The Wind Driven Optimization Technique and its Application in Electromagnetics

A new type of nature-inspired global optimization methodology based on atmospheric motion is introduced. The proposed Wind Driven Optimization (WDO) technique is a population based iterative heuristic global optimization algorithm for multi-dimensional and multi-modal problems with the potential to...

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Bibliographic Details
Published in:IEEE transactions on antennas and propagation Vol. 61; no. 5; pp. 2745 - 2757
Main Authors: Bayraktar, Z., Komurcu, M., Bossard, J. A., Werner, D. H.
Format: Journal Article
Language:English
Published: New York, NY IEEE 01.05.2013
Institute of Electrical and Electronics Engineers
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ISSN:0018-926X, 1558-2221
Online Access:Get full text
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Summary:A new type of nature-inspired global optimization methodology based on atmospheric motion is introduced. The proposed Wind Driven Optimization (WDO) technique is a population based iterative heuristic global optimization algorithm for multi-dimensional and multi-modal problems with the potential to implement constraints on the search domain. At its core, a population of infinitesimally small air parcels navigates over an N -dimensional search space following Newton's second law of motion, which is also used to describe the motion of air parcels within the earth's atmosphere. Compared to similar particle based algorithms, WDO employs additional terms in the velocity update equation (e.g., gravitation and Coriolis forces), providing robustness and extra degrees of freedom to fine tune. Along with the theory and terminology of WDO, a numerical study for tuning the WDO parameters is presented. WDO is further applied to three electromagnetics optimization problems, including the synthesis of a linear antenna array, a double-sided artificial magnetic conductor for WiFi applications, and an E-shaped microstrip patch antenna. These examples suggest that WDO can, in some cases, out-perform other well-known techniques such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA) or Differential Evolution (DE) and that WDO is well-suited for problems with both discrete and continuous-valued parameters.
ISSN:0018-926X
1558-2221
DOI:10.1109/TAP.2013.2238654