Parallel Modified Artificial Bee Colony Algorithm for Solving Conditional Nonlinear Optimal Perturbation

Intelligent algorithms have been applied to solving conditional nonlinear optimal perturbation (CNOP), which plays an important role in the study of weather and climate predictability. Single particle intelligent optimization algorithms can get similar CNOP to adjoint method, and show higher time ef...

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Bibliographic Details
Published in:2016 IEEE 18th International Conference on High Performance Computing and Communications; IEEE 14th International Conference on Smart City; IEEE 2nd International Conference on Data Science and Systems (HPCC/SmartCity/DSS) pp. 333 - 340
Main Authors: Juhui Ren, Shijin Yuan, Bin Mu
Format: Conference Proceeding
Language:English
Published: IEEE 01.12.2016
Subjects:
Algorithm design and analysis
artificial bee colony algorithm
Atmospheric modeling
CNOP
Genetic algorithms
Mathematical model
MPI
Numerical models
Optimization
Prediction algorithms
Zebiak-Cane model
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Summary:Intelligent algorithms have been applied to solving conditional nonlinear optimal perturbation (CNOP), which plays an important role in the study of weather and climate predictability. Single particle intelligent optimization algorithms can get similar CNOP to adjoint method, and show higher time efficiency in solving CNOP. However, swarm intelligent optimization algorithms can only get similar CNOP, and still show lower time efficiency than adjoint method. In this paper, we proposed a modified artificial bee colony algorithm (MABC) to solve CNOP, and to accelerate the computation speed, we parallelize the MABC algorithm with MPI technology. In order to demonstrate its validity and efficiency, we apply MABC algorithm to solving CNOP in Zebiak-Cane model, which is a medium-complexity numerical model. The results obtained are compared with those from standard bee colony algorithm (ABC) algorithm, generic algorithm (GA) and adjoint method which is the benchmark. The MABC algorithm can get better results than the standard ABC and GA algorithm in solving CNOP, and can obtain similar results with adjoint method in CNOP magnitude and pattern aspects. The parallel MABC with MPI also shows a higher efficiency than adjoint method. All the experimental results show that it is feasible and efficient to solve CNOP with the proposed parallel modified artificial bee colony algorithm.
DOI:10.1109/HPCC-SmartCity-DSS.2016.0055