This note describes a simplified parameter free implementation of Central Force optimization for use in deterministic multidimensional search and optimization The user supplies only the objective function to be maxi a...
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ISBN:
(纸本)9783642149214
This note describes a simplified parameter free implementation of Central Force optimization for use in deterministic multidimensional search and optimization The user supplies only the objective function to be maxi allied nothing mote The algorithm's performance is tested against a widely used suite of twenty three benchmark functions and compared to other state-of-the in algorithms CFO performs very well
An implementation of Central Force optimization (CFO) utilizing variable initial probes and decision space adaptation is presented. The algorithm is tested against a suite of benchmark functions and CFO's results ...
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An implementation of Central Force optimization (CFO) utilizing variable initial probes and decision space adaptation is presented. The algorithm is tested against a suite of benchmark functions and CFO's results compared to those of other algorithms. CFO performs well against the benchmarks, and also in scalability tests in 300-dimensions. (C) 2011 Elsevier Inc. All rights reserved.
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