Nature-inspired metaheuristics for multiobjective activity crashing
نویسندگان
چکیده
منابع مشابه
Nature-inspired metaheuristics for multiobjective activity crashing
Many project tasks and manufacturing processes consist of interdependent timerelated activities that can be represented as networks. Deciding which of these subprocesses should receive extra resources to speed up the whole network (i. e., where activity crashing should be applied) usually involves the pursuit of multiple objectives amid a lack of a priori preference information. A common decisi...
متن کاملEstimation of Optimal Crop Plan Using Nature Inspired Metaheuristics
Irrigation management has gained significance due to growing social needs and increasing command for food grains while the available resources have remained limited and scarce. Irrigation management includes optimal allocation of water for irrigation purposes, optimal cropping pattern for a given land area and water availabilities with an objective to maximize economic returns. In the present s...
متن کاملUsing Nature - Inspired Metaheuristics to Train Predictive Machines
Nature-inspired metaheuristics for optimization have proven successful, due to their fine balance between exploration and exploitation of a search space. This balance can be further refined by hybridization. In this paper, we conduct experiments with some of the most promising nature-inspired metaheuristics, for assessing their performance when using them to replace backpropagation as a learnin...
متن کاملHypervolume based metaheuristics for multiobjective optimization
The purpose of multiobjective optimization is to find solutions that are optimal regarding several goals. In the branch of vector or Pareto optimization all these goals are considered to be of equal importance, so that compromise solutions that cannot be improved regarding one goal without deteriorating in another are Paretooptimal. A variety of quality measures exist to evaluate approximations...
متن کاملTwo Metaheuristics for Multiobjective Stochastic Combinatorial Optimization
Two general-purpose metaheuristic algorithms for solving multiobjective stochastic combinatorial optimization problems are introduced: SP-ACO (based on the Ant Colony Optimization paradigm) which combines the previously developed algorithms S-ACO and P-ACO, and SPSA, which extends Pareto Simulated Annealing to the stochastic case. Both approaches are tested on random instances of a TSP with tim...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Omega
سال: 2008
ISSN: 0305-0483
DOI: 10.1016/j.omega.2006.05.001