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After the movement for each candidate solution in the current population is made, a new population is generated.
Moreover, it instantiates the MAPE-K architecture and uses a reasoner to search the set of possible configurations for the optimal solution in the current context.
The same approach can be applied not only to the initial solution but also continuously to each solution in the current population.
In order to refrain from the shortcoming of premature convergence, a two point reversal crossover operator is defined and in the repulsive process each particle is repelled away from some inferior solution in the current tabu list to fly towards some promising areas which can introduce some new information to guide the swarm searching process.
This is in fact the only possible solution in the current negotiations.
However, a pattern observed in the original experimental data set used to calibrate the model (Koch et al. 2005) suggested a refinement to the best-fit solution in the current model application.
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LS starts with a random solution x and tries to find better solutions in the current neighborhood.
In this paper, we use a simple normalization mechanism based on non-dominated solutions in the current population.
For comparison, we also show the results of the normalization based on non-dominated solutions in the current population.
One may think that the use of all solutions in the current population for the normalization may remedy its severe negative effects.
To be specific, in GRO, a part of good solutions in the current population are selected to produce candidate solutions by the gene combination.
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