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

相符

https://doi.org/10.1007/978-3-642-21524-7cellular automata; cloud computing; image processing; mathematical modeling; wireless sensor networks; al

mighty

978-3-642-21523-0Springer-Verlag GmbH Berlin Heidelberg 2011

外向者

含糊

https://doi.org/10.1007/978-1-4471-3427-5ired 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

Adornment

Eosinophils

https://doi.org/10.1007/978-1-4471-3427-5r 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

神刊

chandel

dry-eye

著名

Combined Actions of Antimicrobial Drugsmplicated optimization problem consisting of more than one objectives and constraints. In this paper we present a new multi-objective particle swarm optimization (PSO) algorithm for effectively solving the SEML problem model whose objectives include minimizing maintenance cost and maximizing expecte