Now showing items 1-10 of 22
Biology migration algorithm: A new nature-inspired heuristic methodology for global optimization
In this paper, inspired by the biology migration phenomenon, which is ubiquitous in the social evolution process in nature, a new meta-heuristic optimization paradigm called biology migration algorithm (BMA) is proposed. ...
Global and local surrogate-assisted differential evolution for expensive constrained optimization
(IEEE Press, 2018-03-29)
For expensive constrained optimization problems, the computation of objective function and constraints is very time-consuming. This paper proposes a novel global and local surrogate-assisted differential evolution for ...
Ant colony stream clustering: A fast density clustering algorithm for dynamic data streams
(IEEE Press, 2018-03-30)
A data stream is a continuously arriving sequence of data and clustering data streams requires additional considerations to traditional clustering. A stream is potentially unbounded, data points arrive on-line and each ...
A predictive strategy based on special points for evolutionary dynamic multi-objective optimization
There are some real-world problems in which multiple objectives conflict with each other and the objectives change with time. These problems require an optimization algorithm to track the moving Pareto front or Pareto set ...
Accelerating differential evolution based on a subset-to-subset survivor selection operator
Differential evolution (DE) is one of the most powerful and effective evolutionary algorithms for solving global optimization problems. However, just like all other metaheuristics, DE also has some drawbacks, such as slow ...
Benchmark Functions for the CEC'2018 Competition on Dynamic Multiobjective Optimization
(Newcastle University, 2018-01)
A proportion-based selection scheme for multi-objective optimization
(IEEE Press, 2018-02-08)
Classical multi-objective evolutionary algorithms (MOEAs) have been proven to be inefficient for solving multiobjective optimizations problems when the number of objectives increases due to the lack of sufficient selection ...
An evolutionary dynamic multi-objective optimization algorithm based on center-point prediction and sub-population autonomous guidance
Dynamic multi-objective optimization problems (DMOPs) provide a challenge in that objectives conflict each other and change over time. In this paper, a hybrid approach based on prediction and autonomous guidance is proposed, ...
A dynamic multiobjective evolutionary algorithm based on a dynamic evolutionary environment model
Traditional dynamic multiobjective evolutionary algorithms usually imitate the evolution of nature, maintaining diversity of population through different strategies and making the population track the Pareto optimal solution ...