1,924 research outputs found
Population-based algorithms for improved history matching and uncertainty quantification of Petroleum reservoirs
In modern field management practices, there are two important steps that shed light on a multimillion dollar investment. The first step is history matching where the simulation model is calibrated to reproduce the historical observations from the field. In this inverse problem, different geological and petrophysical properties may provide equally good history matches. Such diverse models are likely to show different production behaviors in future. This ties the history matching with the second step, uncertainty quantification of predictions. Multiple history matched models are essential for a realistic uncertainty estimate of the future field behavior. These two steps facilitate decision making and have a direct impact on technical and financial performance of oil and gas companies.
Population-based optimization algorithms have been recently enjoyed growing popularity for solving engineering problems. Population-based systems work with a group of individuals that cooperate and communicate to accomplish a task that is normally beyond the capabilities of each individual. These individuals are deployed with the aim to solve the problem with maximum efficiency.
This thesis introduces the application of two novel population-based algorithms for history matching and uncertainty quantification of petroleum reservoir models. Ant colony optimization and differential evolution algorithms are used to search the space of parameters to find multiple history matched models and, using a Bayesian framework, the posterior probability of the models are evaluated for prediction of reservoir performance.
It is demonstrated that by bringing latest developments in computer science such as ant colony, differential evolution and multiobjective optimization, we can improve the history matching and uncertainty quantification frameworks. This thesis provides insights into performance of these algorithms in history matching and prediction and develops an understanding of their tuning parameters. The research also brings a comparative study of these methods with a benchmark technique called Neighbourhood Algorithms. This comparison reveals the superiority of the proposed methodologies in various areas such as computational efficiency and match quality
A review: On path planning strategies for navigation of mobile robot
This paper presents the rigorous study of mobile robot navigation techniques used so far. The step by step investigations of classical and reactive approaches are made here to understand the development of path planning strategies in various environmental conditions and to identify research gap. The classical approaches such as cell decomposition (CD), roadmap approach (RA), artificial potential field (APF); reactive approaches such as genetic algorithm (GA), fuzzy logic (FL), neural network (NN), firefly algorithm (FA), particle swarm optimization (PSO), ant colony optimization (ACO), bacterial foraging optimization (BFO), artificial bee colony (ABC), cuckoo search (CS), shuffled frog leaping algorithm (SFLA) and other miscellaneous algorithms (OMA) are considered for study. The navigation over static and dynamic condition is analyzed (for single and multiple robot systems) and it has been observed that the reactive approaches are more robust and perform well in all terrain when compared to classical approaches. It is also observed that the reactive approaches are used to improve the performance of the classical approaches as a hybrid algorithm. Hence, reactive approaches are more popular and widely used for path planning of mobile robot. The paper concludes with tabular data and charts comparing the frequency of individual navigational strategies which can be used for specific application in robotics
Ant Colony Optimisation – A Proposed Solution Framework for the Capacitated Facility Location Problem
This thesis is a critical investigation into the development, application and evaluation
of ant colony optimisation metaheuristics, with a view to solving a class of
capacitated facility location problems. The study is comprised of three phases.
The first sets the scene and motivation for research, which includes; key concepts
of ant colony optimisation, a review of published academic materials and a
research philosophy which provides a justification for a deductive empirical mode
of study. This phase reveals that published results for existing facility location
metaheuristics are often ambiguous or incomplete and there is no clear evidence
of a dominant method. This clearly represents a gap in the current knowledge
base and provides a rationale for a study that will contribute to existing knowledge,
by determining if ant colony optimisation is a suitable solution technique for
solving capacitated facility location problems.
The second phase is concerned with the research, development and application
of a variety of ant colony optimisation algorithms. Solution methods presented
include combinations of approximate and exact techniques. The study
identifies a previously untried ant hybrid scheme, which incorporates an exact
method within it, as the most promising of techniques that were tested. Also a
novel local search initialisation which relies on memory is presented. These hybridisations
successfully solve all of the capacitated facility location test problems
available in the OR-Library.
The third phase of this study conducts an extensive series of run-time analyses,
to determine the prowess of the derived ant colony optimisation algorithms
against a contemporary cross-entropy technique. This type of analysis for measuring
metaheuristic performance for the capacitated facility location problem is
not evident within published materials. Analyses of empirical run-time distributions
reveal that ant colony optimisation is superior to its contemporary opponent.
All three phases of this thesis provide their own individual contributions to existing
knowledge bases: the production of a series of run-time distributions will be
a valuable resource for future researchers; results demonstrate that hybridisation
of metaheuristics with exact solution methods is an area not to be ignored; the
hybrid methods employed in this study ten years ago would have been impractical
or infeasible; ant colony optimisation is shown to be a very flexible metaheuristic
that can easily be adapted to solving mixed integer problems using hybridisation
techniques
A Survey and Analysis of Cooperative Multi-Agent Robot Systems: Challenges and Directions
Research in the area of cooperative multi-agent robot systems has received wide attention among researchers in recent years. The main concern is to find the effective coordination among autonomous agents to perform the task in order to achieve a high quality of overall performance. Therefore, this paper reviewed various selected literatures primarily from recent conference proceedings and journals related to cooperation and coordination of multi-agent robot systems (MARS). The problems, issues, and directions of MARS research have been investigated in the literature reviews. Three main elements of MARS which are the type of agents, control architectures, and communications were discussed thoroughly in the beginning of this paper. A series of problems together with the issues were analyzed and reviewed, which included centralized and decentralized control, consensus, containment, formation, task allocation, intelligences, optimization and communications of multi-agent robots. Since the research in the field of multi-agent robot research is expanding, some issues and future challenges in MARS are recalled, discussed and clarified with future directions. Finally, the paper is concluded with some recommendations with respect to multi-agent systems
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