Now showing items 1-10 of 142
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, ...
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 ...
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 ...
Adaptive crossover in genetic algorithms using statistics mechanism
(MIT Press, 2002)
Genetic Algorithms (GAs) emulate the natural evolution process and maintain a population of potential solutions to a given problem. Through the population, GAs implicitly maintain the statistics about the search space. ...
TSDE: A new differential evolution with two-stage optimization strategies for numerical optimization
(IEEE Press, 2016-07-25)
Differential Evolution (DE) is a popular paradigm of evolutionary algorithms, which has been successfully applied to solve different kinds of optimization problems. To design an effective DE, it is necessary to consider ...
Benchmark Functions for the CEC'2018 Competition on Dynamic Multiobjective Optimization
(Newcastle University, 2018-01)
A self-organizing random immigrants genetic algorithm for dynamic optimization problems
In this paper a genetic algorithm is proposed where the worst individual and individuals with indices close to its index are replaced in every generation by randomly generated individuals for dynamic optimization problems. ...
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 ...