MOEA3D: a MOEA based on dominance and decomposition with probability distribution model

Hu, Ziyu; Yang, Jingming; Cui, Huihui; Wei, Lixin; Fan, Rui

doi:10.1007/s00500-017-2840-z

Cited by 20 publications

(2 citation statements)

References 36 publications

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“…For MOP, the best solution is to find the best trade-off among the different objectives. The multi-objective evolutionary algorithm (MOEA) has become more and more popular to resolve the MOP [20][21][22][23][24] since the vector evaluated genetic algorithms (VEGA) were created in 1985. 25,26 Most MOEAs follow the concept of dominance, that is, one solution will be considered to dominate the other one if it gets better result in at least one of all objectives.…”

Section: Spea2 Optimizermentioning

confidence: 99%

Model-based computational intelligence multi-objective optimization for gasoline direct injection engine calibration

Tayarani

et al. 2018

Proceedings of the Institution of Mechanical Engineers, Part D:

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For modern engines, the number of adjustable variables is increasing considerably. With an increase in the number of degrees of freedom and the consequent increase in the complexity of the calibration process, traditional design of experiments–based engine calibration methods are reaching their limits. As a result, an automated engine calibration approach is desired. In this paper, a model-based computational intelligence multi-objective optimization approach for gasoline direct injection engine calibration is developed, which can optimize the engine’s indicated specific fuel consumption, indicated specific particulate matter by mass, and indicated specific particulate matter by number simultaneously, by intelligently adjusting the engine actuators’ settings through Strength Pareto Evolutionary Algorithm 2. A mean-value model of gasoline direct injection engine is developed in the author’s earlier work and used to predict the performance of indicated specific fuel consumption, indicated specific particulate matter by mass, and indicated specific particulate matter by number with given value of intake valves opening timing, exhaust valves closing timing, spark timing, injection timing, and rail pressure. Then a co-simulation platform is established for the introduced intelligence engine calibration approach in the given engine operating condition. The co-simulation study and experimental validation results suggest that the developed intelligence calibration approach can find the optimal gasoline direct injection engine actuators’ settings with acceptable accuracy in much less time, compared to the traditional approach.

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Section: Spea2 Optimizermentioning

confidence: 99%

Model-based computational intelligence multi-objective optimization for gasoline direct injection engine calibration

Tayarani

et al. 2018

Proceedings of the Institution of Mechanical Engineers, Part D:

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“…With the increase in the dimensionality of decision variables and objectives, MOEAs face significant computational complexity in generating offspring through crossover and mutation (Gu, Gao et al 2023), as well as immense selection pressure in environmental selection (Hu, Yang et al 2019). Therefore, many researchers have proposed various methods to address these challenges.…”

Section: Introductionmentioning

confidence: 99%

A Many-Objective Optimization Evolutionary Algorithm Based on Double Surrogate-Assisted Adaptive Guiding Evolutionary Direction

Dali,

Yifei,

et al. 2023

Preprint

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In expensive Many-objective Optimization Problems, Surrogate-Assisted Evolutionary Algorithms (SAEAs) are often used to reduce the number of original Function Evaluations (FE). However, traditional SAEAs may suffer from long running times due to the complex computational demands of the surrogate models themselves. In this paper, we propose a novel algorithm called " A Many-Objective Optimization Evolutionary Algorithm Based on Double Surrogate-Assisted Adaptive Guiding Evolutionary Direction (DSAG)", aimed at further reducing the running time of SAEAs. The proposed algorithm employs two surrogate models: one for predicting the diversity of solutions and another for predicting the convergence value of solutions, and then sorts the solutions based on the prediction results. Afterwards, the algorithm adaptively adjusts the type and number of candidate solutions according to the "Convergence-Related Average Move Distance (CAMD)" proposed in this paper. This algorithm can adaptively bias towards either the convergence exploration stage or the diversity exploration stage. Compared with other classical surrogate-assisted evolutionary algorithms, the proposed algorithm first categorizes the decision variables and uses the categorized data to train two surrogate models, which makes the overall complexity and running time of the algorithm superior to others. Finally, we compared the performance of this algorithm on different problems using benchmark test functions to verify its ability to explore diversity and improve convergence. Then, we compared the running time of the algorithm with changes in the target dimension, decision variable dimension, and number of Original FE on the same test problems, and demonstrated the superior performance of the algorithm.

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Self-adaptive weight vector adjustment strategy for decomposition-based multi-objective differential evolution algorithm

Fan

Wei

et al. 2020

Soft Comput

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MOEA3D: a MOEA based on dominance and decomposition with probability distribution model

Cited by 20 publications

References 36 publications

Model-based computational intelligence multi-objective optimization for gasoline direct injection engine calibration

Model-based computational intelligence multi-objective optimization for gasoline direct injection engine calibration

A Many-Objective Optimization Evolutionary Algorithm Based on Double Surrogate-Assisted Adaptive Guiding Evolutionary Direction

Self-adaptive weight vector adjustment strategy for decomposition-based multi-objective differential evolution algorithm

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