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RETRACTED: Improved artificial bee colony algorithm with opposition-based learning (Retracted article. See vol. 17, pg. 301, 2023)

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IET IMAGE PROCESSING 2020年第15期14卷 3639-3650页

作者： Cao, Yongcun Ji, Saisai Lu, Yong Minzu Univ China Sch Informat Engn Beijing 100081 Peoples R China

The artificial bee colony (ABC) algorithm is a biological-inspired optimisation algorithm proposed by Karaboga. Since its solution search equation is good at exploration but poor at exploitation, the ABC algorithm converges slowly and is easy to fall into local optimum. Inspired by opposition-based learning (OBL), the authors propose an improved ABC algorithm called opposition-based learning ABC (OLABC). In OLABC, firstly, the population would be initialised using OBL. Secondly, to ensure the diversity of the population during the iterative process, the solution search equation is employed to bee phase would be improved. Generate the opposite solution when the fitness value of the newly generated solution is smaller than the current solution, and then apply the greedy selection strategy to update the solution. Thirdly, the adaptive weight strategy is used to dynamically adjust the weight, balancing the global exploration and local exploitation capabilities of the algorithm. Experiments on a set of benchmark functions show that OLABC has better convergence speed and optimisation precision than the compared algorithms.

关键词： learning (artificial intelligence) search problems convergence artificial bee colony algorithm greedy algorithms iterative methods global exploration adaptive weight strategy iterative process greedy selection strategy opposition-based learning ABC local exploitation capabilities bee phase OLABC ABC algorithm OBL solution search equation biological-inspired optimisation algorithm artificial bee colony algorithm

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NSABC: Non-dominated sorting based multi-objective artificial bee colony algorithm and its application in data clustering

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NEUROCOMPUTING 2016年 216卷 514-533页

作者： Kishor, Avadh Singh, Pramod Kumar Prakash, Jay ABV IIITM Computat Intelligence & Data Min Res Lab Gwalior 474015 India

This paper presents a non-dominated sorting based multi-objective artificial bee colony algorithm NSABC to solve multi-objective optimization problems. It is an extension of the artificial bee colony algorithm ABC, which is a single objective optimization algorithm, to the multi-objective optimization domain. It uses a novel approach in the employee bee phase to steer the solutions to simultaneously achieve both the orthogonal goals in the multi-objective optimization - convergence and diversity. The onlooker bee phase is similar to the ABC except for the fitness computation to exploit the promising solutions whereas there is no change in the scout bee phase, which is used to get rid of bad solutions and add diversity in the swarm by introducing random solutions. Along with a novel way of exploring new solutions, it uses non-dominated sorting and crowding distance, inspired by the NSGA-II, to maintain the best and diverse solutions in the swarm. It is tested on the 10 two-objective and three-objective unconstrained benchmark problems of varying nature and complexities from the CECO9 suite of test problems and is found better than or commensurable to thirteen state-of-the-art significant multi-objective optimization algorithms as well as other multi-objective variants of the ABC. Further, it is tested on the nine real-life data clustering problems considered from the UCI machine learning repository and proved itself better in comparison to the NSGA-II, MOVGA, and a recent multi-objective variant of the ABC named MOABC. Thus, it is observed that the NSABC is comparatively a simple, light, and powerful algorithm to solve multi-objective problems. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Multi-objective optimization artificial bee colony algorithm Non-dominated sorting Crowding distance Augmented population

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An Adaptive Multi-Strategy artificial bee colony algorithm for Integrated Process Planning and Scheduling

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IEEE ACCESS 2021年 9卷 65622-65637页

作者： Cao, Yang Shi, Haibo Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China Chinese Acad Sci Shenyang Inst Automat Shenyang 110016 Peoples R China Chinese Acad Sci Inst Robot & Intelligent Mfg Shenyang 110169 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Chinese Acad Sci Key Lab Network Control Syst Shenyang 110016 Peoples R China Shenyang Jianzhu Univ Informat & Control Engn Fac Shenyang 110168 Peoples R China

Traditionally, process planning and scheduling were performed sequentially, where scheduling depended on the result of process planning. Considering their complementarity, the two functions are more tightly integrated to improve the performance of job shop flexible manufacturing environment. This study proposes an adaptive multi-strategy artificial bee colony (AMSABC) algorithm to solve integrated process planning and scheduling (IPPS) problem. In AMSABC, two search strategies with different characteristics are introduced into employed bees and onlooker bees to take on the responsibility of both exploration and exploitation. The selection probability of each search strategy is dynamically adjusted according to previous experiences. To further improve the exploitation performance of the approach, a problem-specific multi-objective local search has been embedded in the proposed algorithm. Furthermore, AMSABC algorithm presents a unique solution representation where a food source is represented by three discrete vectors, and a well-designed decoding scheme is developed. Next, the corresponding neighborhood structure is adopted that it can directly generate feasible solutions in the search space. The proposed algorithm is tested on the well-known benchmark instances and compared with the state-of-the-art algorithms. Through detail analysis of experimental results, AMSABC algorithm is more beneficial in the quality and efficiency of solution.

