Multi-objective Memetic Algorithm with Adaptive Weights for Inverse Antenna Design

This paper deals with discrete topology optimization and describes the modification of a single-objective algorithm into its multi-objective counterpart. The result is a significant increase in the optimization speed and quality of the resulting Pareto front as compared to conventional state-of-the-...

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Published in:IEEE transactions on antennas and propagation p. 1
Main Authors: Kadlec, Petr, Capek, Miloslav
Format: Journal Article
Language:English
Published: IEEE 2025
Subjects:
Antennas
Diversity reception
Frequency modulation
Heuristic algorithms
inverse design
Linear programming
Memetics
numerical methods
Optimization
optimization methods
Shape
shape sensitivity analysis
structural topology design
Topology
Vectors
ISSN:0018-926X, 1558-2221
Online Access:Get full text
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Abstract This paper deals with discrete topology optimization and describes the modification of a single-objective algorithm into its multi-objective counterpart. The result is a significant increase in the optimization speed and quality of the resulting Pareto front as compared to conventional state-of-the-art automated inverse design techniques. This advancement is possible thanks to a memetic algorithm combining a gradient-based search for local minima with heuristic optimization to maintain sufficient diversity. The local algorithm is based on rank-1 perturbations; the global algorithm is NSGA-II. An important advancement is the adaptive weighting of objective functions during optimization. The procedure is tested on four challenging examples dealing with both physical and topological metrics and multi-objective settings. The results are compared with standard techniques, and the superb performance of the proposed technique is reported. The implemented algorithm applies to antenna inverse design problems and is an efficient data miner for machine learning tools.
AbstractList This paper deals with discrete topology optimization and describes the modification of a single-objective algorithm into its multi-objective counterpart. The result is a significant increase in the optimization speed and quality of the resulting Pareto front as compared to conventional state-of-the-art automated inverse design techniques. This advancement is possible thanks to a memetic algorithm combining a gradient-based search for local minima with heuristic optimization to maintain sufficient diversity. The local algorithm is based on rank-1 perturbations; the global algorithm is NSGA-II. An important advancement is the adaptive weighting of objective functions during optimization. The procedure is tested on four challenging examples dealing with both physical and topological metrics and multi-objective settings. The results are compared with standard techniques, and the superb performance of the proposed technique is reported. The implemented algorithm applies to antenna inverse design problems and is an efficient data miner for machine learning tools.
Author Capek, Miloslav
Kadlec, Petr
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  surname: Capek
  fullname: Capek, Miloslav
  email: miloslav.capek@fel.cvut.cz
  organization: Czech Technical University in Prague, Prague, Czech Republic
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Snippet This paper deals with discrete topology optimization and describes the modification of a single-objective algorithm into its multi-objective counterpart. The...
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SubjectTerms Antennas
Diversity reception
Frequency modulation
Heuristic algorithms
inverse design
Linear programming
Memetics
numerical methods
Optimization
optimization methods
Shape
shape sensitivity analysis
structural topology design
Topology
Vectors
Title Multi-objective Memetic Algorithm with Adaptive Weights for Inverse Antenna Design
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