A service oriented architecture for decision making in engineering design

Decision making in engineering design can be effectively addressed by using genetic algorithms to solve multi-objective problems. These multi-objective genetic algorithms (MOGAs) are well suited to implementation in a Service Oriented Architecture. Often the evaluation process of the MOGA is compute-intensive due to the use of a complex computer model to represent the real-world system. The emerging paradigm of Grid Computing offers a potential solution to the compute-intensive nature of this objective function evaluation, by allowing access to large amounts of compute resources in a distributed manner. This paper presents a grid-enabled framework for multi-objective optimisation using genetic algorithms (MOGA-G) to aid decision making in engineering design

Calibration of microscopic traffic flow simulation models using a memetic algorithm with solis and wets local search chaining (MA-SW-Chains)

Traffic models require calibration to provide an adequate representation of the actual field conditions. This study presents the adaptation of a memetic algorithm (MA-SW-Chains) based on Solis and Wets local search chains, for the calibration of microscopic traffic flow simulation models. The effectiveness of the proposed MA-SW-Chains approach was tested using two vehicular traffic flow models (McTrans and Reno). The results were superior compared to two state-of-the-art approaches found in the literature: (i) a single-objective genetic algorithm that uses simulated annealing (GASA), and (ii) a stochastic approximation simultaneous perturbation algorithm (SPSA). The comparison was based on tuning time, runtime and the quality of the calibration, measured by the GEH statistic (which calculates the difference between the counts of real and simulated links)