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To do this, it is only necessary to implement other fitness procedures.
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In the proposed multi-objective colliding bodies optimization (MOCBO) algorithm, the collision theory strategy as the search process is utilized and the Maximin fitness procedure is incorporated to the CBO for sorting the agents.
In the optimization procedure, a fitness function is defined based on the target and synthesis variable spaces that can be applied into various antenna array designs, combining part by part with the different requirements.
A new concept of fitness function, based on the ranking procedure, was adopted.
The selection procedure introduces the influence of the fitness function to the genetic algorithm optimization process.
The new presented Multi-View fitness function considers two kinds of evaluating procedures.
The execution time of the algorithms is mainly impacted by the fitness evaluation procedures, the use of matrix M can help to reduce this time and to scale our implementation to greater FMs.
The accuracy of any criteria-based clustering method (or those combined with the random walk procedures) is greatly affected by the chosen clustering fitness function.
With the help of AFFG, the number of the fitness function evaluations is reduced by over 50% and the procedure converges 5 times faster than the conventional evolutionary algorithm to reach the same final fitness values.
We propose a Pareto fitness genetic algorithm (PFGA) in which we introduce a modified ranking procedure and a promising way of sharing; a new fitness function based on the rank of the individual and its density value is designed.
Kumar et al. (2014) also introduced a genetic algorithm with new fitness assignment procedure and tested the algorithm on standard benchmark problems.
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