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

In multi-objective evolutionary algorithms (MOEAs), non-domina-ted sorting is one of the
critical steps to locate efficient solutions. A large percentage of computational cost of MOEAs …

Variable-rate fertilization (VRF) decision is a key aspect of prescription generation in
precision agriculture, which typically involves multiple criteria and objectives. This paper …

Most current approaches in the evolutionary multiobjective optimization literature
concentrate on adapting an evolutionary algorithm to generate an approximation of the …

List of References on Evolutionary Multiobjective Optimization Page 1 List of References on
Evolutionary Multiobjective Optimization Carlos A. Coello Coello ccoello@cs.cinvestav.mx …

This paper presents an evolutionary algorithm, called the multi-objective symbiotic
evolutionary algorithm (MOSEA), to solve a multi-objective FMS process planning (MFPP) …

Nowadays, ontologies are widely used to solve data heterogeneity problems on the
Semantic Web. However, simple use of these ontologies may raise the heterogeneity …

Abstract In Wireless Sensor Networks (WSNs) instead of using the possible network
connectivity to its maximum extent, a deliberate choice must be made to restrict the topology …

It's an basic requirement in cloud computing that scheduling virtual resources to physical
resources with balance load, however, the simple scheduling methods can not meet this …

The cellular automata (CA) model is the predominant method for predicting land use and
land cover (LULC) changes. The accuracy of this model critically depends on well-defined …