Now showing items 1-10 of 217
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 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 ...
Guest editorial: memetic computing in the presence of uncertainties.
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 ...