Firefly Algorithm for Finding Optimal Shapes of Electromagnetic Devices

Many real-world optimization problems have to be treated as multi-objective optimization problems. An approach, well established in recent years, is to find Pareto optimal configurations of the trial variables by detecting nondominated solutions with the help of a suitable vector optimization method...

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Veröffentlicht in:IEEE transactions on magnetics Jg. 52; H. 3; S. 1 - 4
Hauptverfasser: Alb, Michael, Alotto, Piergiorgio, Magele, Christian, Renhart, Werner, Preis, Kurt, Trapp, Bernhard
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.03.2016
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Clustering algorithms
Electromagnetics
Evolution
Evolutionary computation
Fireflies
Heuristic methods
Linear programming
Magnetic noise
Magnetism
Mathematical analysis
Optimization
Pareto Optimization
Particle swarm optimization
Scalars
Sociology
Statistics
Strategy
Pareto optimization
particle swarm optimization
optimization
Evolutionary computation
ISSN:0018-9464, 1941-0069
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Zusammenfassung:Many real-world optimization problems have to be treated as multi-objective optimization problems. An approach, well established in recent years, is to find Pareto optimal configurations of the trial variables by detecting nondominated solutions with the help of a suitable vector optimization method. Alternatively, relying on scalar optimization methods (both stochastic or deterministic), a suitable objective function taking all objectives into account simultaneously has to be defined. Depending on the number of trial variables, a scalar objective function of that type will exhibit a considerable number of feasible local solutions besides the global one. Therefore, a useful scalar optimization strategy should be able to end up (with a high probability) in the best of all possible solutions in the given search space and additionally detect as many local solutions as possible. Some population-based stochastic methods are implicitly suited for that task; others can be enhanced to fulfill these requirements. Higher order evolution strategies have successfully been tuned for that kind of problem by introducing cluster sensitive recombination [niching higher order evolution strategy (NES)]. The firefly algorithm (FFA) mimics the behavior of fireflies, which use a kind of flashing light to communicate with other members of their species. Since the intensity of the light of a single firefly diminishes with increasing distance, the FFA is implicitly able to detect local solutions on its way to the best solution for a given scalar objective function. The FFA will be applied to the well-known Rastrigin test function and to a shielding/shunting electromagnetic problem with two and three objectives, respectively, and its results will be compared with the ones obtained with an NES.
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ISSN:0018-9464
1941-0069
DOI:10.1109/TMAG.2015.2483058