Now showing items 1-10 of 20
Particle filter with swarm move for optimization.
We propose a novel generalized algorithmic framework to utilize particle filter for optimization incorporated with the swarm move method in particle swarm optimization (PSO). In this way, the PSO update equation is treated ...
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 ...
Evolutionary programming with q-Gaussian mutation for evolutionary optimization problems.
The use of evolutionary programming algorithms with self-adaptation of the mutation distribution for dynamic optimization problems is investigated in this paper. In the proposed method, the q-Gaussian distribution is ...
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 ...
Benchmark generator for CEC 2009 competition on dynamic optimization. Technical Report 2008.
(Department of Computer Science, University of Leicester., 2008)
A genetic-inspired joint multicast routing and channel assignment algorithm in wireless mesh networks.
This paper proposes a genetic algorithm (GA) based optimization approach to search a minimum-interference multicast tree which satisfies the end-to-end delay constraint and optimizes the usage of the scarce radio network ...
Genetic algorithms with memory- and elitism-based immigrants in dynamic environments.
(MIT Press., 2008)
In recent years the genetic algorithm community has shown a growing interest in studying dynamic optimization problems. Several approaches have been devised. The random immigrants and memory schemes are two major ones. The ...
Learning in abstract memory schemes for dynamic optimization.
We investigate an abstraction based memory scheme for evolutionary algorithms in dynamic environments. In this scheme, the abstraction of good solutions (i.e., their approximate location in the search space) is stored in ...
A memetic algorithm for the university course timetabling problem.
The design of course timetables for academic institutions is a very hectic job due to the exponential number of possible feasible timetables with respect to the problem size. This process involves lots of constraints that ...
Hyper-selection in dynamic environments.
In recent years, several approaches have been developed for genetic algorithms to enhance their performance in dynamic environments. Among these approaches, one kind of methods is to adapt genetic operators in order for ...