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

TL;DR

This paper introduces a multi-objective memetic algorithm with adaptive weights that significantly improves the speed and quality of inverse antenna design optimization, outperforming existing methods.

Contribution

It presents a novel multi-objective memetic algorithm combining gradient-based local search with heuristic global search and adaptive weighting, enhancing inverse antenna design optimization.

Findings

Faster convergence compared to traditional methods

Higher quality Pareto fronts achieved

Effective on complex physical and topological metrics

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.