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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.
LS starts with a random solution x and tries to find better solutions in the current neighborhood.
The mean and standard deviation values calculated in step 6 corresponds to the variables of the top M ℓ solutions in the current sample.
These observations suggest that the normalization based on non-dominated solutions in the current population may severely modify the scale of each objective.
One may think that the use of all solutions in the current population for the normalization may remedy its severe negative effects.
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After the movement for each candidate solution in the current population is made, a new population is generated.
The same approach can be applied not only to the initial solution but also continuously to each solution in the current population.
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.
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.
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