Now showing items 1-10 of 242
Constraint satisfaction adaptive neural network and heuristics combined approaches for generalized job-shop scheduling
(IEEE Press, 2000-03-01)
This paper presents a constraint satisfaction adaptive neural network, together with several heuristics, to solve the generalized job-shop scheduling problem, one of NP-complete constraint satisfaction problems. The proposed ...
Benchmark Functions for the CEC'2017 Competition on Many-Objective Optimization
(University of Birmingham, U.K., 2017-01)
In the real world, it is not uncommon to face an optimization problem with more than three objectives. Such problems, called many-objective optimization problems (MaOPs), pose great challenges to the area of evolutionary ...
Multi-line distance minimization: A visualized many-objective test problem suite
Studying the search behavior of evolutionary manyobjective optimization is an important, but challenging issue. Existing studies rely mainly on the use of performance indicators which, however, not only encounter increasing ...
Guest editorial: Memetic computing in the presence of uncertainties
Population-based incremental learning with memory scheme for changing environments
(ACM Press, 2005)
In recent years there has been a growing interest in studying evolutionary algorithms for dynamic optimization problems due to its importance in real world applications. Several approaches have been developed, such as the ...
Memory-based immigrants for genetic algorithms in dynamic environments
(ACM Press, 2005)
Investigating and enhancing the performance of genetic algorithms in dynamic environments have attracted a growing interest from the community of genetic algorithms in recent years. This trend reflects the fact that many ...
Associative memory scheme for genetic algorithms in dynamic environments
In recent years dynamic optimization problems have attracted a growing interest from the community of genetic algorithms with several approaches developed to address these problems, of which the memory scheme is a major ...
Finding multi-density clusters in non-stationary data streams using an ant colony with adaptive parameters
(IEEE Press, 2017-06)
Density based methods have been shown to be an effective approach for clustering non-stationary data streams. The number of clusters does not need to be known a priori and density methods are robust to noise and changes ...
A survey of swarm intelligence for dynamic optimization: Algorithms and applications
Swarm intelligence (SI) algorithms, including ant colony optimization, particle swarm optimization, bee-inspired algorithms, bacterial foraging optimization, firefly algorithms, fish swarm optimization and many more, have ...