A Real Coded Vector Population-Based Incremental Learning Algorithm for Multi-Objective Optimizations of Electromagnetic Devices

Even though evolutionary algorithm (EA) has now become the standard and paradigm for solving multi-objective design problems, the complexity of its genetic operation is, however, limiting its popularity in engineering applications. Hitherto, there has been insufficient research that addresses the in...

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Vydáno v:IEEE transactions on magnetics Ročník 54; číslo 3; s. 1 - 4
Hlavní autoři: Ho, S. L., Yang, Jiaqiang, Yang, Shiyou, Bai, Yanan
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.03.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithm design and analysis
Algorithms
Antenna arrays
Biological cells
Design optimization
Evolutionary algorithms
Feature extraction
Fitness
Formulations
inverse problem
Inverse problems
Linear programming
Machine learning
Magnetism
multi-objective
Multiple objective analysis
population-based incremental learning (PBIL)
Probabilistic models
Sociology
Statistical analysis
Statistics
ISSN:0018-9464, 1941-0069
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Shrnutí:Even though evolutionary algorithm (EA) has now become the standard and paradigm for solving multi-objective design problems, the complexity of its genetic operation is, however, limiting its popularity in engineering applications. Hitherto, there has been insufficient research that addresses the inadequacy of EAs in extracting the characteristic landscape features of an objective function. However, increasing attentions have now been devoted to EAs based on probabilistic models (EAPMs) in computational intelligence studies. A real coded scalar population-based incremental learning algorithm, an EAPM, is proposed for multi-objective optimizations of electromagnetic devices. Major improvements include the design of a generating mechanism for new intermediate solutions, the selection of elite solutions to update the probability matrix, matrix updating formulations, and refinement mechanism for intervals to precisely generate intermediate solution. Also, a methodology to consider quantitatively both the number of improved objectives and the amount of improvements in a specified objective of multi-objective design problems is introduced in fitness assignments. Numerical results on a high frequency and a low inverse problem are reported to showcase the merits of the proposed algorithm.
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ISSN:0018-9464
1941-0069
DOI:10.1109/TMAG.2017.2766158