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This paper proposes a new Teaching Learning Algorithm (TLA) that uses the chaotic map to prevent the conventional TLA from getting stuck on local optima and enhancing the convergence characteristics, due to non-repetitions nature and ergodicity of chaotic functions.
On the basis of self-defined minimum and maximum points of the POS in this paper, the second step applies the gradual search to produce some well-distributed solutions in the decision space so as to compensate for the inaccuracy of the first step, simultaneously and further enhancing the convergence and diversity of the population.
Therefore, this research uses a new mutation rule with a view of balancing the global exploration ability and the local exploitation tendency and enhancing the convergence rate of the algorithm.
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Additional auxiliary techniques are incorporated to enhance the convergence and stability of the NOT.
This paper proposes an original correction factor to compute the sensitivities and enhance the convergence of the algorithm.
This paper introduces an improvement to the original differential evolution (DE) to enhance the convergence rate during the optimization cycle.
Tabu search (TS) based local search is embedded in the proposed algorithm to enhance the convergence capability.
A response surface methodology based on desirability function is used to enhance the convergence speed of the proposed algorithm.
This paper introduces a modification to original DE that enhances the convergence rate without compromising on solution quality.
A homotopy-based backtracking strategy is applied to enhance the convergence in solving the formulated VTVL trajectory optimization problem.
It is shown that the weak implementation is superior and enhances the convergence to steady-state for coarse meshes.
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CEO of Professional Science Editing for Scientists @ prosciediting.com