A novel hybrid multi-objective immune algorithm with adaptive differential evolution

Lin, Qiuzhen; Zhu, Qingling; Huang, Pei-Zhi; Ming, Zhong; Yu, Jianping

doi:10.1016/j.cor.2015.04.003

Cited by 108 publications

(26 citation statements)

References 50 publications

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“…A novel MOEA to solve many-objective optimisation problems (MaOPs) was proposed by Zhang and Li [58] namely a multi-objective evolutionary algorithm based on decomposition (MOEA/D). The algorithm was found to perform very well on a number of MOPs.…”

Section: Amoea/d-dementioning

confidence: 99%

“…If the value of is high, the trial vector will be more closely similar to the mutant vector than the base vector crating more solution diversity. Lin, et al [58] suggested that the values of should be set at high during the beginning phase of the evolutionary search (exploration) and continuously decrease to a low value during the termination phase (exploitation). The regression equation was proposed to find appropriate values of .…”

Section: Control Parameter Adaptationmentioning

confidence: 99%

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Solving Many-Objective Car Sequencing Problems on Two-Sided Assembly Lines Using an Adaptive Differential Evolutionary Algorithm

Chutima¹,

Kirdphoksap²

2019

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The car sequencing problem (CSP) is addressed in this paper. The original environment of the CSP is modified to reflect real practices in the automotive industry by replacing the use of single-sided straight assembly lines with two-sided assembly lines. As a result, the problem becomes more complex caused by many additional constraints to be considered. Six objectives (i.e. many objectives) are optimised simultaneously including minimising the number of colour changes, minimising utility work, minimising total idle time, minimising the total number of ratio constraint violations and minimising total production rate variation. The algorithm namely adaptive multi-objective evolutionary algorithm based on decomposition hybridised with differential evolution algorithm (AMOEA/D-DE) is developed to tackle this problem. The performances in the Pareto sense of AMOEA/D-DE are compared with COIN-E, MODE, MODE/D and MOEA/D. The results indicate that AMOEA/D-DE outperforms the others in terms of convergencerelated metrics.

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Section: Amoea/d-dementioning

confidence: 99%

Section: Control Parameter Adaptationmentioning

confidence: 99%

Solving Many-Objective Car Sequencing Problems on Two-Sided Assembly Lines Using an Adaptive Differential Evolutionary Algorithm

Chutima¹,

Kirdphoksap²

2019

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“…Due to the complex work process of the muffler, how to theoretically compute and design the inner structure of the muffler has been a topic which was constantly discussed. This paper took the minimum mass and noises of the exhaust muffler as the optimization objective, which belonged to a multi-objective optimization problem [19][20][21][22]. However, noises as the multivariate function of structural parameters had not mathematical explicit expressions.…”

Section: Selection Of Optimization Algorithmmentioning

confidence: 99%

Numerical optimization of flow noises for mufflers based on the improved BP neural network

Xie¹,

Gao²,

Huang³

et al. 2016

J. vibroeng.

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Aimed at the large noise of tail pipe, the method of fluid dynamics was firstly applied to analyze the inner flow field of the exhaust muffler. According to the result, the large noise of tail pipe was mainly caused by air flow regeneration noise, and the vice muffler was not the major component for generating airflow noise. The largest pressure of the whole muffler system was at the outlet end of main mufflers. The largest flow velocity was in the connection pipe between main mufflers and vice mufflers. Secondly, boundary element model of transmission loss for the muffler was established to compare and analyze it with the experimental. The experimental and computational value of transmission loss for the muffler has a good consistency in both change trend and numerical value, and the computational model was reliable. Finally, GA-BP neural network algorithm was used to optimize the acoustic performance of the muffler. Airflow noises of the tail pipe were effectively reduced through optimizing the inner structure of the muffler.

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“…The second category is mean-centric crossover, which steers to the mean center of the parents' position, such as uni-modal normal distribution crossover (UNDX) [6], simplex crossover (SPX) [7] and blend crossover (BLX-D) [8]. Beside that, differential evolution (DE) [9] also has been used as a crossover operator to solve continuous optimization problems, which owns a strong global search ability [13,15] However, the quality of offspring is highly dependent on the characteristics of target problems, which indicates that different crossover operators may show various performances to diverse problems. Thus, one promising solution is to generate offspring using different crossover operators.…”

Section: Introductionmentioning

confidence: 99%

A Gene-Level Hybrid Crossover Operator for Multiobjective Evolutionary Algorithm

Zhu

Lin

Huang

2015

2015 Second International Conference on Soft Computing and Machine Intelligence (ISCMI)

Self Cite

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This study proposes a novel recombination operator, called hybrid crossover operator (HX), which is performed in gene level of chromosome to enhance the optimization performance of multi-objective evolutionary algorithms (MOEAs). The proposed HX operator combines the advantages of simulated binary crossover with local search ability and differential evolution with strong global search capability. When HX is embedded into two state-of-the-art MOEAs, i.e., NSGA-II and MOEA/D-DE, the experimental results validate the improvement of HX when compared to the original counterpart.

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A novel hybrid multi-objective immune algorithm with adaptive differential evolution

Cited by 108 publications

References 50 publications

Solving Many-Objective Car Sequencing Problems on Two-Sided Assembly Lines Using an Adaptive Differential Evolutionary Algorithm

Solving Many-Objective Car Sequencing Problems on Two-Sided Assembly Lines Using an Adaptive Differential Evolutionary Algorithm

Numerical optimization of flow noises for mufflers based on the improved BP neural network

A Gene-Level Hybrid Crossover Operator for Multiobjective Evolutionary Algorithm

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