An improved epsilon constraint-handling method in MOEA/D for CMOPs with large infeasible regions

Fan, Zhun; Li, Wenji; Cai, Xinye; Huang, Han; Fang, Yi; You, Yancheng; Mo, Jiajie; Wei, Cai-Min; Goodman, Erik D.

doi:10.1007/s00500-019-03794-x

Cited by 220 publications

(75 citation statements)

References 36 publications

(37 reference statements)

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“…A specific decreasing scheme for the level is adopted, where exponential or polynomial (as in [24]) decrease can be used, depending on the feasibility ratio. Tested over a 14 functions benchmark earlier proposed by the same authors [25], the resulting PPS-MOEA/D outperforms some classical MOEAs quoted in this section but requires tuning many parameters.…”

Section: Related Workmentioning

confidence: 89%

“…In [23], the authors allow increasing the level when the ratio of feasible solutions is greater than a threshold value, to promote exploration. This strategy, embedded in MOEA/D, is compared with MOEA/D-CDP [13] and with the original -constraint mechanism (decreasing pattern), over a set of nine constrained MOPs introduced earlier by the same authors [25]. Furthermore, this strategy is also compared with classical MOEAs (either dominance or decomposition-based) in a later work [26], over the CTP [17] and CF series [18], using IGD as a performance indicator.…”

Section: Related Workmentioning

confidence: 99%

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A New Hybrid Evolutionary Algorithm for the Treatment of Equality Constrained MOPs

et al. 2019

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Multi-objective evolutionary algorithms are widely used by researchers and practitioners to solve multi-objective optimization problems (MOPs), since they require minimal assumptions and are capable of computing a finite size approximation of the entire solution set in one run of the algorithm. So far, however, the adequate treatment of equality constraints has played a minor role. Equality constraints are particular since they typically reduce the dimension of the search space, which causes problems for stochastic search algorithms such as evolutionary strategies. In this paper, we show that multi-objective evolutionary algorithms hybridized with continuation-like techniques lead to fast and reliable numerical solvers. For this, we first propose three new problems with different characteristics that are indeed hard to solve by evolutionary algorithms. Next, we develop a variant of NSGA-II with a continuation method. We present numerical results on several equality-constrained MOPs to show that the resulting method is highly competitive to state-of-the-art evolutionary algorithms.

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Section: Related Workmentioning

confidence: 89%

Section: Related Workmentioning

confidence: 99%

A New Hybrid Evolutionary Algorithm for the Treatment of Equality Constrained MOPs

et al. 2019

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“…To evaluate the performance of the proposed MOEA/D-ACDP, 14 constrained multi-objective test problems with large infeasible regions in the objective space are used [27,28].…”

Section: Test Instances Lir-cmopsmentioning

confidence: 99%

MOEA/D with angle-based constrained dominance principle for constrained multi-objective optimization problems

Fan

Fang

et al. 2019

Applied Soft Computing

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114

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This paper proposes a novel constraint-handling mechanism named angle-based constrained dominance principle (ACDP) embedded in a decomposition-based multi-objective evolutionary algorithm (MOEA/D) to solve constrained multiobjective optimization problems (CMOPs). To maintain the diversity of the working population, ACDP utilizes the information of the angle of solutions to adjust the dominance relation of solutions during the evolutionary process. This paper uses 14 benchmark instances to evaluate the performance of the MOEA/D with ACDP (MOEA/D-ACDP). Additionally, an engineering optimization problem (which is I-beam optimization problem) is optimized. The proposed MOEA/D-ACDP, and four other decomposition-based CMOEAs, including C-MOEA/D, MOEA/D-CDP, MOEA/D-Epsilon and MOEA/D-SR are tested by the above benchmarks and the engineering application. The experimental results manifest that MOEA/D-ACDP is significantly better than the other four CMOEAs on these test instances and the real-world case, which indicates that ACDP is more effective for solving CMOPs.

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“…-Multi-objective Evolutionary Algorithm ( -MOEA) -MOEA presented in 2003 by Dieb et al [7] is a steady-state algorithm, that is it updates the population one solution at a time and coevolves a population of individuals with an external archive, where are stored the best non dominated solutions. The external archive applies -dominance to prevent the deterioration of the population and to ensure both convergence and diversity of the solutions.…”

Section: Pareto Archived Evolutionary (Paes) Published In 2000 By Knomentioning

confidence: 99%

MaaSim: A Liveability Simulation for Improving the Quality of Life in Cities

Woszczyk

Spanakis

2019

ECML PKDD 2018 Workshops

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Urbanism is no longer planned on paper thanks to powerful models and 3D simulation platforms. However, current work is not open to the public and lacks an optimisation agent that could help in decision making. This paper describes the creation of an open-source simulation based on an existing Dutch liveability score with a built-in AI module. Features are selected using feature engineering and Random Forests. Then, a modified scoring function is built based on the former liveability classes. The score is predicted using Random Forest for regression and achieved a recall of 0.83 with 10-fold cross-validation. Afterwards, Exploratory Factor Analysis is applied to select the actions present in the model. The resulting indicators are divided into 5 groups, and 12 actions are generated. The performance of four optimisation algorithms is compared, namely NSGA-II, PAES, SPEA2 and -MOEA, on three established criteria of quality: cardinality, the spread of the solutions, spacing, and the resulting score and number of turns. Although all four algorithms show different strengths, − M OEA is selected to be the most suitable for this problem. Ultimately, the simulation incorporates the model and the selected AI module in a GUI written in the Kivy framework for Python. Tests performed on users show positive responses and encourage further initiatives towards joining technology and public applications.

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An improved epsilon constraint-handling method in MOEA/D for CMOPs with large infeasible regions

Cited by 220 publications

References 36 publications

A New Hybrid Evolutionary Algorithm for the Treatment of Equality Constrained MOPs

A New Hybrid Evolutionary Algorithm for the Treatment of Equality Constrained MOPs

MOEA/D with angle-based constrained dominance principle for constrained multi-objective optimization problems

MaaSim: A Liveability Simulation for Improving the Quality of Life in Cities

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