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...

Hyper-heuristics are emerging methodologies that perform a search over the space of heuristics to solve difficult computational optimization problems. There are two main types of hyper-heuristics: selective and generative hyper-heuristics. An online selective hyper-heuristic framework manages a set of low level heuristics and aims to choose the best one at any given time using a performance mea...

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 ...

Generally, the main focus of grid-linked photovoltaic systems is to scale up penetration level ensure full electricity consumption coverage. However, due stochasticity and nondispatchable nature its generation, significant adverse impacts such as power overloading, voltage, harmonics, current, frequency instabilities on utility grid arise. These vary in severity a function degree system. Thus, ...

The increasing complexity of circuit design needs to be managed with appropriate optimization algorithms and accurate statistical description of design models in order to reach the design specifics, guaranteeing ’’zero defects’’. In the Design for Yield open problems are the design of effective optimization algorithms and statistical analysis for yield design, which require time consuming techn...