Constrained Optimization Based on Ensemble Differential Evolution and Two-Level-Based Epsilon Method

Xu, Bin; Zhang, Zonghao

doi:10.1109/access.2020.3040647

Cited by 5 publications

(6 citation statements)

References 57 publications

(91 reference statements)

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“…For computational-efficiency, in this work, the evaluation of ( 11 ) involves knowledge-based predictors rendered using response features as described in the remaining part of this section. Response feature technology serves to expedite and enhance reliability of local optimization of antenna structures 57 . It exploits a close-to-linear dependence between the frequency and level coordinates of adequately chosen characteristic attributes of the circuit responses and the system designable parameters.…”

Section: Uncertainty Quantification Proceduresmentioning

confidence: 99%

Rapid multi-criterial design of microwave components with robustness analysis by means of knowledge-based surrogates

Kozieł

Pietrenko‐Dabrowska

2023

Sci Rep

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Manufacturing tolerances and uncertainties concerning material parameters, e.g., operating conditions or substrate permittivity are detrimental to characteristics of microwave components. The knowledge of relations between acceptable parameter deviations (not leading to violation of design specifications) and the nominal performance (not considering uncertainties), and is therefore indispensable. This paper proposes a multi-objective optimization technique of microwave components with tolerance analysis. The goal is to identify a set of trade-off designs: nominal performance versus robustness (quantified by the maximum input tolerance values that allow for achieving 100-percent fabrication yield). Our approach exploits knowledge-driven regression predictors rendered using characteristic points (features) of the component’s response for a rapid evaluation of statistical performance figures, along with trust-region algorithm to enable low execution cost as well as convergence. The proposed methodology is verified with the use of three microstrip circuits, a broadband filter, and two branch-line couplers (a single- and a dual-band one). It is demonstrated that a Pareto set w.r.t. nominal performance and robustness objectives can be produced using only 40 to 60 EM simulations of the respective structure (per design). Reliability of the proposed algorithm is corroborated with the use of EM-based Monte Carlo simulation.

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Section: Uncertainty Quantification Proceduresmentioning

confidence: 99%

Rapid multi-criterial design of microwave components with robustness analysis by means of knowledge-based surrogates

Kozieł

Pietrenko‐Dabrowska

2023

Sci Rep

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“…Trivedi et al [84] proposed an ensemble of three mutation operator as in CoDE [41] where a static based penalty function is applied on the first half of the functional evaluations and superiority of feasible solutions is applied on the rest. Xu [85] proposed use of ensemble DE variants and a two-level -constrained method using generation and population level comparison Wen et al [86] proposed a voting mechanism for selection among four ECHTs (superiority of feasible solutions, -constrained method, stochastic ranking and self-adaptive penalty function)…”

Section: Constrained Handling Techniquementioning

confidence: 99%

Framework of Ensemble Parmeter Adapted Evolutionary Algorithm for Solving Constrained Optimization Problems

Saha¹,

De²

2022

Preprint

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Real-world optimization problems are often governed by one or more constraints. Over the last few decades, extensive research has been performed in Constrained Optimization Problems (COPs) fueled by advances in computational intelligence. In particular, Evolutionary Algorithms (EAs) are a preferred tool for practitioners for solving these COPs within practicable time limits. We propose an ensemble of multi- method hybrid EA framework with four mutation operators, two crossover operators, multi-search [Differential Evolution (DE) & Gaining Sharing Knowledge (GSK)] optimization algorithm, and ensemble of constraint handling techniques to solve global real- world constrained optimization problem. The proposed frame- work FEPEA has an ascendancy of multiple adaptation strategies concerning the control parameters, search mechanisms, two sub-populations as well as uses knowledge sharing mechanism between junior and senior phases. The algorithm also combines the power of four popular constraint handling techniques (CHT) and uses a voting mechanism to select any particular CHT. On top of that, this algorithm also uses both linear and non- linear population size reduction in every step of the evolutionary process. We test our method on 57 real-world problems provided as part of the CEC 2020 special session & competition on real- world constrained optimization benchmark suite. Experimental results indicate that FEPEA is able to achieve state-of-the- art performance on real-world constrained global optimization when compared against other well-known real-world constrained optimizers.

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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

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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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Constrained Optimization Based on Ensemble Differential Evolution and Two-Level-Based Epsilon Method

Cited by 5 publications

References 57 publications

Rapid multi-criterial design of microwave components with robustness analysis by means of knowledge-based surrogates

Rapid multi-criterial design of microwave components with robustness analysis by means of knowledge-based surrogates

Framework of Ensemble Parmeter Adapted Evolutionary Algorithm for Solving Constrained Optimization Problems

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

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