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14th International Conference on Swarm Intelligence (ICSI)

作者： Li, Jiacheng Noto, Masato Zhang, Yang Kanagawa Univ Dept Appl Syst & Math Yokohama Kanagawa Japan Kanagawa Univ Dept Comp Sci Yokohama Kanagawa Japan

ISBN: (纸本)9783031366215;9783031366222

The artificial bee colony (ABC) algorithm is a swarm intelligence optimization algorithm inspired by the foraging behavior of honey bee colonies, which means to gradually explore and find food-rich nectar sources through the behavioral patterns of three different bees foraging between hives. We propose an adaptive ABC (AABC) algorithm to accelerate the convergence rate by considering a colony's memory in the employed-bee phase of the algorithm, since some bees will select and explore the best nectar source, and limiting the initial search range of the swarm to search for better honey sources faster. This is done by using tent chaotic search and random backward learning in the scout-bee phase to reselect the nectar source and increase the possibility of jumping out of local optimal solution. We conducted 30 numerical experiments to verify the effectiveness of the proposed adaptive ABC algorithm.

关键词： artificial bee colony algorithm memory random backward learning tent chaotic mapping

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A reinforcement learning based artificial bee colony algorithm with application in robot path planning

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EXPERT SYSTEMS WITH APPLICATIONS 2022年 203卷

作者： Cui, Yibing Hu, Wei Rahmani, Ahmed Cent Lille CNRS UMR 9189 CRIStAL F-59651 Villeneuve Dascq France Beijing Jiaotong Univ Sch Traff & Transportat Beijing 100044 Peoples R China

artificial bee colony (ABC) algorithm is a popular optimization algorithm with excellent exploration ability and various applications. Nevertheless, its effectiveness is limited by the one-dimensional search strategy. Therefore, in order to improve its performance, a reinforcement learning (RL) based ABC algorithm is proposed (named ABC_RL). In ABC_RL, the number of dimensions to be updated in search equation of the employed bee phase is varied and adjusted intelligently via RL. Moreover, two improved search strategies are adopted to maintain a nice balance between diversification and intensification. The performance of ABC_RL is evaluated through a series of comparisons conducted on CEC 2017 benchmark problems. The results indicate that ABC_RL outperforms the compared ABC variants considering the solution accuracy. In addition, a robot path planning problem is concerned to further test the effectiveness of ABC_RL. And the comparison results show the advantages of ABC_RL in terms of path length and running time.

关键词： artificial bee colony algorithm Reinforcement learning Mittag-Leffler distribution Differential search equation Robot path planning

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A new artificial bee colony algorithm using a gradual weight method for the bi-objective traveling salesman problems

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EVOLUTIONARY INTELLIGENCE 2022年第3期15卷 2077-2088页

作者： Bouzid, Mouna Alaya, Ines Tagina, Moncef Univ Manouba Cosmos ENSI Manouba 2010 Tunisia

The traveling salesman problem (TSP) is a typical problem in combinatorial optimization. The unique objective of the traditional TSP is to minimize the tour distance. When more than one objective is taken into account to be optimized simultaneously, the problem is known as a multi-objective TSP. In the present paper, a bi-objective traveling salesman problem (BOTSP) is considered, where both the cost and the distance are taken as optimization objectives. We introduce a new algorithm that solves the BOTSP called Gw-ABC. This algorithm is based on an artificial bee colony algorithm (ABC) and uses a Gradual Weight Generation method. Firstly, we apply a gradual method to prepare the weight vectors. In fact, weight vectors values are gradually distributed in the objective space and change relatively to the optimization process from cycle to another. Secondly, we apply the ABC algorithm with the generated weights to our problem. The proposed algorithm is experimentally tested on well-known benchmark instances of different sizes and compared with other state-of-the-art algorithms. The obtained experimental results show that Gw-ABC is significantly better.

关键词： artificial bee colony algorithm Gradual weight generated vectors Bi-objective traveling salesman problem

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Particle swarm optimization and artificial bee colony algorithm for clustering and mobile based software-defined wireless sensor networks

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WIRELESS NETWORKS 2022年第4期28卷 1671-1688页

作者： Lu Sixu Wu Muqing Zhao Min Beijing Univ Posts & Telecommun Beijing Lab Adv Informat Networks Beijing 100876 Peoples R China Beijing Univ Posts & Telecommun Beijing Key Lab Network Syst Architecture & Conve Beijing 100876 Peoples R China

With the development of the internet of things, people pay more and more attention to wireless sensor networks. Designing the energy efficient routing is an essential objective for wireless sensor networks. Cluster routing is one of the most popular routing protocols to enhance the network lifetime. However, hotspot problem always exists in cluster-based routing protocol. The task of this study is designing a cluster routing protocol with mobile base station which aims at balancing the energy consumption and prolonging the network lifetime. In this article, we design a particle swarm optimization and artificial bee colony algorithm for clustering and mobile based software-defined wireless sensor networks. The software defined network architecture is used to reduce the energy overhead and computation overhead in sensor nodes. Particle swarm optimization-based cluster routing algorithm is used to calculate the cluster heads and the sojourn locations of base station. artificial bee colony algorithm-based traversal path algorithm is used to design the move path of the base station. Comparing with relevant protocols, the proposed protocol reduces the energy consumption, enhances the network lifetime and reduces the control overhead.

