A+ Evolutionary search algorithm and QR decomposition based rotation invariant crossover operator

•A new evolutionary search algorithm, i.e., A+, has been introduced.•A QR decomposition based orthogonal crossover operator has been presented.•Five different point clouds are filtered using the newly introduced evolutionary algorithm. The recently proposed artificial cooperative search (ACS) algori...

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Bibliographic Details
Published in:Expert systems with applications 2018-08, Vol.103, p.49-62
Main Authors: Civicioglu, Pinar, Besdok, Erkan
Format: Article
Language:English
Subjects:
Algorithms
Artificial bee colony algorithm
Artificial cooperative search algorithm
Backtracking search optimization algorithm
CMAES
Cuckoo search algorithm
Decomposition
Differential evolution algorithm
Evolutionary algorithms
Filtration
Gamma rays
Invariants
Iterative methods
Numerical analysis
Parameter estimation
Rotation
Search algorithms
Vector space
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Summary:•A new evolutionary search algorithm, i.e., A+, has been introduced.•A QR decomposition based orthogonal crossover operator has been presented.•Five different point clouds are filtered using the newly introduced evolutionary algorithm. The recently proposed artificial cooperative search (ACS) algorithm is a population-based iterative evolutionary algorithm (EA) for solving real-valued numerical optimization problems. It uses a rotation-invariant line recombination-based mutation strategy and rule-based crossover operator. However, it performs poorly for problems that include closely-related variables because, in these cases, generating uncorrelated feasible trial solution vectors using stochastic crossover methods is extremely difficult, and its mutation and crossover operators are also less effective. This paper adds a new QR-decomposition-based rotation-invariant search strategy to the ACS algorithm to improve its ability to solve such problems. This new, advanced ACS algorithm, called A+, has only one control parameter, α, and experimental results have shown that its performance does not strongly depend on the initial value of α. This paper also examines A+’s performance for noisy point cloud filtering, which is a complex real-world problem. The results of numerical experiments demonstrate that A+’s performance when solving numerical and real-world problems with closely-related variables is better than those of the comparison algorithms.
ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2018.03.009