Now showing items 1-10 of 248
A two-layer optimisation management method for the microgrid with electric vehicles
(IEEE Press, 2019-06)
The energy management of the microgrid (MG) with electric vehicles (EVs) is a large-scale optimization problem where the goal should take into account the performance and economic benefits of the power system while meeting ...
Memory-based multi-population genetic learning for dynamic shortest path problems
(IEEE Press, 2019-06)
This paper proposes a general algorithm framework for solving dynamic sequence optimization problems (DSOPs). The framework adapts a novel genetic learning (GL) algorithm to dynamic environments via a clustering-based ...
Pareto or Non-Pareto: Bi-Criterion Evolution in Multi-Objective Optimization
It is known that Pareto dominance has its own weaknesses as the selection criterion in evolutionary multi-objective optimization. Algorithms based on Pareto dominance can suffer from slow convergence to the optimal front, ...
Adapting the pheromone evaporation rate in dynamic routing problems
An ant system with direct communication for the capacitated vehicle routing problem.
(University of Manchester., 2011)
In Silico Discovery of Significant Pathways in Colorectal Cancer Metastasis Using a Two-Stage Optimization Approach
Accurate and reliable modelling of protein-protein interaction networks for complex diseases such as colorectal cancer can help better understand mechanism of diseases and potentially discover new drugs. Different machine ...
A memetic algorithm with adaptive hill climbing strategy for dynamic optimization problems.
Dynamic optimization problems challenge traditional evolutionary algorithms seriously since they, once converged, cannot adapt quickly to environmental changes. This paper investigates the application of memetic algorithms, ...
A memetic particle swarm optimization algorithm for multimodal optimization problems.
(Elsevier B.V., 2012)
Recently, multimodal optimization problems (MMOPs) have gained a lot of attention from the evolutionary algorithm (EA) community since many real-world applications are MMOPs and may require EAs to present multiple optimal ...
A clustering particle swarm optimizer for locating and tracking multiple optima in dynamic environments
In the real world, many optimization problems are dynamic. This requires an optimization algorithm to not only find the global optimal solution under a specific environment but also to track the trajectory of the changing ...