An artificial bee colony algorithm for multi-objective optimisation

Luo, Jianping; Liu, Qiqi; Yang, Yun; Li, Xia; Chen, Min-Rong; Cao, Wenming

doi:10.1016/j.asoc.2016.11.014

Cited by 86 publications

(36 citation statements)

References 40 publications

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“…Consider the general form of a constrained multi-objective optimization problem [46,47], as Multi-Objective Evolutionary Algorithms (MOEAs) are robust and efficient multi-objective optimization algorithms, however, EAs do not have any explicit mechanism to handle constraints while most real-world design multi-objective optimization problems have multiple constraints [48]. The penalty function method is a traditional approach for handling the constraints of singleobjective optimization problems.…”

Section: Constrain Handling Methods For the Mocssmentioning

confidence: 99%

Application of Multi-objective Charged System Search Algorithm for Optimization Problems

2018

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Section: Constrain Handling Methods For the Mocssmentioning

confidence: 99%

Application of Multi-objective Charged System Search Algorithm for Optimization Problems

2018

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“…Namely, an improved model (ISRM) method is developed. In this method, an artificial bee colony (ABC) algorithm [26] is used to find the optimal parameters of SR models. e reliability optimization of a turbine blade deformation is completed by the proposed ISRM method with a multipopulation genetic algorithm (MPGA) [27].…”

Section: Introductionmentioning

confidence: 99%

Reliability Optimization of Structural Deformation with Improved Support Vector Regression Model

Zhu

Feng

et al. 2020

Advances in Materials Science and Engineering

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Deformation is one important failure mode of turbine blades. e quality of a model seriously influences the reliability optimization of turbine blades in turbo machines. To improve the reliability optimization of turbine blades, this paper proposes a novel machine learning-based reliability optimization approach, named improved support vector regression (SR) model (ISRM) method, by fusing artificial bee colony (ABC), traditional SR model, and multipopulation genetic algorithm (MPGA). In this proposed method, the ABC algorithm was applied to find the optimal parameters in the SR model to establish accurate ISRM, following the thought of the surrogate model method and the randomness of constraints. en the reliability optimization model and procedure with the ISRM method were resolved by the MPGA. Regarding many design parameters (i.e., rotor speed, temperature, and aerodynamic pressure), design objective (i.e., blade deformation), and the randomness constraints of reliability degree and boundary conditions, we performed the reliability optimization of a turbine blade deformation. From the optimization results, we find that the turbine blade deformation is reduced by 0.09329 mm, and the ISRM learning method can improve the reliability optimization design of complex structures with the emphasis on modeling precision and optimization efficiency. e works of this paper provide a machine learning-based reliability optimization approach for the reliability optimization of complex structures and enrich and develop mechanical reliability theory and methods.

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“…Some scholars have put forward some improvements, which mainly involve the enhancement of the initial solution, selection strategy, update strategy [24,25], operation mode [26,27], and hybrid algorithm [28]. Because the ABC algorithm is a kind of unconstrained optimization algorithm, some scholars have applied it to the constrained optimization problem [28,29] and multiobjective optimization problem [30][31][32][33], for the search strategy formula of basic ABC algorithm; that is, only one individual and one dimension are selected randomly at each update. This paper uses the evolutionary ideas of particle swarm optimization (PSO), combining with the current location [34], individual best value, and global optimal value, and introduces the linearly decreasing inertia weight.…”

Section: Artificial Bee Colony Algorithmmentioning

confidence: 99%

Research on Dynamic Facility Layout Problem of Manufacturing Unit Considering Human Factors

Tan

2018

Mathematical Problems in Engineering

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As many said, industry 4.0 is an epoch-making revolution which brought the manufacturing market much faster changes and severer competitions. As an important part of the manufacturing system, facility layout has direct impact on business benefit; at the same time, despite the intelligent factory, intelligent production has its own characteristics. However, there is one point on which industry and academia have basically formed a consensus: it is not true that industry 4.0 does not need human beings; on the contrary, human initiative plays an unabated role in the development of industry 4.0. This paper will focus on the dynamic facility layout of the manufacturing unit. Based on the system above and the traditional optimization model, a mathematic model is built to find the best solution combining safety, sustainability, high efficiency, and low cost. And penalty function with adaptive penalty factor and advanced artificial bee colony algorithm is used to solve the constrained model. In the end, by studying few cases, the model is proved to be effective in both efficiency improvement and the implementation of safe and comfort human-machine interaction.

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An artificial bee colony algorithm for multi-objective optimisation

Cited by 86 publications

References 40 publications

Application of Multi-objective Charged System Search Algorithm for Optimization Problems

Application of Multi-objective Charged System Search Algorithm for Optimization Problems

Reliability Optimization of Structural Deformation with Improved Support Vector Regression Model

Research on Dynamic Facility Layout Problem of Manufacturing Unit Considering Human Factors

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