Irene Moser

A simple and efficient multi-component algorithm for solving dynamic function optimisation problems

A new multi-phase multi-individual version of the extremal optimisation algorithm was devised for dynamic function optimisation. The algorithm was tested on the three standardised benchmark scenarios of the publicly available moving peaks (MP) problem and observed to outperform all numerical results of other algorithmic approaches currently available in the literature. Parts of the algorithm were subsequently tested on variations of the scenarios to establish the role of each algorithm component in solving the problem as well as its contribution to the overall result The reasons for the algorithms impressive performance on the particular problem instance are discussed and possible limitations to its wider applicability are identified.

Architecture-based reliability evaluation under uncertainty

The accuracy of architecture-based reliability evaluations depends on a number of parameters that need to be estimated, such as environmental factors or system usage. Researchers have tackled this problem by including uncertainties in architecture evaluation models and solving them analytically and with simulations. The usual assumption is that the input parameter distributions are normal, and that it is sufficient to report the attributes that describe the system in terms of the mean and variance of the attribute. In this work, we introduce a simulation-based approach that can accommodate a diverse set of parameter range distributions, self-regulate the number of architecture evaluations to the desired significance level and reports the desired percentiles of the values which ultimately characterise a specific quality attribute of the system. We include a case study which illustrates the flexibility of this approach using the evaluation of system reliability.

Let the Ants Deploy Your Software - An ACO Based Deployment Optimisation Strategy

Decisions regarding the mapping of software components to hardware nodes affect the quality of the resulting system. Making these decisions is hard when considering the ever-growing complexity of the search space, as well as conflicting objectives and constraints. An automation of the solution space exploration would help not only to make better decisions but also to reduce the time of this process. In this paper, we propose to employ Ant Colony Optmisation (ACO) as a multi-objective optimisation strategy. The constructive approach is compared to an iterative optimisation procedure - a Genetic Algorithm (GA) adaptation - and was observed to perform suprisingly similar, although not quite on a par with the GA, when validated based on a series of experiments.

Dynamic function optimisation with hybridised extremal dynamics

Dynamic function optimisation is an important research area because many real-world problems are inherently dynamic in nature. Over the years, a wide variety of algorithms have been proposed to solve dynamic optimisation problems, and many of these algorithms have used the Moving Peaks (MP) benchmark to test their own capabilities against other approaches. This paper presents a detailed account of our hybridised Extremal Optimisation (EO) approach that has achieved hitherto unsurpassed results on the three standardised scenarios of the MP problem. Several different components are used in the hybrid EO, and it has been shown that a large proportion of the quality of its outstanding performance is due to the local search component. In this paper, the behaviour of the local search algorithms used is analysed, and the roles of other components are discussed. In the concluding remarks, the generalisation ability of this method and its wider applicability are highlighted.

The automotive deployment problem: A practical application for constrained multiobjective evolutionary optimisation

State-of-the art constrained multiobjective optimisation methods are often explored and demonstrated with the help of function optimisation problems from these accounts. It is sometimes hard for practitioners to extract good approaches for practical problems. In this paper we apply an evolutionary algorithm to a factual problem with realistic constraints and compare the effects of different operators and constraint handling methods. We observe that in spite of an apparently very insular search space, we consistently obtain the best results when using a repair mechanism, effectively eliminating infeasible solutions. This runs contrary to some recommendations in the optimisation literature which propose penalty functions for search spaces where feasible solutions are sparse.

Solving Dynamic Single-Runway Aircraft Landing Problems With Extremal Optimisation

A dynamic implementation of the single-runway aircraft landing problem was chosen for experiments designed to investigate the adaptive capabilities of extremal optimisation. As part of the problem space is unimodal, we developed a deterministic algorithm which optimises the time lines of the permutations found by the EO solver. To assess our results, we experimented on known problem instances for which benchmark solutions exist. The nature and difficulty of the instances used were assessed to discuss the quality of results obtained by the solver. Compared to the benchmark results available, our approach was highly competitive

Predictive parameter control

In stochastic optimisation, all currently employed algorithms have to be parameterised to perform effectively. Users have to rely on approximate guidelines or, alternatively, undertake extensive prior tuning. This study introduces a novel method of parameter control, i.e. the dynamic and automated variation of values for parameters used in approximate algorithms. The method uses an evaluation of the recent performance of previously applied parameter values and predicts how likely each of the parameter values is to produce optimal outcomes in the next cycle of the algorithm. The resulting probability distribution is used to determine the parameter values for the following cycle. The results of our experiments show a consistently superior performance of two very different EA algorithms when they are parameterised using the predictive parameter control method.

Entropy-based adaptive range parameter control for evolutionary algorithms

Evolutionary Algorithms are equipped with a range of adjustable parameters, such as crossover and mutation rates which significantly influence the performance of the algorithm. Practitioners usually do not have the knowledge and time to investigate the ideal parameter values before the optimisation process. Furthermore, different parameter values may be optimal for different problems, and even problem instances. In this work, we present a parameter control method which adjusts parameter values during the optimisation process using the algorithms performance as feedback. The approach is particularly effective with continuous parameter intervals, which are adapted dynamically. Successful parameter ranges are identified using an entropy-based clusterer, a method which outperforms state-of-the-art parameter control algorithms.

Adaptive Range Parameter Control

All existing stochastic optimisers such as Evolutionary Algorithms require parameterisation which has a significant influence on the algorithms performance. In most cases, practitioners assign static values to variables after an initial tuning phase. This parameter tuning method requires experience the practitioner may not have and, when done conscientiously, is rather time-consuming. Also, the use of parameter values that remain constant over the optimisation process has been observed to achieve suboptimal results. This work presents a parameter control method which redefines variables repeatedly based on a separate optimisation process which receives its feedback from the primary optimisation algorithm. The feedback is used for a projection of the value performing well in the future. The parameter values are sampled from intervals which are adapted dynamically, a method which has proved particularly effective and outperforms all existing adaptive parameter controls significantly.

Road traffic optimisation using an evolutionary game

In a commuting scenario, drivers expect to arrive at their destinations on time. Drivers have an expectation as to how long it will take to reach the destination. To this end, drivers make independent decisions regarding the routes they take. Independent decision-making is uncoordinated and unlikely to lead to a balanced usage of the road network. However, a well-balanced traffic situation is in the best interest of all drivers, as it minimises their travel times on average over time. This study investigates the possibility of using an Evolutionary Game, Minority Game (MG), to achieve a balanced usage of a road network through independent decisions made by drivers assisted by the MG algorithm. The experimental results show that this simple game-theoretic approach can achieve a near-optimal distribution of traffic in a network. An optimal distribution can be assumed to lead to equitable travel times which are close to the possible minimum considering the number of cars in the network.