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It is shown that the proposed methods outperform the reference algorithm significantly in the simulation scenarios investigated.
Our reference algorithm with a single goal is able to find solutions with competitive fitness, but these solutions are results of premature convergence, because they are poorly evolvable.
In addition, a reference algorithm based on the solution of the traveling salesman problem and the consecutive touring polygons problem is provided to find high quality solutions of the created set of problems.
The proposed SOM based algorithms provide a unified approach to solve various visibility based routing problems in polygonal maps while they provide a competitive quality of solutions to the reference algorithm with significantly lower computational requirements.
In the precedent part, we have presented 3 robust and fast new algorithms and compared them to the reference algorithm.
Firstly, we will define our GPU-based 2D-DWT algorithm, named as CUDA Conv 9/7, as the reference algorithm.
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In particular, the reference algorithms MOEA/D, SPEA2 and NSGA-II have been considered.
Furthermore, it has been compared to the reference algorithms NSGA-II, SPEA2 and MOEA/D.
The multivariate model-based algorithm proved to be more robust on end-point determination compared to the reference algorithms.
We investigate the efficiency of the algorithms for a set of network and DPP scenarios and we show that the proposed algorithms outperform other reference algorithms.
The performance of the enhanced particle swarm algorithm is compared to two reference algorithms, a standard particle swarm and a genetic algorithm.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com