Now showing items 1-10 of 15
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 ...
Evolutionary dynamic optimization: test and evaluation environments.
Multi-population methods in unconstrained continuous dynamic environments: the challenges
The multi-population method has been widely used to solve unconstrained continuous dynamic optimization problems with the aim of maintaining multiple populations on different peaks to locate and track multiple changing ...
Adaptive learning particle swarm optimizer-II for global optimization.
This paper presents an updated version of the adaptive learning particle swarm optimizer (ALPSO), we call it ALPSO-II. In order to improve the performance of ALPSO on multi-modal problems, we introduce several new major ...
An Adaptive Multi-Population Framework for Locating and Tracking Multiple Optima
(IEEE Press, 2015-11-30)
Multi-population methods are effective to solve dynamic optimization problems. However, to efficiently track multiple optima, algorithm designers need to address a key issue: how to adapt the number of populations. In this ...
Benchmark Generator for the IEEE WCCI-2012 Competition on Evolutionary Computation for Dynamic Optimization Problems
(Brunel University, U.K., 2011-10)
Based on our previous benchmark generator for the IEEE CEC’09 Competition on Dynamic Optimization, this report updates the two benchmark instances where a new change type has been developed as well as a constraint to the ...
Maintaining diversity by clustering in dynamic environments.
Maintaining population diversity is a crucial issue for the performance of evolutionary algorithms (EAs) in dynamic environments. In the literature of EAs for dynamic optimization problems (DOPs), many studies have been ...
Benchmark Generator for the IEEE WCCI-2014 Competition on Evolutionary Computation for Dynamic Optimization Problems: Dynamic Travelling Salesman Problem Benchmark Generator
(De Montfort University, U.K., 2013-10)
In this report, the dynamic benchmark generator for permutation-encoded problems for the travelling salesman problem (DBGPTSP) proposed in is used to convert any static travelling salesman problem benchmark to a dynamic ...
A directed mutation operator for real coded genetic algorithms.
Developing directed mutation methods has been an interesting research topic to improve the performance of genetic algorithms (GAs) for function optimization. This paper introduces a directed mutation (DM) operator for GAs ...
Benchmark generator for the IEEE WCCI-2012 competition on evolutionary computation for dynamic optimization problems. Technical Report 2011.
(Department of Information Systems and Computing, Brunel University., 2011)