A Novel Evolutionary Algorithm for Dynamic Constrained Multiobjective Optimization Problems

Chen, Qingda; Ding, Jinliang; Yang, Shengxiang; Chai, Tianyou

doi:10.1109/tevc.2019.2958075

“…This constraint handling technique can be used in other MOEAs based on Pareto dominance. In addition, the number of violated constraints [22], the dominance relation based on constraints [23], and the normalized constraint violation [24] have also been considered.…”

Section: A Existing Moeas With Constraint Handling Techniquesmentioning

confidence: 99%

A Coevolutionary Framework for Constrained Multiobjective Optimization Problems

Tian

¹

,

Zhang

²

,

Xiao

³

et al. 2021

IEEE Trans. Evol. Computat.

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Constrained multi-objective optimization problems (CMOPs) are challenging because of the difficulty in handling both multiple objectives and constraints. While some evolutionary algorithms have demonstrated high performance on most CMOPs, they exhibit bad convergence or diversity performance on CMOPs with small feasible regions. To remedy this issue, this paper proposes a coevolutionary framework for constrained multi-objective optimization, which solves a complex CMOP assisted by a simple helper problem. The proposed framework evolves one population to solve the original CMOP and evolves another population to solve a helper problem derived from the original one. While the two populations are evolved by the same optimizer separately, the assistance in solving the original CMOP is achieved by sharing useful information between the two populations. In the experiments, the proposed framework is compared to several state-of-the-art algorithms tailored for CMOPs. High competitiveness of the proposed framework is demonstrated by applying it to 47 benchmark CMOPs and the vehicle routing problem with time windows.

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“…In other words, the parameter space is an interval, which is much easier to handle by local search procedures, let alone nature-inspired algorithms (where constrained optimization is a non-trivial task [55][56][57] ).…”

Section: Geometry Parameterization: Absolute Vs Relativementioning

confidence: 99%

On geometry parameterization for simulation-driven design closure of antenna structures

Kozieł¹,

Pietrenko‐Dabrowska²

2021

Preprint

0

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Full-wave electromagnetic (EM) simulation tools have become ubiquitous in antenna design, especially final tuning of geometry parameters. From the reliability standpoint, the recommended realization of EM-driven design is through rigorous numerical optimization. It is a challenging endeavor with the major issues related to the high computational cost of the process, but also the necessity of handling several objectives and constraints over often highly-dimensional parameter spaces. From the numerical perspective, making decisions about the formulation of the optimization problem, the approach to handling the design constraints, but also parameterization of the antenna geometry, are all non-trivial. At the same time, these issues are interleaved, and may play an important role in the performance and reliability of the simulation-based design closure process. This paper demonstrates that the approach to arranging the structure parameterization (e.g., the use of absolute or relative parameters) may have a major effect of the optimization outcome. Our investigations are carried out using three broadband monopole antennas optimized under different scenarios and using different parameterizations. In particular, the results indicate that relative parameterization is preferred for optimization of input characteristics, whereas absolute parameterization is more suitable for size reduction.

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“…In other words, the parameter space is an interval, which is much easier to handle by local search procedures, let alone nature-inspired algorithms (where constrained optimization is a non-trivial task 56 – 58 ).…”

Section: Introductionmentioning

confidence: 99%

On geometry parameterization for simulation-driven design closure of antenna structures

Kozieł

¹

,

Pietrenko‐Dabrowska

²

2021

Sci Rep

View full text Add to dashboard Cite

Full-wave electromagnetic (EM) simulation tools have become ubiquitous in antenna design, especially final tuning of geometry parameters. From the reliability standpoint, the recommended realization of EM-driven design is through rigorous numerical optimization. It is a challenging endeavor with the major issues related to the high computational cost of the process, but also the necessity of handling several objectives and constraints over often highly-dimensional parameter spaces. From the numerical perspective, making decisions about the formulation of the optimization problem, the approach to handling the design constraints, but also parameterization of the antenna geometry, are all non-trivial. At the same time, these issues are interleaved, and may play an important role in the performance and reliability of the simulation-based design closure process. This paper demonstrates that the approach to arranging the structure parameterization (e.g., the use of absolute or relative parameters) may have a major effect of the optimization outcome. Our investigations are carried out using three broadband monopole antennas optimized under different scenarios and using different parameterizations. In particular, the results indicate that relative parameterization is preferred for optimization of input characteristics, whereas absolute parameterization is more suitable for size reduction.

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A Novel Evolutionary Algorithm for Dynamic Constrained Multiobjective Optimization Problems

Cited by 89 publications

References 64 publications

A Coevolutionary Framework for Constrained Multiobjective Optimization Problems

A Coevolutionary Framework for Constrained Multiobjective Optimization Problems

On geometry parameterization for simulation-driven design closure of antenna structures

On geometry parameterization for simulation-driven design closure of antenna structures

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