Multi-phase algorithm design for accurate and efficient model fitting

DOI10.1007/s10479-021-04028-wzbMath1482.65090OpenAlexW3139142454MaRDI QIDQ2115821

Jacob Aubertine, Lance Fiondella, Joshua Steakelum, Vidhyashree Nagaraju, Kenan Chen

Publication date: 21 March 2022

Published in: Annals of Operations Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10479-021-04028-w

zbMATH Keywords

numerical methodssoft computingsoftware reliabilitysoftware reliability growth modelmulti-phase algorithms

Mathematics Subject Classification ID

Numerical mathematical programming methods (65K05) Multi-objective and goal programming (90C29) Nonlinear programming (90C30) Approximation methods and heuristics in mathematical programming (90C59) Reliability, testing and fault tolerance of networks and computer systems (68M15)

Uses Software

• PLCP
• LBFGS-B
• SQPlab
• SciPy
• ABC

Cites Work

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• The normalized normal constraint method for generating the Pareto frontier
• A truncated Newton method with non-monotone line search for unconstrained optimization
• A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm
• A Successive Approach to Compute the Bounded Pareto Front of Practical Multiobjective Optimization Problems
• A New Metaheuristic Bat-Inspired Algorithm
• Firefly Algorithms for Multimodal Optimization
• Flower Pollination Algorithm for Global Optimization
• A Limited Memory Algorithm for Bound Constrained Optimization
• An efficient method for finding the minimum of a function of several variables without calculating derivatives
• A Simplex Method for Function Minimization
• Advances in Neural Networks – ISNN 2005
• Methods of conjugate gradients for solving linear systems
• Numerical optimization. Theoretical and practical aspects. Transl. from the French

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