关键词： Software-defined wireless sensor networks Cluster routing Mobile base station Particle swarm optimization artificial bee colony algorithm

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Optimizing the material and printing parameters of the additively manufactured fiber-reinforced polymer composites using an artificial neural network model and artificial bee colony algorithm

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STRUCTURES 2022年 46卷 1781-1795页

作者： Alhaddad, Wael He, Minjuan Halabi, Yahia Almajhali, Khalil Yahya Mohammed Tongji Univ Dept Struct Engn Shanghai 200092 Peoples R China Southwest Jiaotong Univ Sch Civil Engn Chengdu 610031 Sichuan Peoples R China

Fiber-reinforced polymer composite (FRP) has always been attracting the attention of researchers due to its multiple applications as a primary material for manufacturing structural elements and parts or as a reinforcing material for the structural elements. Controlling the quality of the additively manufactured FRP is critical to guarantee an FRP material of high performance for different applications. Therefore, in this research, a machine learning model based on data collected from experimental studies was developed using the artificial neural network (ANN) modeling method to control the quality of FRP manufactured by fused deposition modeling (FDM) printing technology. ANN model predicts the tensile properties, particularly ultimate tensile strength (UTS), of the FRP as a function of 7 material and printing parameters, which are fiber tensile strength, polymer tensile strength, layer thickness, fiber volume fraction, fiber form, infill pattern, and fiber orientation. A parametric study was conducted to confirm the credibility of the developed ANN model in representing the pairwise relationship between each input parameter and the predicted UTS. The UTS of the FRP was maximized by optimizing the printing and material parameters by using the artificial bee colony (ABC) algorithm. Several case studies were conducted to prove the efficiency of the coupled ANN model and optimization algorithm. The ANN modeling method and ABC algorithm were codified and applied using MATLAB software. The results showed that the developed ANN model could accurately predict the UTS of FRP. The parametric study confirmed that the developed ANN model was valid for representing the pairwise relationships between the input parameters and the predicted UTS. Also, it was found that the fiber properties, fiber form, fiber orientation, and fiber volume fraction have the highest influence on the UTS of FRP manufactured by FDM compared with other parameters. Furthermore, the ABC optimization algor

关键词： Fiber-reinforced polymer 3D printing artificial neural network Optimization artificial bee colony algorithm

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PID controller enhanced with artificial bee colony algorithm for active magnetic bearing

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SYSTEMS SCIENCE & CONTROL ENGINEERING 2022年第1期10卷 686-697页

作者： Du, Hualong Cui, Qiuyu Liu, Pengfei Ma, Xin Wang, He Univ Sci & Technol Liaoning Sch Mech Engn & Automat Anshan Peoples R China

To reduce the effect of non-linearity in air gap control in active magnetic bearings (AMB). The PID controller for the AMB is proposed in this study, which is optimized with a reformative artificial bee colony (RABC) algorithm. The RABC algorithm balances the exploitation and exploration capabilities of the ABC algorithm by introducing globally optimal solutions and improved food source probabilities. Simulation with six benchmark functions validates the proposed algorithm, and the results reveal that the RABC algorithm has higher search accuracy and faster search speed than previous ABC algorithm versions. The experimental results show that RABC-PID outperforms the other four approaches and has greater robustness when compared to traditional PID, PSO-PID, DE-PID, and GA-PID. Meanwhile, the RABC-PID controller makes the AMB system more stable.

关键词： artificial bee colony algorithm active magnetic bearing PID controller RABC algorithm parameter optimization

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artificial bee colony algorithm based PID controller for steel stripe deviation control system

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SCIENCE PROGRESS 2022年第1期105卷 368504221075188-368504221075188页

作者： Wang, He Du, Hualong Cui, Qiuyu Song, Hua Univ Sci & Technol Liaoning Sch Mech Engn & Automat Anshan Peoples R China Univ Sci & Technol Liaoning Sch Mech Engn & Automat 189 Qianshan Middle Rd Anshan 114051 Peoples R China

To improve the performance of the PID controller for a steel strip deviation control system (SSDCS), an enhanced artificial bee colony algorithm (EABC) is proposed to optimize PID controller gains (EABC-PID). The proposed EABC changes the candidate solution equation to balance its explorative and exploitative capabilities. The experiment presents a detailed comparison of EABC-PID and four bio-inspired algorithms based PID controllers considering four types of objective functions. Simulation results show that EABC-PID proves to be superior for SSDCS compared to four bio-inspired algorithms based PID controller in terms of convergence, dynamic adjustment, and robustness.

