the main objective of this paper is to introduce a new intelligent optimization technique that uses a predictioncorrectionstrategy supported by a recurrent neural network for finding a near optimal solution of a givenobjective function. recently there have been attempts for using artificial neural networks (anns) in optimizationproblems and some types of anns such as hopfield network and boltzm...

Optimization of timetable is the need of the day for the rescheduling and routing of trains in real time. Trains are scheduled in parallel with the road transport vehicles to the same destination. As the number of trains is restricted due to single track, customers usually opt for road transport to use frequently. The air pollution increases as the density of vehicles on road transport is incre...

A new methodology has been developed to design optimal monitoring network to estimate the transient pollution plume resulting from active pollution sources in a contaminated aquifer. An optimization algorithm is linked with geostatistical kriging model as well as a numerical simulation model. Simulated annealing is used as the optimization tool. The physical process in the aquifer, i.e., the fl...

abstract the present study was carried out for developing and evaluating of the optimization model of cropping pattern in irrigation networks using analytical hierarchy process (ahp). the model was programmed into two sub-models. the objective of the first sub-model was ranking of crop appropriately, and the objective of the second sub-model was ranking of cropping area of each crop. ten criter...

background: one of the key issues in determining the quality of water in rivers is to create a water quality control network with a suitable performance. the measured qualitative variables at stations should be representative of all the changes in water quality in water systems. since the increase in water quality monitoring stations increases annual monitoring costs, recognition of the station...

nowadays, the environmental impact of transportation project and, especially air pollution impacts, are major concerns in evaluating transportation projects. based on this concern, beside definition of traditional objective functions like total travel time and total investment cost, different type of environmental related function is considered as objective function in urban network design. in ...

‎By p-power (or partial p-power) transformation‎, ‎the Lagrangian function in nonconvex optimization problem becomes locally convex‎. ‎In this paper‎, ‎we present a neural network based on an NCP function for solving the nonconvex optimization problem‎. An important feature of this neural network is the one-to-one correspondence between its equilibria and KKT points of the nonconvex optimizatio...

The branched transport problem, a popular recent variant of optimal transport, is non-convex and non-smooth variational problem on Radon measures. so-called urban planning the contrary, shape optimization that seeks geometry street or pipe network. We show with concave cost function equivalent to generalized version problem. Apart from unifying these two different models used in literature, ano...

in this paper, the application of neural networks for simulation and optimization of the cogeneration systems has been presented. cgam problem, a benchmark in cogeneration systems, is chosen as a casestudy. thermodynamic model includes precise modeling of the whole plant. for simulation of the steadysate behavior, the static neural network is applied. then using dynamic neural network, plant is...

Material flow problems are complex logistic optimization problems. We want to utilize the available logistic network in such a way that the load is minimized or the throughput is maximized. This lecture deals with these optimization problems from the viewpoint of network flow theory and reports on two industrial applications: (1) contolling material flow with automated guided vehicles in a cont...