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Particle swarm optimization (PSO) is a population-based stochastic optimization. Its parameters are easy to control, and it operates easily. But, the particle swarm optimization is a local convergence algorithm. Quantum-behaved particle swarm optimization (QPSO) overcomes this shortcoming, and outperforms original PSO. Based on classical QPSO, cooperative quantumbehaved particle swarm optimization (CQPSO) is present. This CQPSO, a particle firstly obtaining several individuals using Monte Carlo method and these individuals cooperate between them. In the experiments, five benchmark functions and six composition functions are used to test the performance of CQPSO. The results show that CQPSO performs much better than the other improved QPSO in terms of the quality of solution and computational cost.

In previous studies, we reported quantitative trait loci (QTL) associated with the heading, flowering, and maturity time in four hard red spring wheat recombinant inbred line (RIL) populations but the results are scattered in population-specific genetic maps, which is challenging to exploit efficiently in breeding. Here, we mapped and characterized QTL associated with these three earliness traits using the International Wheat Genome Sequencing Consortium (IWGSC) RefSeq v2.0 physical map. Our data consisted of (i) 6526 single nucleotide polymorphisms (SNPs) and two traits evaluated at five conventionally managed environments in the ‘Cutler’ × ‘AC Barrie’ population; (ii) 3158 SNPs and two traits evaluated across three organic and seven conventional managements in the ‘Attila’ × ‘CDC Go’ population; (iii) 5731 SilicoDArT and SNP markers and the three traits evaluated at four conventional and organic management systems in the ‘Peace’ × ‘Carberry’ population; and (iv) 1058 SNPs and two traits evaluated across two conventionally and organically managed environments in the ‘Peace’ × ‘CDC Stanley’ population. Using composite interval mapping, the phenotypic data across all environments, and the IWGSC RefSeq v2.0 physical maps, we identified a total of 44 QTL associated with days to heading (11), flowering (10), and maturity (23). Fifteen of the 44 QTL were common to both conventional and organic management systems, and the remaining QTL were specific to either the conventional (21) or organic (8) management systems. Some QTL harbor known genes, including the Vrn-A1, Vrn-B1, Rht-A1, and Rht-B1 that regulate photoperiodism, flowering time, and plant height in wheat, which lays a solid basis for cloning and further characterization.

Identification of consistent QTL in both conventional and organic management systems is a prerequisite for improving spring wheat (Triticum aestivum) through marker‐assisted selection. The objectives of the present study were to continue investigating the effect of management systems on agronomic and end‐use quality traits, and map associated quantitative trait loci (QTL) in a hard red spring wheat population. We evaluated 165 recombinant inbred lines derived from ‘Peace’ × ‘CDC Stanley’ for nine agronomic and end‐use quality traits under conventional and organic management systems for 2 yr and genotyped them with the wheat 90K iSelect array. Using inclusive composite interval mapping on phenotypic data across two environments per management and the International Wheat Genome Sequence Consortium (IWGSC) RefSeq v2.0 physical map of 1,058 informative single nucleotide polymorphisms, we uncovered 32 QTL (17 under conventional and 15 under organic) associated with nine traits of which five QTL were common in both managements. The remaining QTL were specific either to the conventional (12) or organic (10) managements. Each QTL explained from 1.2 to 19.2% and together accounted for 3.0 to 28.4% and 3.3 to 21.9% of the total phenotypic variance of each trait evaluated under conventional and organic management systems, respectively. We found three coincident genomic regions on chromosomes 1B, 3B, and 4B associated with two and three traits. All QTL identified in the present study were not reported in any of our previous mapping populations and would provide more data to wheat researchers conducting similar studies under conventional and organic management systems.