Reliability Optimization Problems using Adaptive Genetic Algorithm and Improved Particle Swarm Optimization

YoungSu Yun (Editor)
Chosun University, Dong-gu, Republic of Korea

Series: Mathematics Research Developments
BISAC: MAT000000

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Volume 10

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Special issue: Resilience in breaking the cycle of children’s environmental health disparities
Edited by I Leslie Rubin, Robert J Geller, Abby Mutic, Benjamin A Gitterman, Nathan Mutic, Wayne Garfinkel, Claire D Coles, Kurt Martinuzzi, and Joav Merrick

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This new book discusses why several reliability optimization problems are considered to be optimized. For the optimization, a hybrid approach using adaptive genetic algorithm (aGA) and improved particle swarm optimization (iPSO) is proposed. For the aGA, an adaptive scheme is incorporated into genetic algorithm (GA) loop and it adaptively regulates crossover and mutation rates during genetic search process. For the iPSO, a conventional PSO is improved and it is applied to the hybrid approach.

Therefore, the proposed hybrid approach takes an advantage of compensatory property of the aGA and the iPSO. For proving the performance of the proposed hybrid approach, conventional hybrid algorithms using GA and PSO are presented and their performances are compared with that of the proposed hybrid algorithm using several reliability optimization problems which have been often used in many conventional studies. (Imprint: Nova)

ABSTRACT

1. INTRODUCTION

2. RELIABILITY OPTIMIZATION PROBLEMS

3. HYBRID APPROACH USING IGA AND IPSO

4. NUMERICAL EXAMPLES

5. CONCLUSION

6. REFERENCES

INDEX

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