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2017•IEEE Antennas and Propagation MagazineRequires access

A Differential Evolution Performance Comparison: Comparing How Various Differential Evolution Algorithms Perform in Designing Microstrip Antennas and Arrays

Arindam Deb, Jibendu Sekhar Roy, Bhaskar Gupta

Open publisher page 26 citations

Abstract

In this article, we have undertaken a performance comparison of different variants and hybridized schemes of differential evolution (DE) algorithms with respect to several antenna and array designs. These include DE/rand/1, self-adaptive DE (SADE), DE with global and local neighborhood search (DEGL), biogeography-based optimization with DE (BBODE), modified DE (MDE), MDE with p-best crossover strategy (MDE-pBX), improved DE (IDE), harmonic search DE (HSDE), DE with an individual dependent mechanism (DE-IDP-IDM), and adaptive DE with optimization-state estimation (ADE).

About this research paper

What this paper is about

In this article, we have undertaken a performance comparison of different variants and hybridized schemes of differential evolution (DE) algorithms with respect to several antenna and array designs. These include DE/rand/1, self-adaptive DE (SADE), DE with global and local neighborhood search (DEGL), biogeography-based optimization with DE (BBODE), modified DE (MDE), MDE with p-best crossover strategy (MDE-pBX), improved DE (IDE), harmonic search DE (HSDE), DE with an individual dependent mechanism (DE-IDP-IDM), and adaptive DE with optimization-state estimation (ADE).

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OpenAlex reports 26 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

In this article, we have undertaken a performance comparison of different variants and hybridized schemes of differential evolution (DE) algorithms with respect to several antenna and array designs. These include DE/rand/1, self-adaptive DE (SADE), DE with global and local neighborhood search (DEGL), biogeography-based optimization with DE (BBODE), modified DE (MDE), MDE with p-best crossover strategy (MDE-pBX), improved DE (IDE), harmonic search DE (HSDE), DE with an individual dependent mechanism (DE-IDP-IDM), and adaptive DE with optimization-state estimation (ADE).

Key concepts: Differential evolution, Crossover, Computer science, Algorithm, Global optimization, Differential (mechanical device), Antenna (radio), Evolutionary algorithm

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