ACCESS 2023年 11卷 45255-45269页

作者： Mkaouar, Arij Htiouech, Skander Chabchoub, Habib Univ Sfax OLID Lab ISGIS Sfax Tunisia Univ Jeddah CS & AI Dept Jeddah Saudi Arabia Al Ain Univ Coll Business Abu Dhabi U Arab Emirates

The multiple-choice multidimensional knapsack problem (MMKP) is a well-known NP-hard problem that has many real-time applications. However, owing to its complexity, finding computationally efficient solutions for the MMKP remains a challenging task. In this study, we propose a Modified artificial bee colony algorithm (MABC) to solve the MMKP. The MABC employs surrogate relaxation, Hamming distance, and a tabu list to enhance the local search process and exploit neighborhood information. We evaluated the performance of the MABC on standard benchmark instances and compared it with several state-of-the-art algorithms, including RLS, ALMMKP, ACO, PEGF-PERC, TIKS-TIKS2 and D-QPSO. The experimental results reveal that MABC produces highly competitive solutions in terms of the best solutions found, achieving approximately 2% of the optimal solutions with trivial (milliseconds) CPU time. The Kruskal-Wallis test revealed that there was no statistically significant difference in the objective function values between the MABC algorithm and other state-of-the-art algorithms (H = 0.31506, p = 0.98882). However, for CPU efficiency, the test showed a statistically significant difference (H = 84.90850, p = 0), indicating that the MABC algorithm exhibited significantly better CPU efficiency (with shorter execution times) than the other algorithms did. Along with these findings, the ease of implementation of the algorithm and the small number of control parameters make our approach highly adaptive for large-scale real-time systems.

关键词： artificial bee colony algorithm Approximation algorithms Heuristic algorithms Search problems Hamming distances Linear programming Convergence multiple-choice multidimensional knapsack problem hamming distance surrogate relaxation

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Solving line balancing and AGV scheduling problems for intelligent decisions using a Genetic-artificial bee colony algorithm

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COMPUTERS & INDUSTRIAL ENGINEERING 2024年 189卷

作者： Mumtaz, Jabir Minhas, Kaynat Afzal Rauf, Mudassar Yue, Lei Chen, Yarong Wenzhou Univ Coll Mech & Elect Engn Wenzhou Peoples R China Univ Engn & Technol Ind Engn Dept Taxila Pakistan Guangzhou Univ Sch Mech & Elect Engn Guangzhou Peoples R China

Due to the rapid advancement of technology, the demand for electronic devices in various sectors such as consumer electronics, automotive, telecommunications, healthcare, and industrial applications, as well as customized printed circuit board (PCB) products, has significantly increased. Balancing the PCB assembly line is crucial for improving productivity to meet market demand, which means balancing the workload distributed among various assembly stations on the assembly line. However, the workload balancing depends on materialhandling systems in production environments that facilitate the transportation of raw materials to the buffer storage of the assembly stations. In the PCB assembly system, the material handling is carried out using automated guided vehicles (AGVs). This paper studies the assembly line balancing and AGV scheduling collectively because of their dependency on each other. A mixed integer linear programming (MILP) model is formulated to balance the workload distribution among stations and schedule the AGVs. An existing intelligent platform of the real-life PCB industry is considered to solve this integrated problem with intelligent decisions. The platform includes three layers modules: physical layer, data management layer and application service layer. A novel genetic artificial bee colony (GABC) algorithm is proposed and embedded with an application service layer to optimize the current problem and give optimum solutions for the real-life physical layer. The proposed GABC algorithm incorporates the operators of the genetic algorithm (GA) i.e., crossover and mutation into the search process of the ABC algorithm. Additionally, the greedy selection feature is employed in GABC which significantly enhances the exploitation capabilities, leading to faster convergence, higher-quality solutions, and improved robustness. The performance of the proposed GABC algorithm is tested based on different parameters by comparing the results with GA, ABC and PSO al

关键词： Assembly line balancing AGV scheduling Workload Smart decision-making artificial bee colony algorithm

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Real Time Implementation and Analysis of Enhanced artificial bee colony algorithm Optimized PI Control algorithm for Single Phase Shunt Active Power Filter

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JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2020年第4期15卷 1541-1554页

作者： Rameshkumar, Kanagavel Indragandhi, Vairavasundaram Vellore Inst Technol Sch Elect Engn Vellore Tamil Nadu India

