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From another viewpoint, there are also two heuristic groups for solving combinatorial optimization problems: construction algorithms that produce a feasible solution themselves, and improvement algorithms that can improve the solutions with having a feasible solution.
In a second step we improve the solutions using local search based on arc exchanges.
A fitness function is used on each generation of algorithms to gradually improve the solutions in analogy to the process of natural selection.
To solve the problem, we design a post-optimization method allowing to improve the solutions provided by a heuristic.
Three local search techniques: shifting, exchange, and k-variable move algorithms are added to the DE algorithm in order to improve the solutions.
The algorithm is designed in such a way that the ELM solves the prediction problem, whereas the CRO evolves the weights of the neural network, in order to improve the solutions obtained.
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An iterative improvement procedure is introduced to improve the solution.
Test and improve the solution.
Furthermore, we applied tabu search to improve the solution quality.
After the construction of an initial solution, local search is used to improve the solution.
Rollout algorithms are applied to the heuristics to improve the solution quality.
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