Transactions of the Korean Society for Noise and Vibration Engineering (한국소음진동공학회논문집)

The Korean Society for Noise and Vibration Engineering (한국소음진동공학회)
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Development of an Enhanced Artificial Life Optimization Algorithm and Optimum Design of Short Journal Bearings

향상된 인공생명 최적화 알고리듬의 개발과 소폭 저널 베어링의 최적설계

  • Published : 2002.06.01
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Abstract

This paper presents a hybrid method to compute the solutions of an optimization Problem. The present hybrid algorithm is the synthesis of an artificial life algorithm and the random tabu search method. The artificial life algorithm has the most important feature called emergence. The emergence is the result of dynamic interaction among the individuals consisting of the system and is not found in an individual. The conventional artificial life algorithm for optimization is a stochastic searching algorithm using the feature of artificial life. Emergent colonies appear at the optimum locations in an artificial ecology. And the locations are the optimum solutions. We combined the feature of random-tabu search method with the conventional algorithm. The feature of random-tabu search method is to divide any given region into sub-regions. The enhanced artificial life algorithm (EALA) not only converge faster than the conventional artificial life algorithm, but also gives a more accurate solution. In addition, this algorithm can find all global optimum solutions. The enhanced artificial life algorithm is applied to the optimum design of high-speed, short journal bearings and its usefulness is verified through an optimization problem.

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References

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  1. Pareto Artificial Life Algorithm for Multi-Objective Optimization vol.4, pp.2, 2011, https://doi.org/10.4018/jitr.2011040104