Now showing items 1-10 of 15
Memory-based multi-population genetic learning for dynamic shortest path problems
(IEEE Press, 2019-06)
This paper proposes a general algorithm framework for solving dynamic sequence optimization problems (DSOPs). The framework adapts a novel genetic learning (GL) algorithm to dynamic environments via a clustering-based ...
A clustering particle swarm optimizer for locating and tracking multiple optima in dynamic environments
In the real world, many optimization problems are dynamic. This requires an optimization algorithm to not only find the global optimal solution under a specific environment but also to track the trajectory of the changing ...
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.
A self-learning particle swarm optimizer for global optimization problems
Particle swarm optimization (PSO) has been shown as an effective tool for solving global optimization problems. So far, most PSO algorithms use a single learning pattern for all particles, which means that all particles ...
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 ...