关键词： Process planning Job shop scheduling Search problems Scheduling Optimization Heuristic algorithms artificial bee colony algorithm Integrated process planning and scheduling artificial bee colony algorithm multi-objective optimization multi-strategy collaboration strategy adaptation

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Solving human-robot collaborative mixed-model two-sided assembly line balancing using multi-objective discrete artificial bee colony algorithm

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COMPUTERS & INDUSTRIAL ENGINEERING 2024年 187卷

作者： Huang, Yuzhe Sheng, Buyun Luo, Ruiping Lu, Yingkang Fu, Gaocai Yin, Xiyan Wuhan Univ Technol Dept Mech & Elect Engn Wuhan Peoples R China Hubei Univ Technol Dept Mech Engn Wuhan Peoples R China

Human-robot collaboration (HRC) is an emerging technology that aims to optimize production efficiency and reduce ergonomic risks by enabling humans and collaborative robots to work together in a shared workspace. This paper investigates HRC mixed-model two-sided assembly line balancing problem, which is a challenging real-world problem in manufacturing systems. The problem is formulated using a mixed-integer programming model with the objectives of minimizing ergonomic risk, energy consumption, and cycle time. To solve the problem efficiently, a multi-objective discrete artificial bee colony algorithm with specialist bees (MDABCSB) is proposed. MDABCSB utilizes a new five-vector encoding and decoding scheme and incorporates two types of neighbor structures for each vector. Additionally, the algorithm introduces specialist bees to search for the optimal solution for each objective, combines a tabu list to help them jump out of local optima, and employs variable neighborhood descent to enhance their exploration capabilities. Furthermore, the algorithm adopts a new temperature-based food sources update mechanism to probabilistically update the food sources of employed bees to reinforce exploitation. By comparing the proposed algorithm with other algorithms, the results demonstrate that MDABCSB performs better in terms of solution performance.

关键词： Two-sided assembly line balancing Human -robot collaboration Collaborative robots artificial bee colony algorithm Ergonomic risk Energy consumption

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Optimisation of Multiple Hydropower Reservoir Operation Using artificial bee colony algorithm

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WATER RESOURCES MANAGEMENT 2017年第4期31卷 1397-1411页

作者： Choong, Shi-Mei El-Shafie, A. Mohtar, W. H. M. Wan Univ Kebangsaan Malaysia Fac Engn & Built Environm Civil & Struct Engn Dept Ukm Bangi 43600 Selangor Darul Malaysia Univ Malaya Fac Engn Dept Civil Engn Jalan Univ Kuala Lumpur 50603 Malaysia

In this study, the artificial bee colony (ABC) algorithm was developed to solve the Chenderoh Reservoir operation optimisation problem which located in the state of Perak, Malaysia. The proposed algorithm aimed to minimise the water deficit in the operating system and examine its performance impact based on monthly and weekly data input. Due to its capability to identify different possible events occurring in the reservoir, the ABC algorithm provides promising and comparable solutions for optimum release curves. The optimal release curves were then used to stimulate the reservoir release under different operating times under different inflow scenarios. To investigate the performance of both the monthly and weekly ABC optimisation employed in the reservoir, the well-known reliability, resilience and vulnerability indices were used for performance assessment. The indices tests revealed that weekly ABC optimisation outperformed in terms of reliability and vulnerability leading to the development of a better release policy for optimal operation.

关键词： Reservoir optimisation artificial bee colony algorithm Operation system Simulation Reliability

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An elitism based multi-objective artificial bee colony algorithm

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2015年第1期245卷 168-193页

作者： Xiang, Yi Zhou, Yuren Liu, Hailin Sun Yat Sen Univ Sch Adv Comp Guangzhou 510275 Guangdong Peoples R China Guangdong Baiyun Univ Dept Adv Math Guangzhou 510450 Guangdong Peoples R China Guangdong Univ Technol Dept Appl Math Guangzhou 510090 Guangdong Peoples R China

In this paper, we suggest a new multi-objective artificial bee colony (ABC) algorithm by introducing an elitism strategy. The algorithm uses a fixed-size archive that is maintained based on crowding-distance to store non-dominated solutions found during-the search process. In the proposed algorithm, an improved artificial bee colony algorithm with an elitism strategy is adopted for the purpose of avoiding premature convergence. Specifically, the elites in the archive are selected and used to generate new food sources in both employed and onlooker bee phases in each cycle. To keep diversity, a member located at the most crowded region will be removed when the archive overflows. The algorithm is very easy to be implemented and it employs only a few control parameters. The proposed algorithm is tested on a wide range of multi-objective problems, and compared with other state-of-the-art algorithms in terms of often-used quality indicators with the help of a nonparametric test. It is revealed by the test procedure that the algorithm produces better or comparable results when compared with other well-known algorithms, and it can be used as a promising alternative tool to solve multi-objective problems with the advantage of being simple and effective. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Heuristics Multi-objective optimization Elitism strategy artificial bee colony algorithm

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Web Service Selection Using Modified artificial bee colony algorithm

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IEEE ACCESS 2019年 7卷 88673-88684页