This paper proposes a new hybrid novel optimization approach, called Enhanced artificial bee colony algorithm (EABC) for designing an optimal PI controller for single-phase Shunt Active Power Filter (SAPF). The proposed EABC algorithm optimizes the gain values of the PI controller to improve the dynamic performance of SAPF. In this EABC, the adaptive real coded genetic algorithm (ARGA) is integrated with the artificial bee colony (ABC) algorithm and this integration improves the exploration and exploitation ABC and speed up the convergence rate. The minimization of integral square error (ISE) is considered as an objective function to manipulate the gain values of the PI controller. The system tested with MATLAB simulation results are implemented in the hardware circuit with the same set of parameters. The proposed hardware system is designed with the Cyclone-IV EP4CE30F484 FPGA controller and the gain value for this proposed controller is fed from the simulation results tested with ABC and EABC algorithm. The results obtained from the hardware setup is compared with simulation results. The experimental result enhanced the performance of THD of source current, settling time and percentage peak overshoot of DC Link voltage.

关键词： Single phase Shunt Active Power Filter artificial bee colony algorithm Harmonics Power quality Total harmonic distortion

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Developing a Reading Concentration Monitoring System by Applying an artificial bee colony algorithm to E-Books in an Intelligent Classroom

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SENSORS 2012年第10期12卷 14158-14178页

作者： Hsu, Chia-Cheng Chen, Hsin-Chin Su, Yen-Ning Huang, Kuo-Kuang Huang, Yueh-Min Natl Cheng Kung Univ Dept Engn Sci Tainan 701 Taiwan Natl Penghu Univ Sci & Technol Dept Informat Management Magong City 880 Penghu County Taiwan

A growing number of educational studies apply sensors to improve student learning in real classroom settings. However, how can sensors be integrated into classrooms to help instructors find out students' reading concentration rates and thus better increase learning effectiveness? The aim of the current study was to develop a reading concentration monitoring system for use with e-books in an intelligent classroom and to help instructors find out the students' reading concentration rates. The proposed system uses three types of sensor technologies, namely a webcam, heartbeat sensor, and blood oxygen sensor to detect the learning behaviors of students by capturing various physiological signals. An artificial bee colony (ABC) optimization approach is applied to the data gathered from these sensors to help instructors understand their students' reading concentration rates in a classroom learning environment. The results show that the use of the ABC algorithm in the proposed system can effectively obtain near-optimal solutions. The system has a user-friendly graphical interface, making it easy for instructors to clearly understand the reading status of their students.

关键词： reading concentration sensor technology artificial bee colony algorithm e-books intelligent classroom

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Energy-saving optimisation method for green space planning of urban gardens based on artificial bee colony algorithm

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INTERNATIONAL JOURNAL OF GLOBAL ENERGY ISSUES 2020年第5-6期42卷 393-408页

作者： Wang, Qingfen Zeng, Xinfa Changchun Architecture & Civil Engn Coll Sch Architecture & Planning Changchun Jilin Peoples R China Hunan City Univ Coll Civil Engn Yiyang Hunan Peoples R China

In order to overcome the problems of time-consuming and high energy consumption in traditional green space planning optimisation methods, a new energy-saving optimisation method based on artificial bee colony algorithm is proposed. The energy-saving model of urban green space planning is established by energy plus software, and the influence of various variables on energy consumption of urban green space planning is analysed by combining the parameter operation algorithm in genpot optimisation software. Based on the artificial bee colony optimisation algorithm, the optimisation parameters are selected, the optimisation objective function is established to optimise the parameters, and the energy-saving method of urban green space planning is studied. The experimental results show that the proposed method has high optimisation efficiency, and the energy consumption of the optimised model is greatly reduced.

关键词： artificial bee colony algorithm urban landscape architecture green space planning energy conservation effect

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Optimal Chiller Loading Using Modified artificial bee colony algorithm

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SENSORS AND MATERIALS 2020年第7期32卷 2387-2397页

作者： Lin, Chang-Ming Tseng, Ko-Ying Lin, Sheng-Fuu Natl Chiao Tung Univ Inst Elect & Control Engn 1001 Univ Rd Hsinchu 30010 Taiwan Ind Technol Res Inst Energy & Environm Res Labs Rm 820Bldg 518F195Sec 4Chung Hsing Rd Hsinchu 31040 Taiwan

In Taipei, over 45% of the energy used in buildings is for air-conditioning systems. In particular, multiple chiller systems consume about 70% of the energy in an air-conditioning system. Consequently, optimal chiller loading (OCL) or energy saving of a building is a vital issue. In this paper, we report a newly developed heuristic algorithm to solve OCL problems. A digital flow meter and a digital meter are installed to calculate the energy efficiency of a chiller. The exploration and exploitation of chiller loading can be efficiently improved without increasing the number of iterations by adopting the proposed modified artificial bee colony (MABC) algorithm. To demonstrate the performance of the proposed algorithm, it has been analyzed in comparison with other optimization methods. The result shows that the proposed algorithm can obtain a similar or better solution than previous algorithms. Therefore, it is a promising approach for solving the OCL problem.

