Now showing items 21-28 of 28
Dynamics in the Multi-objective Subset Sum: Analysing the Behavior of Population Based Algorithms
Evolutionary dynamic optimization: test and evaluation environments.
Memetic algorithms for dynamic optimization problems
Dynamic optimization problems challenge traditional evolutionary algorithms seriously since they, once converged, cannot adapt quickly to environmental changes. This chapter investigates the application of memetic algorithms, ...
A multiobjective particle swarm optimization for load scheduling in electric smelting furnaces
(IEEE Press, 2013-09-26)
Electric smelting furnaces, applied in the smelting process of infusible mineral, are highly energy-intensive. In China, they waste a huge amount of electric energy, but yield a small quantity of valuable metals due to the ...
Multi-population methods with adaptive mutation for multi-modal optimization problems
(AirCC Publishing, 2013-04)
This paper presents an efficient scheme to locate multiple peaks on multi-modal optimization problems by using genetic algorithms (GAs). The premature convergence problem shows due to the loss of diversity, the multi-population ...
Benchmark Generator for the IEEE WCCI-2014 Competition on Evolutionary Computation for Dynamic Optimization Problems: Dynamic Rotation Peak Benchmark Generator (DRPBG) and Dynamic Composition Benchmark Generator (DCBG)
(De Montfort University, UK, 2013-10)
Based on our previous benchmark generator for the IEEE CEC’12 Competition on Dynamic Optimization, this report updates the two benchmark instances where two new features have 1been developed as well as a constraint to the ...