New Probabilistic-Dynamic Multi-Method Ensembles for Optimization based
on the CRO-SL
- URL: http://arxiv.org/abs/2212.00742v2
- Date: Fri, 2 Dec 2022 09:25:24 GMT
- Title: New Probabilistic-Dynamic Multi-Method Ensembles for Optimization based
on the CRO-SL
- Authors: Jorge P\'erez-Aracil and Carlos Camacho-G\'omez and Eugenio
Lorente-Ramos and Cosmin M. Marina and Sancho Salcedo-Sanz
- Abstract summary: We propose two new strategies to create ensembles based on the Coral Reefs Optimization with Substrate Layers (CRO-SL) algorithm.
The first strategy is the Probabilistic CRO-SL, which substitutes the substrates in the CRO-SL population by em tags associated with each individual.
The second strategy is the Dynamical Probabilistic CRO-SL, in which the probability of tag assignment is modified during the evolution of the algorithm.
- Score: 1.7307692398051588
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In this paper we propose new probabilistic and dynamic (adaptive) strategies
to create multi-method ensembles based on the Coral Reefs Optimization with
Substrate Layers (CRO-SL) algorithm. The CRO-SL is an evolutionary-based
ensemble approach, able to combine different search procedures within a single
population. In this work we discuss two different probabilistic strategies to
improve the algorithm. First, we defined the Probabilistic CRO-SL (PCRO-SL),
which substitutes the substrates in the CRO-SL population by {\em tags}
associated with each individual. Each tag represents a different operator which
will modify the individual in the reproduction phase. In each generation of the
algorithm, the tags are randomly assigned to the individuals with a similar
probability, obtaining this way an ensemble with a more intense change in the
application of different operators to a given individual than the original
CRO-SL. The second strategy discussed in this paper is the Dynamical
Probabilistic CRO-SL (DPCRO-SL), in which the probability of tag assignment is
modified during the evolution of the algorithm, depending on the quality of the
solutions generated in each substrate. Thus, the best substrates in the search
process will be assigned with a higher probability that those which showed a
worse performance during the search. We test the performance of the proposed
probabilistic and dynamic ensembles in different optimization problems,
including benchmark functions and a real application of wind turbines layout
optimization, comparing the results obtained with that of existing algorithms
in the literature.
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