An-Najah Staff

Simulation today is one of the most used tools in science and engineering. Traffic engineering is no exception. Simulators to be usable passes through processes of verification, validation and calibration. All simulators are based on assumptions and parameters that need to be calibrated so as to be practical in real world applications. Some parameters change from site to site. Therefore, the calibration process is often needed. Calibration can be seen as an optimization process that seeks to minimize the difference between observed and simulated measures. The question of which optimization technique suits more for this particular problem remains open. In this paper the convergence velocity of main heuristic optimization techniques, namely Genetic Algorithm (GA), Tabu Search (TS), Particle Swarm Optimization (PS) and Simultaneous Perturbation for Stochastic Approximation algorithm (SPSA) were used to calibrate a traffic simulation model called SUMO. The results of the calibration of the mentioned optimization techniques were compared. Classical optimization techniques, namely Neldear-Mead and COBYLA were used as a baseline comparison. Each technique has its own parameters that affect convergence velocity. Therefore, optimization techniques themselves need to be calibrated. However, TS and PS are not widely used to calibrate traffic simulators. They perform well in this particular problem. PS is highly parallel compared to the TS and SPSA. The paper shows that classical optimization techniques are not suitable for this particular problem, PS and TS appear to be better than GA and SPSA. PS seems to be a promising optimization technique.

Simulation today is one of the most used tools in science and engineering. Traffic engineering is no exception. Simulators to be usable passes through processes of verification, validation and calibration. All simulators are based on assumptions and parameters that need to be calibrated so as to be practical in real world applications. Some parameters change from site to site. Therefore, the calibration process is often needed. Calibration can be seen as an optimization process that seeks to minimize the difference between observed and simulated measures. The question of which optimization technique suits more for this particular problem remains open. In this paper the convergence velocity of main heuristic optimization techniques, namely Genetic Algorithm (GA), Tabu Search (TS), Particle Swarm Optimization (PS) and Simultaneous Perturbation for Stochastic Approximation algorithm (SPSA) were used to calibrate a traffic simulation model called SUMO. The results of the calibration of the mentioned optimization techniques were compared. Classical optimization techniques, namely Neldear-Mead and COBYLA were used as a baseline comparison. Each technique has its own parameters that affect convergence velocity. Therefore, optimization techniques themselves need to be calibrated. However, TS and PS are not widely used to calibrate traffic simulators. They perform well in this particular problem. PS is highly parallel compared to the TS and SPSA. The paper shows that classical optimization techniques are not suitable for this particular problem, PS and TS appear to be better than GA and SPSA. PS seems to be a promising optimization technique.

A maximum partition of a directed weighted graph is partitioning the nodes into two sets such that it maximizes the total weights of edges between the two sets. In this study a genetic algorithm is proposed to solve the maximum partition problem. Experiments performed on randomly generated graphs of different sizes show that the proposed algorithm converges to an optimal solution faster than the existing heuristic algorithm.

This paper presents a Genetic Algorithm for Production Systems Optimization (GAPSO). The GAPSO finds an ordering of Condition Elements (CEs) in the rules of a Production System (PS) that results in a (near) optimal PS with respect to execution time. Finding such an ordering can be difficult since there is often a large number of ways to order CEs in the rules of a PS. Additionally, existing heuristics to order CEs in many cases conflict with each other. The GAPSO is applicable to PSs in general and no assumptions are made about the matching algorithm or the interpreter that executes the PS. The results of applying the GAPSO to some example PSs are presented. In all examples, the GAPSO found an optimal ordering of CEs in a small number of iterations.

We present an integrated methodology for the optimal management of nitrate contamination of ground water combining environmental assessment and economic cost evaluation through multi-criteria decision analysis. The proposed methodology incorporates an integrated physical modeling framework accounting for on-ground nitrogen loading and losses, soil nitrogen dynamics, and fate and transport of nitrate in ground water to compute the sustainable on-ground nitrogen loading such that the maximum contaminant level is not violated. A number of protection alternatives to stipulate the predicted sustainable on-ground nitrogen loading are evaluated using the decision analysis that employs the importance order of criteria approach for ranking and selection of the protection alternatives. The methodology was successfully demonstrated for the Sumas–Blaine aquifer in Washington State. The results showed the importance of using this integrated approach which predicts the sustainable on-ground nitrogen loadings and provides an insight into the economic consequences generated in satisfying the environmental constraints. The results also show that the proposed decision analysis framework, within certain limitations, is effective when selecting alternatives with competing demands.