Now showing items 1-10 of 28
Evolutionary computation for dynamic optimization problems.
A comparative study on particle swarm optimization in dynamic environments.
A grid-based evolutionary algorithm for many-objective optimization
Balancing convergence and diversity plays a key role in evolutionary multiobjective optimization (EMO). Most current EMO algorithms perform well on problems with two or three objectives, but encounter difficulties in their ...
Dynamic vehicle routing: A memetic ant colony optimization approach
Evolutionary computation for dynamic optimization problems
(ACM Press, 2013-07)
Adapting the pheromone evaporation rate in dynamic routing problems
Shift-based density estimation for Pareto-based algorithms in many-objective optimization
(IEEE Press, 2013-05-16)
It is commonly accepted that Pareto-based evolutionary multiobjective optimization (EMO) algorithms encounter difficulties in dealing with many-objective problems. In these algorithms, the ineffectiveness of the Pareto ...