Wind farm layout optimisation has become a very challenging and widespread problem in recent years. In many publications, the main goal is to achieve maximum power output minimum wind cost. This may be accomplished by applying single or multi-objective techniques. this paper, we apply objective hill-climbing algorithm (HCA) three evolutionary algorithms (NSGA-II, SPEA2 PESA-II) well-known bench...

There is a large number of applications in the design of telecommunication devices where it is necessary to apply optimisation techniques to find the best values for a set of parameters [2, 5]. Normally, it is not possible to solve those optimisation problems with analytical techniques, and the alternative is to use metaheuristic methods [6]. In this work we tackle the problem of designing coup...

The dynamics of a genetic algorithm (GA) on a model of a hard optimisation problem are analysed using a formalism which describes the changing tness distribution of the GA population under ranking selection, uniform crossover and mutation. The time to solve the optimisation problem is calculated in a closed form expression which enables the eeect of the various GA parameters | population size, ...

A greedy heuristic to solve a given combinatorial optimisation problem can be seen as an element of an infinite set of heuristics, H, which is defined by a function that depends on several parameters. We propose a procedure for determining the best element of H for a set of instances of the combinatorial optimisation problem. The procedure consists essentially in applying a direct non-linear op...

Dialogue management optimisation has been cast into a planning under uncertainty problem for long. Some methods such as Reinforcement Learning (RL) are now part of the state of the art. Whatever the solving method, strong assumptions are made about the dialogue system properties. For instance, RL assumes that the dialogue state space is Markovian. Such constraints may involve important engineer...

Hyperheuristics can be defined to be heuristics which choose between heuristics in order to solve a given optimisation problem or class of optimisation problems[5, 1]. One aim of using hyperheuristic methods is to achieve robustness, that is, to generate good-quality solutions for various problems or problem instances using the same method with very limited problem-specific knowledge. Over the ...

The paper presents mathematical underpinnings of the locally linear embedding technique for data dimensionality reduction. It is shown that a cogent framework for describing the method is that of optimisation on a Grassmann manifold. The solution delivered by the algorithm is characterised as a constrained minimiser for a problem in which the cost function and all the constraints are defined on...

Designing an optimal communication network is a complex, multiconstraint and multi-criterion optimisation problem. Previous work in this field has optimized a single objective or a combination of multiple values into a single scalar value. In this work, we use the multiobjective genetic optimisation technique along with the convergence to obtain a Pareto front – a set of solutions, which are no...

The goal of this study is to show the usefulness of reinforcement learning (RL) to solve a common greenhouse climate optimisation problem. The problem is to minimise the daily heating cost while achieving simultaneously two agronomic goals, namely maintaining a good crop growth and an appropriate development rate. The complexity of the problem is due to the very different time constants of thes...