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, alpha, and experimental results have shown that its performance does not strongly depend on the initial value of alpha. 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. (C) 2018 Elsevier Ltd. All rights reserved.