A hybrid artificial bee colony algorithm for optimal selection of QoS-based cloud manufacturing service composition
01 Feb 2017-The International Journal of Advanced Manufacturing Technology (Springer London)-Vol. 88, Iss: 9, pp 3371-3387
TL;DR: This study proposes a new approach for CCSOS problems, the so-called hybrid artificial bee colony (HABC) algorithm, which employs both the probabilistic model of Archimedean copula estimation of distribution algorithm (ACEDA) and the chaos operators of global best-guided artificialbee colony to generate the offspring individuals with consideration of quality of service (QoS) and CMfg environment.
Abstract: With the advent of cloud manufacturing (CMfg), more and more services in CMfg platforms may provide the same functionality but differ in performance. In order to insure the manufacturing cloud to match the complicated task requirements, composited CMfg service optimal selection (CCSOS) is becoming increasingly important. This study proposes a new approach for such CCSOS problems, the so-called hybrid artificial bee colony (HABC) algorithm, which employs both the probabilistic model of Archimedean copula estimation of distribution algorithm (ACEDA) and the chaos operators of global best-guided artificial bee colony to generate the offspring individuals with consideration of quality of service (QoS) and CMfg environment. Different-scale CCSOS problems are adopted to evaluate the performance of the proposed HABC. Experimental results have shown that the HABC can find better solutions compared with such algorithms as genetic algorithm, particle swarm optimization, and basic artificial bee colony algorithm.
Citations
More filters
TL;DR: A digital twin and its application are introduced along with the development of intelligent manufacturing based on the digital twin technology and the future development direction of intelligent Manufacturing is presented.
Abstract: As the next-generation manufacturing system, intelligent manufacturing enables better quality, higher productivity, lower cost, and increased manufacturing flexibility. The concept of sustainability is receiving increasing attention, and sustainable manufacturing is evolving. The digital twin is an emerging technology used in intelligent manufacturing that can grasp the state of intelligent manufacturing systems in real-time and predict system failures. Sustainable intelligent manufacturing based on a digital twin has advantages in practical applications. To fully understand the intelligent manufacturing that provides the digital twin, this study reviews both technologies and discusses the sustainability of intelligent manufacturing. Firstly, the relevant content of intelligent manufacturing, including intelligent manufacturing equipment, systems, and services, is analyzed. In addition, the sustainability of intelligent manufacturing is discussed. Subsequently, a digital twin and its application are introduced along with the development of intelligent manufacturing based on the digital twin technology. Finally, combined with the current status, the future development direction of intelligent manufacturing is presented.
253 citations
TL;DR: A state-of-the-art literature survey on scheduling issues in cloud manufacturing and a detailed statistical analysis of the literature is provided based on the data gathered from the Elsevier’s Scopus abstract and citation database.
Abstract: For the past eight years, cloud manufacturing as a new manufacturing paradigm has attracted a large amount of research interest worldwide. The aim of cloud manufacturing is to deliver on-demand man ...
192 citations
Cites background or methods from "A hybrid artificial bee colony algo..."
...Zhou and Yao (2017c) Artificial bee colony algorithm Single composite task Time, cost, availability, reliability Zhou and Yao (2017a) Artificial bee colony algorithm Single composite task Time, price, availability, reputation Huang, Li, and Tao (2014) Chaos optimization Single composite task Time,…...
[...]
...…Execution time, latency time, cost, reliability, availability Xu and Sun (2016) Bat algorithm Single composite task Time, availability, reliability Zhou and Yao (2017b) Bee colony algorithm Single composite task Time, price, availability, reputation, energy consumption Xu et al. (2016) Bees…...
[...]
...…their matching method have also been taken into account, including energy consumption (Xiang et al. 2014; Xiang, Xu, and Jiang 2016), correlations (Li, Jiang, and Ge 2014; Jin, Yao, and Chen 2015; Li et al. 2016; Xu et al. 2016; Zhou and Yao 2017d), execution reliability (Jing et al. 2014), etc....
