Now showing items 1-10 of 22
Hybrid meta-heuristic algorithms for independent job scheduling in grid computing
The term ’grid computing’ is used to describe an infrastructure that connects geographically distributed computers and heterogeneous platforms owned by multiple organizations allowing their computational power, storage ...
Benchmark Functions for the CEC'2018 Competition on Dynamic Multiobjective Optimization
(Newcastle University, 2018-01)
A multi-objective evolutionary algorithm based on coordinate transformation
(IEEE Press, 2018-05-28)
In this paper, a novel multiobjective evolutionary algorithm (MOEA/CT) is proposed to better manage convergence and distribution of solutions when MOEAs are used for solving multiobjective optimization problems. The ...
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 ...
Less detectable environmental changes in dynamic multiobjective optimisation
(ACM Press, 2018-05-04)
Multiobjective optimisation in dynamic environments is challenging due to the presence of dynamics in the problems in question. Whilst much progress has been made in benchmarks and algorithm design for dynamic multiobjective ...
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. ...
A many-objective evolutionary algorithm based on rotated grid
Evolutionary optimization algorithms, a meta-heuristic approach, often encounter considerable challenges in many-objective optimization problems (MaOPs). The Pareto-based dominance loses its effectiveness in MaOPs, which ...
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 ...
A Pareto-based many-objective evolutionary algorithm using space partitioning selection and angle-based truncation
Evolutionary algorithms (EAs) have shown to be efficient in dealing with many-objective optimization problems (MaOPs) due to their ability to obtain a set of compromising solutions which not only converge toward the Pareto ...
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 ...