Search

We move from a boundedly rational consumer model (Naimzada and Tramontana, 2008, 2010) characterized by a gradient-like decisional process in which, under particular parameters conditions, the asymptotical convergence to the optimal choice does not happen but it does under a least squared learning mechanism. In the present paper, we prove that even a less sophisticated learning mechanism leads to convergence to the rational choice and also prove that convergence is ensured when both learning mechanisms are available. The stability results that we obtain give more strength to the rational behavior assumption of the original model; in fact, the less demanding is the learning mechanism ensuring convergence to the rational behavior, the higher is the probability that even quite naive consumers will learn the composition of their optimum consumption bundles. [ABSTRACT FROM AUTHOR]

This paper presents a 2D convergence density criterion for minimizing the total junction delay at isolated junctions in the lane-based optimization framework. The lane-based method integrates the design of lane markings and signal settings for traffic movements in a unified framework. The problem of delay minimization is formulated as a Binary Mix Integer Non Linear Program (BMINLP). A cutting plane algorithm can be applied to solve this difficult BMINLP problem by adding hyperplanes sequentially until sufficient numbers of planes are created in the form of solution constraints to replicate the original nonlinear surface in the solution space. A set of constraints is set up to ensure the feasibility and safety of the resultant optimized lane markings and signal settings. The main difficulty to solve this high-dimension nonlinear nonconvex delay minimization problem using cutting plane algorithm is the requirement of substantial computational efforts to reach a good-quality solution while approximating the nonlinear solution space. A new stopping criterion is proposed by monitoring a 2D convergence density to obtain a converged solution. A numerical example is given to demonstrate the effectiveness of the proposed methodology. The cutting-plane algorithm producing an effective signal design will become more computationally attractive with adopting the proposed stopping criterion. [ABSTRACT FROM AUTHOR]

Artificial Bee Colony (ABC) is one of the most recently introduced algorithms based on the intelligent foraging behavior of a honey bee swarm. This paper presents an extended ABC algorithm, namely, the Cooperative Article Bee Colony (CABC), which significantly improves the original ABC in solving complex optimization problems. Clustering is a popular data analysis and data mining technique; therefore, the CABC could be used for solving clustering problems. In this work, first the CABC algorithm is used for optimizing six widely used benchmark functions and the comparative results produced by ABC, Particle Swarm Optimization (PSO), and its cooperative version (CPSO) are studied. Second, the CABC algorithm is used for data clustering on several benchmark data sets. The performance of CABC algorithm is compared with PSO, CPSO, and ABC algorithms on clustering problems. The simulation results show that the proposed CABC outperforms the other three algorithms in terms of accuracy, robustness, and convergence speed. [ABSTRACT FROM AUTHOR]

Standard Runge-Kutta methods are explicit, one-step, and generally constant step-size numericalintegrators for the solution of initial value problems. Such integration schemes of orders 3, 4, and 5require 3, 4, and 6 function evaluations per time step of integration, respectively. In this paper, wepropose a set of simple, explicit, and constant step-size Accerelated-Runge-Kutta methods that aretwo-step in nature. For orders 3, 4, and 5, they require only 2, 3, and 5 function evaluations per timestep, respectively. Therefore, they are more computationally efficient at achieving the same orderof local accuracy. We present here the derivation and optimization of these accelerated integrationmethods. We include the proof of convergence and stability under certain conditions as well asstability regions for finite step sizes. Several numerical examples are provided to illustrate theaccuracy, stability, and efficiency of the proposed methods in comparison with standard Runge-Kutta methods.