Now showing items 11-20 of 21
A performance indicator for reference-point-based multiobjective evolutionary optimization
Aiming at the difficulty in evaluating preference-based evolutionary multiobjective optimization, this paper proposes a new performance indicator. The main idea is to project the preferred solutions onto a constructed ...
A loosely coupled hybrid meta-heuristic algorithm for the static independent task scheduling problem in grid computing
(IEEE Press, 2018-03)
Task scheduling is one of the most difficult problems in grid computing systems. Therefore, various studies have been proposed to present methods which provide efficient schedules. Meta-heuristic approaches are among the ...
Guest editorial: Computational intelligence for cloud computing
(IEEE Press, 2018-02)
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 ...
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 ...
An empirical study of dynamic triobjective optimisation problems
(IEEE Press, 2018-07)
Dynamic multiobjective optimisation deals with multiobjective problems whose objective functions, search spaces, or constraints are time-varying during the optimisation process. Due to wide presence in real-world applications, ...
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 ...
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 ...
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 ...
Ant colony optimization for dynamic combinatorial optimization problems
(The Institution of Engineering and Technology, 2018-02)
The ant colony optimization (ACO) metaheuristic was inspired from the foraging behaviour of real ant colonies. In particular, real ants communicate indirectly via pheromone trails and find the shortest path. Although real ...