关键词： optimal chiller loading artificial bee colony algorithm energy saving

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An adaptive multi-population artificial bee colony algorithm for dynamic optimisation problems

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KNOWLEDGE-BASED SYSTEMS 2016年第Jul.期104卷 14-23页

作者： Nseef, Shams K. Abdullah, Salwani Turky, Ayad Kendall, Graham Univ Kebangsaan Malaysia Ctr Artificial Intelligence Technol Data Min & Optimisat Res Grp Bangi 43600 Selangor Malaysia RMIT Univ Sch Comp Sci & Informat Technol Adelaide SA Australia Univ Nottingham Malaysia Campus Semenyih Malaysia Univ Nottingham ASAP Res Grp Nottingham NG7 2RD England

Recently, interest in solving real-world problems that change over the time, so called dynamic optimisation problems (DOPs), has grown due to their practical applications. A DOP requires an optimisation algorithm that can dynamically adapt to changes and several methodologies have been integrated with population-based algorithms to address these problems. Multi-population algorithms have been widely used, but it is hard to determine the number of populations to be used for a given problem. This paper proposes an adaptive multi-population artificial bee colony (ABC) algorithm for DOPs. ABC is a simple, yet efficient, nature inspired algorithm for addressing numerical optimisation, which has been successfully used for tackling other optimisation problems. The proposed ABC algorithm has the following features. Firstly it uses multi-populations to cope with dynamic changes, and a clearing scheme to maintain the diversity and enhance the exploration process. Secondly, the number of sub-populations changes over time, to adapt to changes in the search space. The moving peaks benchmark DOP is used to verify the performance of the proposed ABC. Experimental results show that the proposed ABC is superior to the ABC on all tested instances. Compared to state of the art methodologies, our proposed ABC algorithm produces very good results. (C) 2016 Published by Elsevier B.V.

关键词： Dynamic optimisation artificial bee colony algorithm Adaptive multi-population method Meta-heuristics

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Dynamic economic dispatch using artificial bee colony algorithm for units with valve-point effect

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EUROPEAN TRANSACTIONS ON ELECTRICAL POWER 2011年第1期21卷 70-81页

作者： Hemamalini, S. Simon, Sishaj P. Natl Inst Technol Dept Elect & Elect Engn Tiruchirappalli 620015 Tamil Nadu India

Dynamic economic dispatch (DED) is an important dynamic problem in power system operation and control. The objective of the problem is to schedule power generation for the online units over a time horizon, satisfying the unit and ramp rate constraints. In this paper, artificial bee colony (ABC) algorithm is used to solve the dynamic economic dispatch problem for generating units with valve-point effect. The feasibility of the proposed method is validated with ten- and five-unit-test systems for a period of 24 hours. In addition, the effects of control parameters on the performance of ABC algorithm for DED problem are studied. Results obtained with the proposed approach are compared with other techniques in the literature. The results obtained substantiate the applicability of the proposed method for solving DED problems with non-smooth cost functions in terms of solution quality and computation efficiency. Copyright (C) 2010 John Wiley & Sons, Ltd.

关键词： dynamic economic dispatch artificial bee colony algorithm ramp rate limits valve-point loading

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A modified artificial bee colony algorithm for real-parameter optimization

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INFORMATION SCIENCES 2012年 192卷 120-142页

作者： Akay, Bahriye Karaboga, Dervis Erciyes Univ Dept Comp Engn TR-38039 Kayseri Turkey

Swarm intelligence is a research field that models the collective intelligence in swarms of insects or animals. Many algorithms that simulates these models have been proposed in order to solve a wide range of problems. The artificial bee colony algorithm is one of the most recent swarm intelligence based algorithms which simulates the foraging behaviour of honey bee colonies. In this work, modified versions of the artificial bee colony algorithm are introduced and applied for efficiently solving real-parameter optimization problems. (C) 2010 Elsevier Inc. All rights reserved.

关键词： Swarm intelligence Self-organization artificial bee colony algorithm Real-parameter optimization

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