A self-optimization approach for L-SHADE incorporated with eigenvector-based crossover and successful-parent-selecting framework on CEC 2015 benchmark set

Relationship Between Calibration Time and Final Performance of Conceptual Rainfall-Runoff Models

“…Control parameters of all three optimization algorithms are set as suggested in the source papers (in the case of SPS-L-SHADE-EIG, the so called Bdefaultv ariant of control parameter settings has been used, see Guo et al 2015). Population size has been set to 100 for MDE_pBX (Islam et al 2012;Piotrowski 2017), 50 for GLPSO (Gong et al 2016) and follows linear reduction scheme from 19D (where D is a problem dimensionality; D = 13 for the HBV and 7 for the GR4J) to 4 in SPS-L-SHADE-EIG (Guo et al 2015). Note that in SPS-L-SHADE-EIG algorithm the values of population size are related to the computational budget, hence the method will behave differently when large and small numbers of maximum function calls are preset.…”

“…Hence, calibration of each model for every catchment and with each considered number of function calls is performed by three optimization algorithms: Modified Differential Evolution with p-best crossover (MDE_pBX, Islam et al 2012), Successful parents selecting L-SHADE with eigenvector-based crossover (SPS-L-SHADE-EIG, Guo et al 2015) and Genetic Learning Particle Swarm Optimization (GLPSO, Gong et al 2016). Control parameters of all three optimization algorithms are set as suggested in the source papers (in the case of SPS-L-SHADE-EIG, the so called Bdefaultv ariant of control parameter settings has been used, see Guo et al 2015). Population size has been set to 100 for MDE_pBX (Islam et al 2012;Piotrowski 2017), 50 for GLPSO (Gong et al 2016) and follows linear reduction scheme from 19D (where D is a problem dimensionality; D = 13 for the HBV and 7 for the GR4J) to 4 in SPS-L-SHADE-EIG (Guo et al 2015).…”

Improved Biogeography-Based Optimization Based on Affinity Propagation

“…In this work, to investigate the performance of the MBBO, we compared it with SPS-L-SHADE-EIG algorithm [14], DEsPA algorithm [15] and MVMO algorithm [16] using the CEC 2015 benchmarks. For convenient description, these functions [19] are denoted as F1-F15, as shown in Table 1.…”

“…Further, the MBBO algorithm uses simulated annealing (SA) [13] as a local search strategy to promote exploitation and strengthen the ability to get out of the local optimum. This work also tests the MBBO algorithm and the three first winners of the CEC 2015 competition [14][15][16] in the CEC 2015 benchmarks and compares their operation results. After the modification of BBO, the Wilcoxon signed-rank test is used to demonstrate the differences between different implementations of the MBBO and the other three algorithms of the CEC 2015 competition, based on which it can be found that MBBO proposed in this work performs significantly better than do the three algorithms.…”

Reflected Adaptive Differential Evolution with Two External Archives for Large-Scale Global Optimization

“…A group of researchers have introduced new variants like opposition based DE [5], centroid dependent initialization ciJADE [6], cluster-based population initialization (CBPI) [7] jDE [8], genDE [9], Individual dependent Mechanism (IDE) [10] etc. Control parameters adaptation and self-adaptation have devised in [11], [12], jDErpo [13] SaDE [14], JADE [15], [16], EPSDE [17], IDE [18], SHADE [19]L-SHADE [20] [21], EWMA-DECrF [22]. Cooperative coevolution have been brought into DE for large scale optimization [23].…”