Article
Automation & Control Systems
Dongge Qin, Zhenxue He, Xiaojun Zhao, Jia Liu, Fan Zhang, Limin Xiao
Summary: This paper presents a Multi-strategy Multi-objective Artificial Bee Colony (MMABC) algorithm to solve the binary multi-objective optimization problem. The algorithm includes a flexible foraging behavior strategy, a genetic retention evolution, and an efficient transform strategy. Additionally, an area and power optimization approach for FPRM logic circuits is proposed, using the MMABC to search for polarities with smaller area and lower power. Experimental results demonstrate the efficacy and superiority of this approach.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Hui Wang, Shuai Wang, Zichen Wei, Tao Zeng, Tingyu Ye
Summary: This paper proposes an improved many-objective artificial bee colony algorithm based on decomposition and dimension learning to solve many-objective optimization problems. The multi-objective problem is converted into several sub-problems by decomposition, and a new fitness function is defined. Elite solutions are selected based on their fitness values. The algorithm uses an elite set guided search strategy and dimension learning to improve convergence, and dynamically allocates computing resources in the scout bee stage. Experimental results show that this method outperforms seven other many-objective evolutionary algorithms.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Mathematics
Nien-Che Yang, Danish Mehmood, Kai-You Lai
Summary: This study proposes a new multi-objective optimization method based on an artificial bee colony (ABC) algorithm for achieving optimal design of passive power filters. Through a series of case studies, the efficiency and better performance of the proposed method over previous well-known algorithms have been demonstrated.
Review
Automation & Control Systems
Ebubekir Kaya, Beyza Gorkemli, Bahriye Akay, Dervis Karaboga
Summary: The ABC algorithm is a popular optimization algorithm that has been successfully applied to solve real-world problems. This study examines combinatorial optimization approaches based on the ABC algorithm, provides summaries of related studies, and introduces the ABC algorithm-based approaches used. The study also evaluates mechanisms to improve the local search capability of the ABC algorithm and analyzes neighborhood operators, selection schemes, initial populations determination approaches, hybrid approaches, and test instances used in evaluating the performances of ABC algorithms.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2022)
Article
Chemistry, Multidisciplinary
Hassan Shokouhandeh, Sohaib Latif, Sadaf Irshad, Mehrdad Ahmadi Kamarposhti, Ilhami Colak, Kei Eguchi
Summary: This study proposes a modified version of the artificial bee colony (MABC) algorithm to solve reactive power compensation problems in power systems and compares its results with other algorithms. The simulation results show that the MABC algorithm has lower active power losses and reactive power costs, as well as better voltage characteristics and system stability.
APPLIED SCIENCES-BASEL
(2022)
Article
Computer Science, Artificial Intelligence
Yuyang Bai, Changsheng Zhang, Weitong Bai
Summary: The article introduces a two-level parallel decomposition-based artificial bee colony algorithm for solving dynamic multiple-objective optimization problems. By decomposing the problem into a set of single-objective optimization problems and using an improved parallel bee colony algorithm for solving them, the method can efficiently obtain the Pareto front and shows good performance in experiments.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Omur Sahin, Bahriye Akay
Summary: Microservices decompose applications into maintainable services and reduce complexity. The study proposes a Discrete Dynamic Artificial Bee Colony with Hyper-Scout algorithm to address issues in RESTful testing generation. Experimental results show the algorithm achieved high performance in multiple problems.
APPLIED SOFT COMPUTING
(2021)
Article
Automation & Control Systems
Serap Ercan Comert, Harun Resit Yazgan
Summary: This paper introduces three multi-objective electric vehicle routing problems that consider different charging strategies and electric vehicle charger types while optimizing five conflicting objectives. A new hierarchical approach consisting of Hybrid Ant Colony Optimization (HACO) and Artificial Bee Colony Algorithm (ABCA) is developed to solve these problems. The proposed approach is examined on test-based instances and achieves the best new results in most cases.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Automation & Control Systems
Rafal Szczepanski, Krystian Erwinski, Mateusz Tejer, Artur Bereit, Tomasz Tarczewski
Summary: This paper focuses on the palletizing problem using robotic arms, considering three production lines. By proposing four objective functions and applying the Artificial Bee Colony algorithm, the authors solve the constrained multi-objective optimization problem. Experimental results show that the proposed approach significantly improves the production rate and satisfies specific requirements.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2022)
Article
Engineering, Multidisciplinary
Salman Harasis, Yilmaz Sozer, Malik Elbuluk
Summary: The work presented in this article addresses the tradeoff between operating cost and reliability in microgrids by developing flexible control algorithms to enable high penetration of PV power. A power flow optimization framework is created considering all variables associated with system operation, using particle swarm optimization to effectively optimize power flow and schedule battery state of charge. The proposed approach is validated through simulations and experimental studies, demonstrating its effectiveness in achieving cost-effective and reliable operation in microgrids.
IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS
(2021)
Article
Energy & Fuels
Mohammed Said Jouda, Nihan Kahraman
Summary: This article focuses on optimizing the power response in a microgrid, including improving power sharing response and enhancing voltage and frequency stability. It proposes a self-tuning control method using the H-infinity method improved with the artificial bee colony algorithm for optimized droop control. The results are compared with other algorithms to validate the effectiveness of the optimization algorithm.
Article
Computer Science, Information Systems
Saad Motahhir, Aissa Chouder, Aboubakr El Hammoumi, Abou Soufiane Benyoucef, Abdelaziz El Ghzizal, Sofiane Kichou, Kamel Kara, Padmanaban Sanjeevikumar, Santiago Silvestre
Summary: This article proposes an efficient centralized global maximum power tracking algorithm for a multistring PV array under partial shading conditions. The algorithm, based on artificial bee colony optimization, reduces the number of required sensors and improves system efficiency.
IEEE SYSTEMS JOURNAL
(2021)
Article
Computer Science, Information Systems
Weicun Zhang, Yanan Li
Summary: A many-objective artificial bee colony algorithm based on adaptive grid (MOABCAG) is proposed to enhance solution convergence and diversity by improving the location sharing mechanism and setting an adaptive grid search method. Comparing with other algorithms, MOABCAG shows better performance in solving many-objective optimization problems.
Article
Computer Science, Information Systems
Toshiyuki Satoh, Shun Nishizawa, Jun-ya Nagase, Naoki Saito, Norihiko Saga
Summary: This paper addresses the design problem of discrete-time stable unknown input estimators (UIEs) using the artificial bee colony (ABC) algorithm for parameter optimization. A stability-guaranteed design method is presented along with a new objective function incorporating waveform-based and norm-based performance criteria to improve disturbance rejection properties. The proposed method is compared to a previous method based on estimated disturbances to confirm the improvement in disturbance rejection properties at the plant output. Additionally, a constrained ABC algorithm is combined with the original UIE design method and compared in terms of disturbance rejection properties.
INFORMATION SCIENCES
(2023)
Article
Mathematics
Zsuzsanna Nagy, Agnes Werner-Stark, Tibor Dulai
Summary: In this study, an ABC algorithm for the CARP problem was developed and proved to excel in finding high-quality solutions and being efficient. The sub-route plan operator was also shown to be more effective in finding better solutions compared to other operators.