关键词： PID controller gains optimization artificial bee colony algorithm steel strip deviation control system control method

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artificial bee colony algorithm based on Dominance (ABCD) for a hybrid gene selection method

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KNOWLEDGE-BASED SYSTEMS 2020年 205卷 106323-106323页

作者： Coleto-Alcudia, Veredas Vega-Rodriguez, Miguel A. Univ Extremadura Dept Comp & Commun Technol Campus Univ S-N Caceres 10003 Spain

In cancer research, it is important to classify tissue samples in different classes (normal, tumour, tumour type, etc.). Gene selection purpose is to find the minimum number of genes that can predict sample classes with efficacy. This work is focused on the gene selection problem by introducing a new hybrid method. This new method combines a first step of gene filtering with an optimization algorithm in a second step to find the best subset of genes for the classification task. The first step uses the Analytic Hierarchy Process, in which five ranking methods are used to select the most relevant genes in the dataset. In this way, this gene filtering reduces the number of genes to manage. Regarding the second step, the gene selection can be divided into two objectives: minimizing the number of selected genes and maximizing the classification accuracy. Therefore, we have used a multi-objective optimization approach. More exactly, an artificial bee colony based on Dominance (ABCD) algorithm has been proposed for this second step. Our approach has been tested with eleven real cancer datasets and the results have been compared with several multi-objective methods proposed in the scientific literature. Our results show a high accuracy in the classification task with a small subset of genes. Also, to prove the relevance of our proposal, a biological analysis has been developed on the genes selected. The conclusions of this biological analysis are positive, because the selected genes are closely linked to the cancer dataset they belong to. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Gene selection Multi-objective optimization artificial bee colony algorithm Cancer Support vector machine Analytic hierarchy process

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Applying artificial bee colony algorithm to the multidepot vehicle routing problem

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SOFTWARE-PRACTICE & EXPERIENCE 2022年第3期52卷 756-771页

作者： Gu, Zhaoquan Zhu, Yan Wang, Yuexuan Du, Xiaojiang Guizani, Mohsen Tian, Zhihong Guangzhou Univ Cyberspace Inst Adv Technol Guangzhou Peoples R China Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China Zhejiang Univ Coll Comp Sci & Technol Hangzhou Peoples R China Temple Univ Dept Comp & Informat Sci Philadelphia PA 19122 USA Qatar Univ Comp Sci & Engn Dept Doha Qatar

With advanced information technologies and industrial intelligence, Industry 4.0 has been witnessing a large scale digital transformation. Intelligent transportation plays an important role in the new era and the classic vehicle routing problem (VRP), which is a typical problem in providing intelligent transportation, has been drawing more attention in recent years. In this article, we study multidepot VRP (MDVRP) that considers the management of the vehicles and the optimization of the routes among multiple depots, making the VRP variant more meaningful. In addressing the time efficiency and depot cooperation challenges, we apply the artificial bee colony (ABC) algorithm to the MDVRP. To begin with, we degrade MDVRP to single-depot VRP by introducing depot clustering. Then we modify the ABC algorithm for single-depot VRP to generate solutions for each depot. Finally, we propose a coevolution strategy in depot combination to generate a complete solution of the MDVRP. We conduct extensive experiments with different parameters and compare our algorithm with a greedy algorithm and a genetic algorithm (GA). The results show that the ABC algorithm has a good performance and achieve up to 70% advantage over the greedy algorithm and 3% advantage over the GA.

关键词： artificial bee colony algorithm coevolution strategy depot clustering multidepot vehicle routing problem

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Dual search mode artificial bee colony algorithm based on exploration-exploitation tradeoff and its application

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Kongzhi yu Juece/Control and Decision 2025年第2期40卷 507-516页

作者： Wang, Ying-Cong Li, Bo Sun, Jun-Wei Wang, Yan-Feng School of Electrical and Information Engineering Zhengzhou University of Light Industry Zhengzhou450002 China

The artificial bee colony algorithm has the advantages of a simple structure and easy implementation. However, due to its strong exploration ability and weak exploitation capability, it also suffers from drawbacks such as low solution accuracy and slow convergence speed. To solve these problems, this paper proposes a dual search mode artificial bee colony algorithm based on exploration-exploitation tradeoff, which includes three core modules: exploration-exploitation control, exploration-exploitation execution, and exploration-exploitation reinforcement. In the control stage, we design an exploration-exploitation indicator based on the population’s evolutionary dynamics, which initially emphasizes exploration and gradually shifts towards exploitation. In the execution stage, we design search equations guided by differential solution information, with a focus on the implementation points of exploration and exploitation. In the enhancement stage, the onlooker bees are instructed to select high-quality food sources for further search, using criteria such as diversity ranking and fitness ranking. Numerical experiments are conducted on traditional test functions and CEC2013 test functions. The results demonstrate that compared with eight high-level artificial bee colony algorithms proposed in recent years, the proposed algorithm exhibits strong competitiveness in terms of solution quality and convergence speed. Finally, the proposed algorithm is applied to optimize the risk prediction model for esophageal cancer, yielding satisfactory results. © 2025 Northeast University. All rights reserved.

关键词： artificial bee colony algorithm esophageal cancer prediction exploration-exploitation tradeoff function optimization

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