A Perspective of Conventional and Bio-inspired Optimization Techniques in Maximum Likelihood Parameter Estimation

Journal: Journal of Autonomous Intelligence DOI: 10.32629/jai.v1i2.28

Yongzhong Lu1, Min Zhou2, Shiping Chen3, David Levy4, Jicheng You1

1. School of Software Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China
2. College of Public Administration, Huazhong University of Science and Technology, Wuhan, 430074, China
3. Data61, Commonwealth Scientific and Industrial Research Organization, Marsfield, 2122, Australia
4. School of Electrical and Information Engineering, University of Sydney, Sydney, 2006, Australia


Maximum likelihood estimation is a method of estimating the parameters of a statistical model in statistics. It has been widely used in a good many multi-disciplines such as econometrics, data modelling in nuclear and particle physics, and geographical satellite image classification, and so forth. Over the past decade, although many conventional numerical approximation approaches have been most successfully developed to solve the problems of maximum likelihood parameter estimation, bio-inspired optimization techniques have shown promising performance and gained an incredible recognition as an attractive solution to such problems. This review paper attempts to offer a comprehensive perspective of conventional and bio-inspired optimization techniques in maximum likelihood parameter estimation so as to highlight the challenges and key issues and encourage the researches for further progress.


maximum likelihood estimation; bio-inspired optimization; differential evolution; swarm intelligence-based algorithm; genetic algorithm; particle swarm optimization; ant colony optimization.


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Copyright © 2018 Yongzhong Lu, Min Zhou, Shiping Chen, David Levy, Jicheng You

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