Now showing items 1-10 of 25
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 comparative study of adaptive mutation operators for metaheuristics.
Genetic algorithms (GAs) are a class of stochastic optimization methods inspired by the principles of natural evolution. Adaptation of strategy parameters and genetic operators has become an important and promising research ...
Fast multi-swarm optimatization for dynamic optimization problems.
In the real world, many applications are non-stationary optimization problems. This requires that the optimization algorithms need to not only find the global optimal solution but also track the trajectory of the changing ...
A sequence based genetic algorithm with local search for the travelling salesman problem.
(University of Nottingham., 2009)
The standard Genetic Algorithm often suffers from slow convergence for solving combinatorial optimization problems. In this study, we present a sequence based genetic algorithm (SBGA) for the symmetric travelling salesman ...
An adaptive mutation operator for particle swarm optimization.
Particle swarm optimization (PSO) is an e cient tool for optimization and search problems. However, it is easy to be trapped into local optima due to its information sharing mechanism. Many research works have shown that ...
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 ...
Benchmark generator for CEC 2009 competition on dynamic optimization. Technical Report 2008.
(Department of Computer Science, University of Leicester., 2008)