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Multiobjective Optimization

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TLDR
In this paper, a wireless communication system consists of various passive components, including antennas, directional couplers, phase shifters, and filters, and the mathematical approach may use some assumptions in the calculation that introduces differences between the calculated dimensions and the actual dimensions.
Abstract
A wireless communication system consists of various passive components, including antennas, directional couplers, phase shifters, and filters. These components have many dimensional parameters that must be determined. For instance, a coupled line section has line width, gap between the coupled lines, and the length of the line as the parameters to be determined. Identifying these parameters through trial and error is ineffective. Meanwhile, the mathematical approach may be very complex and may use some assumptions in the calculation that introduces differences between the calculated dimensions and the actual dimensions.

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References
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Journal ArticleDOI

Optimization by Simulated Annealing

TL;DR: There is a deep and useful connection between statistical mechanics and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters), and a detailed analogy with annealing in solids provides a framework for optimization of very large and complex systems.
Journal ArticleDOI

A fast and elitist multiobjective genetic algorithm: NSGA-II

TL;DR: This paper suggests a non-dominated sorting-based MOEA, called NSGA-II (Non-dominated Sorting Genetic Algorithm II), which alleviates all of the above three difficulties, and modify the definition of dominance in order to solve constrained multi-objective problems efficiently.
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Evolutionary algorithms for solving multi-objective problems

TL;DR: This paper presents a meta-anatomy of the multi-Criteria Decision Making process, which aims to provide a scaffolding for the future development of multi-criteria decision-making systems.
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Evolutionary Algorithms for Multiobjective Optimization: Methods and Applications

TL;DR: The basic principles of evolutionary multiobjective optimization are discussed from an algorithm design perspective and the focus is on the major issues such as fitness assignment, diversity preservation, and elitism in general rather than on particular algorithms.
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