Now showing items 61-70 of 102
Improving the Multiobjective Evolutionary Algorithm Based on Decomposition with New Penalty Schemes
It has been increasingly reported that the multiobjective optimization evolutionary algorithm based on decomposition (MOEA/D) is promising for handling multiobjective optimization problems (MOPs). MOEA/D employs scalarizing ...
Training neural networks with ant colony optimization algorithms for pattern classification
Feed-forward neural networks are commonly used for pattern classification. The classification accuracy of feed-forward neural networks depends on the configuration selected and the training process. Once the architecture ...
Evolutionary computation for dynamic optimization problems
(ACM Press, 2013-07)
An Improved Multiobjective Optimization Evolutionary Algorithm Based on Decomposition for Complex Pareto Fronts
The multi-objective evolutionary algorithm based on decomposition (MOEA/D) has shown to be very efficient in solving multi-objective optimization problems (MOPs). In practice, the Pareto-optimal front (POF) of many MOPs ...
A framework of scalable dynamic test problems for dynamic multi-objective optimization
(IEEE Press, 2014-12)
Dynamic multi-objective optimization has received increasing attention in recent years. One of striking issues in this field is the lack of standard test suites to determine whether an algorithm is capable of solving dynamic ...
An adaptive multi-swarm optimizer for dynamic optimization problems
(The MIT Press, 2014-01-17)
The multi-population method has been widely used to solve dynamic optimization problems (DOPs) with the aim of maintaining multiple populations on different peaks to locate and track multiple changing optima simultaneously. ...
A loosely coupled hybrid meta-heuristic algorithm for the static independent task scheduling problem in grid computing
(IEEE Press, 2018-03)
Task scheduling is one of the most difficult problems in grid computing systems. Therefore, various studies have been proposed to present methods which provide efficient schedules. Meta-heuristic approaches are among the ...
Guest editorial: Computational intelligence for cloud computing
(IEEE Press, 2018-02)
A dynamic multiobjective evolutionary algorithm based on a dynamic evolutionary environment model
Traditional dynamic multiobjective evolutionary algorithms usually imitate the evolution of nature, maintaining diversity of population through different strategies and making the population track the Pareto optimal solution ...
Global and local surrogate-assisted differential evolution for expensive constrained optimization
(IEEE Press, 2018-03-29)
For expensive constrained optimization problems, the computation of objective function and constraints is very time-consuming. This paper proposes a novel global and local surrogate-assisted differential evolution for ...