A modified probability collectives optimization algorithm based on trust region method and a new temperature annealing schedule

This article presents a distributed random search optimization method, the trust region probability collectives (TRPC) method, for unconstrained optimization problems without closed forms. Through analyzing the framework of the original probability collectives (PC) algorithm, three potential require...

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Veröffentlicht in:Soft computing (Berlin, Germany) Germany), 2016-04, Vol.20 (4), p.1581-1600
Hauptverfasser: Yang, Bo, Wu, Ruiming
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description This article presents a distributed random search optimization method, the trust region probability collectives (TRPC) method, for unconstrained optimization problems without closed forms. Through analyzing the framework of the original probability collectives (PC) algorithm, three potential requirements on solving the original PC model are first identified. Then an interior point trust region method for bound constrained minimization is adopted to satisfy these requirements. Besides, the temperature annealing schedule is also redesigned to improve the algorithmic performance. Since the new annealing schedule is linked to the gradient, it is much more flexible and efficient than the original one. Ten benchmark functions are used to test the modified algorithm. Numerical results show that TRPC is superior to the PC algorithm in iteration times, accuracy, and robustness.
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subjects Algorithms
Annealing
Artificial Intelligence
Computational Intelligence
Control
Engineering
Equilibrium
Game theory
Iterative methods
Mathematical Logic and Foundations
Mechatronics
Methodologies and Application
Methods
Optimization
Probability distribution
Robotics
Robustness (mathematics)
Schedules
Trustworthiness
Variables
title A modified probability collectives optimization algorithm based on trust region method and a new temperature annealing schedule
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