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State of the art.
If something is state of the art, it is the most up-to-date model incorporating the latest and best technology.
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Since the resulting robust problem can prove very challenging even for a modern state-of-the art optimization solver, we propose to solve it by an original primal heuristic that combines a probabilistic variable fixing procedure, guided by peculiar Linear Programming relaxations, with a Mixed Integer Programming heuristic, based on an exact very large neighborhood search.
Results of the computational experiments on instances derived from TSPLIB show that some of the proposed algorithms perform remarkably well in cases where formulations solved by a state-of-the-art optimization code fail to yield optimal solutions within reasonable computation time.
Then, optimal solutions are derived by simulations using various state-of-the-art optimization techniques.
Two state-of-the-art optimization strategies are employed.
A comparative study is carried out with other state-of-the-art optimization techniques.
The results show the fast convergence of the decomposition method outperforming commercial state-of-the-art optimization software.
Then, comparative study between the I-VS algorithm and some other state-of-the-art optimization algorithms is performed.
The performance of the proposed ELPSO is evaluated by using the CEC2005 benchmark and compared with each individual algorithm and other state-of-the-art optimization algorithms.
Performance comparisons are made between state-of-the-art optimization techniques, like GA, PSO, DE, ABC and its variants, and the recently developed TLBO.
Performance comparisons are made with state-of-the-art optimization techniques like GA, PSO, DE, ABC and its variants and the recently developed TLBO.
The results are compared to those obtained using twelve state-of-the-art optimization algorithms and confirm its good and robust performance.
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