Now showing items 1-10 of 15
Optimisation of a Stagger Chart for Aviation Fleet Planning
(Multidsciplinary International Scheduling Conference: Theory and Applications (MISTA 2015), 2015)
Continuous Parameter Pools in Ensemble Differential Evolution
Ensemble of parameters and mutation strategies differential evolution (EPSDE) is an elegant promising optimization framework based on the idea that a pool of mutation and crossover strategies along, with associated pools ...
The Importance of Being Structured: a Comparative Study on Multi Stage Memetic Approaches
Memetic Computing (MC) is a discipline which studies optimization algorithms and sees them as structures of operators, the memes. Although the choice of memes is crucial for an effective algorithmic design, special attention ...
Meta-Lamarckian learning in three stage optimal memetic exploration
(IEEE Xplore, 2012-09)
Three Stage Optimal Memetic Exploration (3SOME) is a single-solution optimization algorithm where the coordinated action of three distinct operators progressively perturb the solution in order to progress towards the ...
A Differential Evolution Framework with Ensemble of Parameters and Strategies and Pool of Local Search Algorithms
(Springer Berlin Heidelberg, 2014-11)
The ensemble structure is a computational intelligence supervised strategy consisting of a pool of multiple operators that compete among each other for being selected, and an adaptation mechanism that tends to reward the ...
Multicriteria adaptive differential evolution for global numerical optimization
(IOS Press, 2015-02-01)
Differential evolution (DE) has become a prevalent tool for global optimization problems since it was proposed in 1995. As usual, when applying DE to a specific problem, determining the most proper strategy and its associated ...
Structural bias in population-based algorithms
Abstract Challenging optimisation problems are abundant in all areas of science and industry. Since the 1950s, scientists have responded to this by developing ever-diversifying families of ‘black box’ optimisation algorithms. ...
A comparison of three Differential Evolution strategies in terms of early convergence with different population sizes
Differential Evolution (DE) is a popular population-based continuous optimization algorithm that generates new candidate solutions by perturbing the existing ones, using scaled differences of randomly selected solutions ...
Improving anytime behavior for traffic signal control optimization based on NSGA-II and local search
Multi-Objective Evolutionary Algorithms (MOEAs) and transport simulators have been widely utilized to optimise traffic signal timings with multiple objectives. However, traffic simulations require much processing time and ...
Logan's run: Lane optimisation using genetic algorithms based on nsga-ii
Whilst bus lanes are an important tool to ensure bus time reliability their presence can be detrimental to urban traffic. In this paper a Non-dominated Sorting Genetic Algorithm (NSGA-II) has been adopted to study the ...