[...]
...Zhou and Yao (2017a) proposed a context-aware artificial bee colony (ABC) algorithm based on the principle of ABC and service features in cloud manufacturing, which takes into account dynamics of trust QoS....
[...]
TL;DR: A hybrid artificial bee colony (ABC) algorithm to solve a parallel batching distributed flow-shop problem (DFSP) with deteriorating jobs is proposed, which is favorably compared against several algorithms in terms of both solution quality and population diversity.
Abstract: In this article, we propose a hybrid artificial bee colony (ABC) algorithm to solve a parallel batching distributed flow-shop problem (DFSP) with deteriorating jobs. In the considered problem, there are two stages as follows: 1) in the first stage, a DFSP is studied and 2) after the first stage has been completed, each job is transferred and assembled in the second stage, where the parallel batching constraint is investigated. In the two stages, the deteriorating job constraint is considered. In the proposed algorithm, first, two types of problem-specific heuristics are proposed, namely, the batch assignment and the right-shifting heuristics, which can substantially improve the makespan. Next, the encoding and decoding approaches are developed according to the problem constraints and objectives. Five types of local search operators are designed for the distributed flow shop and parallel batching stages. In addition, a novel scout bee heuristic that considers the useful information that is collected by the global and local best solutions is investigated, which can enhance searching performance. Finally, based on several well-known benchmarks and realistic industrial instances and via comprehensive computational comparison and statistical analysis, the highly effective performance of the proposed algorithm is favorably compared against several algorithms in terms of both solution quality and population diversity.
123 citations
01 Jul 2017
TL;DR: The proposed algorithm adopts multiple parallel subpopulations, each of which evolves according to different mutation strategies borrowed from the differential evolution to generate perturbed food sources for foraging bees, and the control parameters of each mutation strategy are adapted independently.
Abstract: Process of manufacturing service composition.Display Omitted A novel hybrid differential artificial bee colony algorithm for service composition in cloud manufacturing is proposed.Multiple subpopulations with distinct hybrid evolutionary operators are adopted during the evolution process.The size of each subpopulation is adaptively adjusted based on the information derived from its search process.The control parameters of each evolution operator are adapted independently.The proposed algorithm outperforms the state-of-the-art approaches known in the literature. As a new service-oriented smart manufacturing paradigm, cloud manufacturing (CMfg) aims at fully sharing and circulation of manufacturing capabilities towards socialization, in which composite CMfg service optimal selection (CCSOS) involves selecting appropriate services to be combined as a composite complex service to fulfill a customer need or a business requirement. Such composition is one of the most difficult combination optimization problems with NP-hard complexity. For such an NP-hard CCSOS problem, this study proposes a new approach, called multi-population parallel self-adaptive differential artificial bee colony (MPsaDABC) algorithm. The proposed algorithm adopts multiple parallel subpopulations, each of which evolves according to different mutation strategies borrowed from the differential evolution (DE) to generate perturbed food sources for foraging bees, and the control parameters of each mutation strategy are adapted independently. Moreover, the size of each subpopulation is dynamically adjusted based on the information derived from the search process. Different scales of the CCSOS problems are conducted to validate the effectiveness of the proposed algorithm, and the experimental results show that the proposed algorithm has superior performance over other hybrid and single population algorithms, especially for complex CCSOS problems.
111 citations
TL;DR: A multi-objective hybrid artificial bee colony algorithm for service composition and optimal selection (SCOS) in cloud manufacturing, in which both the quality of service and the energy consumption are considered from the perspectives of economy and environment that are two pillars of sustainable manufacturing.
