For optimization algorithms,the most important consideration is their global optimization performance.Our research is conducted with the hope that the algorithm can robustly find the optimal solution to the target pro...For optimization algorithms,the most important consideration is their global optimization performance.Our research is conducted with the hope that the algorithm can robustly find the optimal solution to the target problem at a lower computational cost or faster speed.For stochastic optimization algorithms based on population search methods,the search speed and solution quality are always contradictory.Suppose that the random range of the group search is larger;in that case,the probability of the algorithm converging to the global optimal solution is also greater,but the search speed will inevitably slow.The smaller the random range of the group search is,the faster the search speed will be,but the algorithm will easily fall into local optima.Therefore,our method is intended to utilize heuristic strategies to guide the search direction and extract as much effective information as possible from the search process to guide an optimized search.This method is not only conducive to global search,but also avoids excessive randomness,thereby improving search efficiency.To effectively avoid premature convergence problems,the diversity of the group must be monitored and regulated.In fact,in natural bird flocking systems,the distribution density and diversity of groups are often key factors affecting individual behavior.For example,flying birds can adjust their speed in time to avoid collisions based on the crowding level of the group,while foraging birds will judge the possibility of sharing food based on the density of the group and choose to speed up or escape.The aim of this work was to verify that the proposed optimization method is effective.We compared and analyzed the performances of five algorithms,namely,self-organized particle swarm optimization(PSO)-diversity controlled inertia weight(SOPSO-DCIW),self-organized PSO-diversity controlled acceleration coefficient(SOPSO-DCAC),standard PSO(SPSO),the PSO algorithm with a linear decreasing inertia weight(SPSO-LDIW),and the modified PSO algorithm with a time-varying acceleration constant(MPSO-TVAC).展开更多
[Object] This study was conducted to explore the genetic diversity and structure of the wild Repomucenus curvicornis inhabiting Liaoning Coast, China. [Method] The mitochondrial COⅠ gene and control region（CR） were...[Object] This study was conducted to explore the genetic diversity and structure of the wild Repomucenus curvicornis inhabiting Liaoning Coast, China. [Method] The mitochondrial COⅠ gene and control region（CR） were PCR amplified from the wild R. curvicornis populations from the Liaodong Bay（n=22） and the northern Yellow Sea（n=18）, sequenced and analyzed for genetic diversity. [Result] The contents of A, T, C and G of 624 bp COⅠ gene were 24.09%, 31.04%, 25.28%, and 19.59%, and those of 460 bp CR fragment were 32.96%, 32.80%, 14.86% and 19.38%, respectively. The total number of variable sites, average number of nucleotide differences（ k）, haplotype diversity（H） and nucleotide diversity（π） based on COⅠ gene were 38, 4.67,（0.96±0.02） and（0.007 5±0.004 2）, and those based on CR fragment were 26, 3.35,（0.97 ±0.02） and（0.007 3±0.004 3）, respectively. Based on mitochondrial COⅠ gene and CR, the genetic diversity of Liaodong Bay population was lower than that of the northern Yellow Sea population. The AMOVA analysis based on CR fragments revealed almost significant genetic divergence between the Liaodong Bay and the northern Yellow Sea populations, while there was no significant genetic divergence based on COⅠ gene. The results showed that CR and COⅠ gene are effective molecular markers for detecting the genetic diversity of R. curvicornis population, while CR is more reliable than COⅠ gene in detecting the genetic structure. [Conclusion] CR is an appropriate marker for genetic analysis of marine fish population.展开更多
文摘For optimization algorithms,the most important consideration is their global optimization performance.Our research is conducted with the hope that the algorithm can robustly find the optimal solution to the target problem at a lower computational cost or faster speed.For stochastic optimization algorithms based on population search methods,the search speed and solution quality are always contradictory.Suppose that the random range of the group search is larger;in that case,the probability of the algorithm converging to the global optimal solution is also greater,but the search speed will inevitably slow.The smaller the random range of the group search is,the faster the search speed will be,but the algorithm will easily fall into local optima.Therefore,our method is intended to utilize heuristic strategies to guide the search direction and extract as much effective information as possible from the search process to guide an optimized search.This method is not only conducive to global search,but also avoids excessive randomness,thereby improving search efficiency.To effectively avoid premature convergence problems,the diversity of the group must be monitored and regulated.In fact,in natural bird flocking systems,the distribution density and diversity of groups are often key factors affecting individual behavior.For example,flying birds can adjust their speed in time to avoid collisions based on the crowding level of the group,while foraging birds will judge the possibility of sharing food based on the density of the group and choose to speed up or escape.The aim of this work was to verify that the proposed optimization method is effective.We compared and analyzed the performances of five algorithms,namely,self-organized particle swarm optimization(PSO)-diversity controlled inertia weight(SOPSO-DCIW),self-organized PSO-diversity controlled acceleration coefficient(SOPSO-DCAC),standard PSO(SPSO),the PSO algorithm with a linear decreasing inertia weight(SPSO-LDIW),and the modified PSO algorithm with a time-varying acceleration constant(MPSO-TVAC).
基金Supported by the National Key R&D Program of China(2017YFC1404400)The National Natural Science Foundation of China(31770458)
文摘[Object] This study was conducted to explore the genetic diversity and structure of the wild Repomucenus curvicornis inhabiting Liaoning Coast, China. [Method] The mitochondrial COⅠ gene and control region（CR） were PCR amplified from the wild R. curvicornis populations from the Liaodong Bay（n=22） and the northern Yellow Sea（n=18）, sequenced and analyzed for genetic diversity. [Result] The contents of A, T, C and G of 624 bp COⅠ gene were 24.09%, 31.04%, 25.28%, and 19.59%, and those of 460 bp CR fragment were 32.96%, 32.80%, 14.86% and 19.38%, respectively. The total number of variable sites, average number of nucleotide differences（ k）, haplotype diversity（H） and nucleotide diversity（π） based on COⅠ gene were 38, 4.67,（0.96±0.02） and（0.007 5±0.004 2）, and those based on CR fragment were 26, 3.35,（0.97 ±0.02） and（0.007 3±0.004 3）, respectively. Based on mitochondrial COⅠ gene and CR, the genetic diversity of Liaodong Bay population was lower than that of the northern Yellow Sea population. The AMOVA analysis based on CR fragments revealed almost significant genetic divergence between the Liaodong Bay and the northern Yellow Sea populations, while there was no significant genetic divergence based on COⅠ gene. The results showed that CR and COⅠ gene are effective molecular markers for detecting the genetic diversity of R. curvicornis population, while CR is more reliable than COⅠ gene in detecting the genetic structure. [Conclusion] CR is an appropriate marker for genetic analysis of marine fish population.
