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- W77683752 abstract "Practical antenna design is a multi-objective task. In many situations, it is possible to identify the most important objective (e.g., reflection requirements) and handle antenna design using conventional, single-objective algorithms. This approach was used in the previous chapters, where secondary objectives (e.g., gain performance requirements) were handled through design constraints, either as explicit constraints or using penalty functions. In general, however, having more than one objective substantially complicates the design process because if the designer priorities are not clearly defined beforehand, multi-objective optimization becomes a necessity (Koulouridis et al. 2007; Kuwahara 2005). The goal of formal multi-objective optimization is to find the so-called Pareto front representing the best possible trade-offs between conflicting objectives. Probably the most popular approaches to solve multi-objective antenna design problems are metaheuristic (or population-based) algorithms such as genetic algorithms (GAs) and particle swarm optimizers (PSO) ( e.g., Koulouridis et al. 2007; Jin and Rahmat-Samii 2007). The advantage of metaheuristics is their ability to find the entire Pareto front in one algorithm run. The drawback is a large number (hundreds, thousands, or even tens of thousands) of objective function evaluations required, which often turns in a prohibitive computational time if the objective functions are supplied by full-wave discrete simulators." @default.
- W77683752 created "2016-06-24" @default.
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- W77683752 date "2014-01-01" @default.
- W77683752 modified "2023-10-17" @default.
- W77683752 title "Simulation-Based Multi-objective Antenna Optimization with Surrogate Models" @default.
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- W77683752 doi "https://doi.org/10.1007/978-3-319-04367-8_11" @default.
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