Over the past two decades, the feedforward neural network (FNN) optimization has been a
key interest among the researchers and practitioners of multiple disciplines. The FNN …

A multiobjective optimization problem involves several conflicting objectives and has a set of
Pareto optimal solutions. By evolving a population of solutions, multiobjective evolutionary …

Over the last three decades, a large number of evolutionary algorithms have been
developed for solving multi-objective optimization problems. However, there lacks an upto …

Problems with multiple objectives arise in a natural fashion in most disciplines and their
solution has been a challenge to researchers for a long time. Despite the considerable …

Accurate tourist demand forecasting systems are essential in tourism planning, particularly
in tourism-based countries. Artificial neural networks are attracting attention to forecast …

Extracting the valuable information about the connections between the overall properties
and the related factors from the industrial big data of materials is of significant interest to the …

The present work reports on the development of modeling and optimization for micro-electric
discharge machining (μ-EDM) process. Artificial neural network (ANN) is used for analyzing …

We present the results of our investigation into the use of genetic algorithms (GAs) for
identifying near optimal design parameters of diagnostic systems that are based on artificial …

ABSTRACT A new data-driven reference vector-guided evolutionary algorithm has been
successfully implemented to construct surrogate models for various objectives pertinent to …

For the economic operation of a blast furnace, the thermal state change of a blast furnace
hearth (BFH), often represented by the change of the silicon content in hot metal, needs to …