Metaheuristics in recent years has proven its effectiveness; however, robust algorithms that can solve real-world problems are always needed. In this paper, we suggest the first extended version of recently introduced gaining–sharing knowledge optimization (GSK) algorithm, named multiobjective (MOGSK), to deal with (MOPs). MOGSK employs an external archive population store nondominated solution...

In this paper, lift and drag coefficients were numerically investigated using NUMECA software in a set of 4-digit NACA airfoils. Two metamodels based on the evolved group method of data handling (GMDH) type neural networks were then obtained for modeling both lift coefficient (CL) and drag coefficient (CD) with respect to the geometrical design parameters. After using such obtained polynomial n...

New Product Development (NPD) constitutes a challenging problem in the pharmaceutical industry, due to the characteristics of the development pipeline. Formally, the NPD problem can be stated as follows: select a set of R&D projects from a pool of candidate projects in order to satisfy several criteria (economic profitability, time to market) while coping with the uncertain nature of the projec...

A multi-objective genetic algorithm, NSGAII, is applied to calibrate a distributed hydrological model (WetSpa) for prediction of river discharges. The goals of this study include (i) analysis of the applicability of multiobjective approach for WetSpa calibration instead of the traditional approach, i.e. the Parameter ESTimator software (PEST), and (ii) identifiability assessment of model parame...

Coordinating traffic signals can provide great savings to motorists in terms of reduced delays and number of vehicular stops. In order to maximize benefits, engineers need to use a mechanism by which the most optimal timing plans are activated when the traffic patterns change. Common ways of accomplishing this need is by using Time of Day (TOD) plan scheduling, or Traffic Responsive Plan Select...

We would like to thank Jasper Vrugt for his comment on our recent paper Tang et al. (2006) in which we compare the Strength Pareto Evolutionary Algorithm 2 (SPEA2), the Multi-objective Shuffled Complex Evolution Metropolis algorithm (MOSCEM-UA), and the Epsilon Dominance Nondominated Sorted Genetic Algorithm II (ε-NSGAII) using a statistical metrics-based approach. To frame our response, we wil...

Cooperative coevolutionary evolutionary algorithms differ from standard evolutionary algorithms’ architecture in that the population is split into subpopulations, each of them optimising only a sub-vector of the global solution vector. All subpopulations cooperate by broadcasting their local partial solutions such that each subpopulation can evaluate complete solutions. Cooperative coevolution ...