Abstract: This paper proposes a multi-objective hybrid artificial bee colony (MOHABC) algorithm for service composition and optimal selection (SCOS) in cloud manufacturing, in which both the quality of service and the energy consumption are considered from the perspectives of economy and environment that are two pillars of sustainable manufacturing. The MOHABC uses the concept of Pareto dominance to direct the searching of a bee swarm, and maintains non-dominated solution found in an external archive. In order to achieve good distribution of solutions along the Pareto front, cuckoo search with Levy flight is introduced in the employed bee search to maintain diversity of population. Furthermore, to ensure the balance of exploitation and exploration capabilities for MOHABC, the comprehensive learning strategy is designed in the onlooker search so that every bee learns from the external archive elite, itself and other onlookers. Experiments are carried out to verify the effect of the improvement strategies and paramet...
98 citations
Cites methods from "A hybrid artificial bee colony algo..."
...Zhou and Yao (2017) adopted an Archimedean copula estimation of distribution method improved ABC algorithm to solve CMfg service composition....
[...]
...…have been applied to solve the problem (Laili et al. 2013; Tao et al. 2013; Huang, Li, and Tao 2014) such as genetic algorithm (Tao et al. 2012; Seghir and Khababa 2016), ant colony optimisation (Huang et al. 2016) and artificial bee colony algorithm (Xue, Liu, and Wang 2016; Zhou and Yao 2017)....
[...]
References
More filters
TL;DR: Artificial Bee Colony (ABC) Algorithm is an optimization algorithm based on the intelligent behaviour of honey bee swarm that is used for optimizing multivariable functions and the results showed that ABC outperforms the other algorithms.
Abstract: Swarm intelligence is a research branch that models the population of interacting agents or swarms that are able to self-organize. An ant colony, a flock of birds or an immune system is a typical example of a swarm system. Bees' swarming around their hive is another example of swarm intelligence. Artificial Bee Colony (ABC) Algorithm is an optimization algorithm based on the intelligent behaviour of honey bee swarm. In this work, ABC algorithm is used for optimizing multivariable functions and the results produced by ABC, Genetic Algorithm (GA), Particle Swarm Algorithm (PSO) and Particle Swarm Inspired Evolutionary Algorithm (PS-EA) have been compared. The results showed that ABC outperforms the other algorithms.
6,377 citations
01 Jan 2008
TL;DR: The simulation results show that the performance of ABC algorithm is comparable to those of differential evolution, particle swarm optimization and evolutionary algorithm and can be efficiently employed to solve engineering problems with high dimensionality.
Abstract: Artificial bee colony (ABC) algorithm is an optimization algorithm based on a particular intelligent behaviour of honeybee swarms. This work compares the performance of ABC algorithm with that of differential evolution (DE), particle swarm optimization (PSO) and evolutionary algorithm (EA) for multi-dimensional numeric problems. The simulation results show that the performance of ABC algorithm is comparable to those of the mentioned algorithms and can be efficiently employed to solve engineering problems with high dimensionality.
3,242 citations
TL;DR: This paper presents a middleware platform which addresses the issue of selecting Web services for the purpose of their composition in a way that maximizes user satisfaction expressed as utility functions over QoS attributes, while satisfying the constraints set by the user and by the structure of the composite service.
Abstract: The paradigmatic shift from a Web of manual interactions to a Web of programmatic interactions driven by Web services is creating unprecedented opportunities for the formation of online business-to-business (B2B) collaborations. In particular, the creation of value-added services by composition of existing ones is gaining a significant momentum. Since many available Web services provide overlapping or identical functionality, albeit with different quality of service (QoS), a choice needs to be made to determine which services are to participate in a given composite service. This paper presents a middleware platform which addresses the issue of selecting Web services for the purpose of their composition in a way that maximizes user satisfaction expressed as utility functions over QoS attributes, while satisfying the constraints set by the user and by the structure of the composite service. Two selection approaches are described and compared: one based on local (task-level) selection of services and the other based on global allocation of tasks to services using integer programming.
2,872 citations
TL;DR: Results show that the performance of the ABC is better than or similar to those of other population-based algorithms with the advantage of employing fewer control parameters.
2,835 citations