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Additionally, it can provide fairly-competitive results compared with other state-of-the-art evolutionary algorithms in both benchmark functions and engineering problems.
The performance of the BFO-CC algorithm was comprehensively evaluated by comparing it with several other competitive algorithms (based on swarm intelligence) on both benchmark functions and real-world problems.
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The proposed method is tested both on twenty-four benchmark functions and four well-known engineering optimization problems.
The experimental results demonstrate that GPBO can provide better solution accuracy and convergence ability in both large scale benchmark functions and real-world optimization problem.
To verify the performance of our VCS, both the unconstrained classic and CEC2014 modern benchmark functions, and constrained engineering design optimization problems are employed.
The MS-GSA has been verified by comparing with 7 popular meta-heuristics algorithms on 23 typical basic benchmark functions and 7 compositeomposite benchmark functions.
Simulations are implemented on an array of benchmark functions and two engineering optimization problems.
Two well-known suites of benchmark functions and five engineering problems are used as case studies.
Results are obtained from standard optimization benchmark functions and the traveling salesman problem (TSP).
Experiments are conducted on a set of 48 benchmark functions and also two engineering optimization problems.
The efficiency of the MHDA is validated by testing on basic unconstrained benchmark functions and CEC 2014 test functions.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com