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Showing 5 results for Simulated Annealing

Sh. Afandizadeh, S.a.h Zahabi, N. Kalantari,
Volume 8, Issue 1 (3-2010)
Abstract

Logit models are one of the most important discrete choice models and they play an important role in

describing decision makers’ choices among alternatives. In this paper the Multi-Nominal Logit models has been used

in mode choice modeling of Isfahan. Despite the availability of different mathematical computer programs there are

not so many programs available for estimating discrete choice models. Most of these programs use optimization

methods that may fail to optimize these models properly. Even when they do converge, there is no assurance that they

have found the global optimum, and it just might be a good approximation of the global minimum. In this research a

heuristic optimization algorithm, simulated annealing (S.A), has been tested for estimating the parameters of a Logit

model for a mode choice problem that had 17 parameters for the city of Isfahan and has been compared with the same

model calculated using GAUSS that uses common and conventional algorithms. Simulated annealing is and algorithm

capable of finding the global optimum and also it’s less likely to fail on difficult functions because it is a very robust

algorithm and by writing the computer program in MATLAB the estimation time has been decreased significantly. In

this paper, this problem has been briefly discussed and a new approach based on the simulated annealing algorithm

to solve that is discussed and also a new path for using this technique for estimating Nested Logit models is opened

for future research by the authors. For showing the advantages of this method over other methods explained above a

case study on the mode choice of Isfahan has been done.


S. Soudmand, M. Ghatee, S. M. Hashemi,
Volume 11, Issue 4 (12-2013)
Abstract

This paper proposes a new hybrid method namely SA-IP including simulated annealing and interior point algorithms to find the optimal toll prices based on level of service (LOS) in order to maximize the mobility in urban network. By considering six fuzzy LOS for flows, the tolls of congested links can be derived by a bi-level fuzzy programming problem. The objective function of the upper level problem is to minimize the difference between current LOS and desired LOS of links. In this level, to find optimal toll, a simulated annealing algorithm is used. The lower level problem is a fuzzy flow estimator model with fuzzy link costs. Applying a famous defuzzification function, a real-valued multi-commodity flow problem can be obtained. Then a polynomial time interior point algorithm is proposed to find the optimal solution regarding to the estimated flows. In pricing process, by imposing cost on some links with LOS F or E, users incline to use other links with better LOS and less cost. During the iteration of SA algorithm, the LOS of a lot of links gradually closes to their desired values and so the algorithm decreases the number of links with LOS worse than desirable LOS. Sioux Falls network is considered to illustrate the performance of SA-IP method on congestion pricing based on different LOS. In this pilot, after toll pricing, the number of links with LOS D, E and F are reduced and LOS of a great number of links becomes C. Also the value of objective function improves 65.97% after toll pricing process. It is shown optimal toll for considerable network is 5 dollar and by imposing higher toll, objective function will be worse.
A. Kaveh, A. Nasrolahi,
Volume 12, Issue 1 (3-2014)
Abstract

In this paper, a new enhanced version of the Particle Swarm Optimization (PSO) is presented. An important modification is made by adding probabilistic functions into PSO, and it is named Probabilistic Particle Swarm Optimization (PPSO). Since the variation of the velocity of particles in PSO constitutes its search engine, it should provide two phases of optimization process which are: exploration and exploitation. However, this aim is unachievable due to the lack of balanced particles’ velocity formula in the PSO. The main feature presented in the study is the introduction of a probabilistic scheme for updating the velocity of each particle. The Probabilistic Particle Swarm Optimization (PPSO) formulation thus developed allows us to find the best sequence of the exploration and exploitation phases entailed by the optimization search process. The validity of the present approach is demonstrated by solving three classical sizing optimization problems of spatial truss structures.
Mohammad Tamannaei, Mahmoud Saffarzadeh, Amin Jamili, Seyedehsan Seyedabrishami,
Volume 14, Issue 3 (4-2016)
Abstract

This paper presents a novel approach to solve the double-track railway rescheduling problem, when an incident occurs into one of the block sections of the railway. The approach restricts the effects of an incident to a specific time, based on which the trains are divided into rescheduled and unchanged ones, so that the latter retain their original time-table after the incident. The main contribution of this approach is the simultaneous consideration of three rescheduling policies: cancelling, delaying and re-ordering. A mixed-integer optimization model is developed to find optimal conflict-free time-table compatible with the proposed approach. The objective function minimizes two cost parts: the cost of deviation from the primary time-table and the cost of train cancellation. The model is solved by CPLEX 11 software which automatically generates the optimal solution of a problem. Also, a meta-heuristic solution method based on simulated annealing algorithm is proposed for tackling the large-scale problems. The results of an experimental analysis on two double-track railways of the Iranian network show an appropriate capability of the model and solution method for handling the simultaneous train rescheduling. The results indicate that the proposed solution method can provide good solutions in much shorter time, compared with the time taken to solve the mathematical model by CPLEX software.


Jiuping Xu, Qiurui Liu, Zhonghua Yang,
Volume 15, Issue 1 (1-2017)
Abstract

To fully explain hydropower unit operational problems, an optimal multi-objective dynamic scheduling model is presented which seeks to improve the efficiency of reservation regulation management. To reflect the actual hydropower engineering project environment, fuzzy random uncertainty and an integrated consideration of the natural resource constraints, such as load balance, system power balance, generation limits, turbine capacity, water head, discharge capacities, reservoir storage volumes, and water spillages, were included in the model. The aim of this research was to concurrently minimize discharges and maximize economic benefit. Subsequently, a new hybrid dynamic-programming based multi-start multi-objective simulated annealing algorithm was developed to solve the hydro unit operational problem. The proposed model and intelligent algorithm were then applied to the Xiaolongmen Hydraulic and Hydropower Station in China. The computational unit commitment schedule results demonstrated the practicality and efficiency of this optimization method.



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