作者： Chandra, Manik Niyogi, Rajdeep ITT Roorkee Dept Comp Sci & Engn Roorkee 247667 Uttar Pradesh India

Web services are a type of application software, which can be remotely accessed through the Internet. Due to the proliferating growth of web services of the same functionality, the user goes into a dilemma to select suitable service for him. In this paper, we study the web service selection (WSS) problem in a sequential composition model. We formulated the WSS as a constrained optimization problem. To solve the problem, we suggest a modified artificial bee colony (mABC) algorithm, which uses a chaotic-based opposition learning method to generate a better initial population. To improve the exploration capability of the mABC, a new search equation for employed bee phase is suggested. On the other hand, to improve the exploitation capacity of the mABC, a new search strategy, inspired by differential evolution (DE), is adopted in the onlooker bee phase. We test the mABC on synthetic web service selection problem taken from QWS dataset. To assess the relative performance of the mABC, we compare it against five other state-of-the-art algorithms. The experimental results show that the mABC is better than other existing approaches in terms of response time, latency, availability, and reliability.

关键词： Web service selection artificial bee colony algorithm chaotic map QoS attributes

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A hybrid artificial bee colony algorithm for the cooperative maximum covering location problem

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2017年第2期8卷 691-697页

作者： Jayalakshmi, B. Singh, Alok Univ Hyderabad Sch Comp & Informat Sci Hyderabad 500046 Andhra Pradesh India

This paper proposes a hybrid artificial bee colony algorithm for the cooperative maximum covering location problem (CMCLP) on a network. In location covering problems, it is assumed that each facility generates a signal whose strength decreases with the increase in distance and a demand point is considered to be covered if the total signal strength received by it from various facilities exceeds a certain threshold. The objective of the CMCLP is to locate the facilities in such a way that maximizes the total demands covered. The proposed hybrid approach obtained better quality solutions in comparison to the methods available in the literature.

关键词： artificial bee colony algorithm Cooperative maximum covering location problem Heuristic Networks Swarm intelligence

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Design and development of a hybrid artificial bee colony algorithm for the environmental vehicle routing problem

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TRANSPORTATION RESEARCH PART D-TRANSPORT AND ENVIRONMENT 2014年 31卷 85-99页

作者： Zhang, Shuzhu Lee, C. K. M. Choy, K. L. Ho, William Ip, W. H. Hong Kong Polytech Univ Dept Ind & Syst Engn Kowloon Hong Kong Peoples R China Univ Melbourne Dept Management & Mkt Melbourne Vic Australia

The vehicle routing problem (VRP) is a critical and vital problem in logistics for the design of an effective and efficient transportation network, within which the capacitated vehicle routing problem (CVRP) has been widely studied for several decades due to the practical relevance of logistics operation. However, CVRP with the objectives of minimizing the overall traveling distance or the traveling time cannot meet the latest requirements of green logistics, which concern more about the influence on the environment. This paper studies CVRP from an environmental perspective and introduces a new model called environmental vehicle routing problem (EVRP) with the aim of reducing the adverse effect on the environment caused by the routing of vehicles. In this research, the environmental influence is measured through the amount of the emission carbon dioxide, which is a widely acknowledged criteria and accounts for the major influence on environment. A hybrid artificial bee colony algorithm (ABC) is designed to solve the EVRP model, and the performance of the hybrid algorithm is evaluated through comparing with well-known CVRP instances. The computational results from numerical experiments suggest that the hybrid ABC algorithm outperforms the original ABC algorithm by 5% on average. The transformation from CVRP to EVRP can be recognized through the differentiation of their corresponding optimal solutions, which provides practical insights for operation management in green logistics. (C) 2014 Published by Elsevier Ltd.

关键词： Green logistics Capacitated vehicle routing problem artificial bee colony algorithm Environmental influence Environmental vehicle routing problem CO2 emission

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An efficient hybrid artificial bee colony algorithm for disassembly line balancing problem with sequence-dependent part removal times

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ENGINEERING OPTIMIZATION 2019年第11期51卷 1920-1937页

作者： Wang, Shuwei Guo, Xiuping Liu, Jia Southwest Jiaotong Univ Sch Econ & Management Chengdu Peoples R China Qingdao Univ Technol Business Sch Qingdao Peoples R China

This study deals with a sequence-dependent disassembly line balancing problem by considering the interactions among disassembly tasks, and a multi-objective mathematical model is established. Subsequently, a novel hybrid artificial bee colony algorithm is proposed to solve the problem. A new rule is used to initialize a bee colony population with certain diversity, and a dynamic neighbourhood search method is introduced to guide the employed/onlooker bees to promising regions. To rapidly leave the local optima, a global learning strategy is employed to explore higher quality solutions. In addition, a multi-stage evaluation method is designed for onlookers to effectively select employed bees to follow. The performance of the proposed algorithm is tested on a set of benchmark instances and two case scenarios, and the results are compared with several other metaheuristics in terms of solution quality and computation time. The comparisons demonstrate that the proposed algorithm exhibits superior performance.

关键词： Disassembly sequence-dependent disassembly line balancing artificial bee colony algorithm dynamic neighbourhood search

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