Titlebook: Advances in Swarm Intelligence, Part II; Second International Ying Tan,Yuhui Shi,Guoyin Wang Conference proceedings 2011 Springer-Verlag Gm

ACORN

书目名称Advances in Swarm Intelligence, Part II影响因子(影响力)




书目名称Advances in Swarm Intelligence, Part II影响因子(影响力)学科排名




书目名称Advances in Swarm Intelligence, Part II网络公开度




书目名称Advances in Swarm Intelligence, Part II网络公开度学科排名




书目名称Advances in Swarm Intelligence, Part II被引频次




书目名称Advances in Swarm Intelligence, Part II被引频次学科排名




书目名称Advances in Swarm Intelligence, Part II年度引用




书目名称Advances in Swarm Intelligence, Part II年度引用学科排名




书目名称Advances in Swarm Intelligence, Part II读者反馈




书目名称Advances in Swarm Intelligence, Part II读者反馈学科排名

GULLY

Gesture

Anticoagulant

Superconductivity by Kinetic Energy Saving?,cy. The result of experiment indicates that the algorithm is faster and more efficient than present mining algorithms when extracting frequent neighboring class sets with constraint class set in large spatial database.

dictator

0302-9743 ntrol, other optimization algorithms and applications, data fusion and swarm intelligence, as well as fish school search - foundations and applications.   .978-3-642-21523-0978-3-642-21524-7Series ISSN 0302-9743 Series E-ISSN 1611-3349

foodstuff

Conference proceedings 2011election algorithms, pattern recognition methods, intelligent control, other optimization algorithms and applications, data fusion and swarm intelligence, as well as fish school search - foundations and applications.   .

monologue

Multi-Objective Optimization for Dynamic Single-Machine Schedulingor dynamic single-machine scheduling problem (DSMSP) with job release dates. In MOGEP a fitness assignment scheme, diversity maintaining strategy and elitist strategy are incorporated on the basis of original GEP. Results of simulation experiments show that the MOGEP can construct effective SRs whic

Dendritic-Cells

Research of Pareto-Based Multi-Objective Optimization for Multi-Vehicle Assignment Problem Based on ired to satisfy all constrains and optimize overall criteria. Combined with MOPSO algorithm, a Pareto-based multi-objective model is proposed, which includes not only the time-cost tradeoff, but also a “Constraint-First-Objective-Next” strategy which handles constraints as an additional objective. N

愚笨

Correlative Particle Swarm Optimization for Multi-objective Problemsomplex problems. In order to improve convergence and diversity of solutions, a correlative particle swarm optimization (CPSO) with disturbance operation is proposed, named MO-CPSO, for dealing with multi-objective problems. MO-CPSO adopts the correlative processing strategy to maintain population di

Ankylo-

A PSO-Based Hybrid Multi-Objective Algorithm for Multi-Objective Optimization Problemsr operator of the genetic algorithm as the particle updating strategy. Second, a propagating mechanism is adopted to propagate the non-dominated archive. Third, a local search heuristic based on scatter search is applied to improve the non-dominated solutions. Computational study shows that the HMOP