7,239 research outputs found
Hybrid algorithms for distributed constraint satisfaction.
A Distributed Constraint Satisfaction Problem (DisCSP) is a CSP which is divided into several inter-related complex local problems, each assigned to a different agent. Thus, each agent has knowledge of the variables and corresponding domains of its local problem together with the constraints relating its own variables (intra-agent constraints) and the constraints linking its local problem to other local problems (inter-agent constraints). DisCSPs have a variety of practical applications including, for example, meeting scheduling and sensor networks. Existing approaches to Distributed Constraint Satisfaction can be mainly classified into two families of algorithms: systematic search and local search. Systematic search algorithms are complete but may take exponential time. Local search algorithms often converge quicker to a solution for large problems but are incomplete. Problem solving could be improved through using hybrid algorithms combining the completeness of systematic search with the speed of local search. This thesis explores hybrid (systematic + local search) algorithms which cooperate to solve DisCSPs. Three new hybrid approaches which combine both systematic and local search for Distributed Constraint Satisfaction are presented: (i) DisHyb; (ii) Multi-Hyb and; (iii) Multi-HDCS. These approaches use distributed local search to gather information about difficult variables and best values in the problem. Distributed systematic search is run with a variable and value ordering determined by the knowledge learnt through local search. Two implementations of each of the three approaches are presented: (i) using penalties as the distributed local search strategy and; (ii) using breakout as the distributed local search strategy. The three approaches are evaluated on several problem classes. The empirical evaluation shows these distributed hybrid approaches to significantly outperform both systematic and local search DisCSP algorithms. DisHyb, Multi-Hyb and Multi-HDCS are shown to substantially speed-up distributed problem solving with distributed systematic search taking less time to run by using the information learnt by distributed local search. As a consequence, larger problems can now be solved in a more practical timeframe
Adaptive Search and Constraint Optimisation in Engineering Design
The dissertation presents the investigation and development of novel adaptive
computational techniques that provide a high level of performance when searching
complex high-dimensional design spaces characterised by heavy non-linear constraint
requirements. The objective is to develop a set of adaptive search engines that will allow
the successful negotiation of such spaces to provide the design engineer with feasible high
performance solutions.
Constraint optimisation currently presents a major problem to the engineering designer and
many attempts to utilise adaptive search techniques whilst overcoming these problems are
in evidence. The most widely used method (which is also the most general) is to
incorporate the constraints in the objective function and then use methods for
unconstrained search. The engineer must develop and adjust an appropriate penalty
function. There is no general solution to this problem neither in classical numerical
optimisation nor in evolutionary computation. Some recent theoretical evidence suggests
that the problem can only be solved by incorporating a priori knowledge into the search
engine.
Therefore, it becomes obvious that there is a need to classify constrained optimisation
problems according to the degree of available or utilised knowledge and to develop search
techniques applicable at each stage. The contribution of this thesis is to provide such a
view of constrained optimisation, starting from problems that handle the constraints on the
representation level, going through problems that have explicitly defined constraints (i.e.,
an easily computed closed form like a solvable equation), and ending with heavily
constrained problems with implicitly defined constraints (incorporated into a single
simulation model). At each stage we develop applicable adaptive search techniques that
optimally exploit the degree of available a priori knowledge thus providing excellent
quality of results and high performance. The proposed techniques are tested using both well
known test beds and real world engineering design problems provided by industry.British Aerospace,
Rolls Royce and Associate
The Computational Complexity of Linear Optics
We give new evidence that quantum computers -- moreover, rudimentary quantum
computers built entirely out of linear-optical elements -- cannot be
efficiently simulated by classical computers. In particular, we define a model
of computation in which identical photons are generated, sent through a
linear-optical network, then nonadaptively measured to count the number of
photons in each mode. This model is not known or believed to be universal for
quantum computation, and indeed, we discuss the prospects for realizing the
model using current technology. On the other hand, we prove that the model is
able to solve sampling problems and search problems that are classically
intractable under plausible assumptions. Our first result says that, if there
exists a polynomial-time classical algorithm that samples from the same
probability distribution as a linear-optical network, then P^#P=BPP^NP, and
hence the polynomial hierarchy collapses to the third level. Unfortunately,
this result assumes an extremely accurate simulation. Our main result suggests
that even an approximate or noisy classical simulation would already imply a
collapse of the polynomial hierarchy. For this, we need two unproven
conjectures: the "Permanent-of-Gaussians Conjecture", which says that it is
#P-hard to approximate the permanent of a matrix A of independent N(0,1)
Gaussian entries, with high probability over A; and the "Permanent
Anti-Concentration Conjecture", which says that |Per(A)|>=sqrt(n!)/poly(n) with
high probability over A. We present evidence for these conjectures, both of
which seem interesting even apart from our application. This paper does not
assume knowledge of quantum optics. Indeed, part of its goal is to develop the
beautiful theory of noninteracting bosons underlying our model, and its
connection to the permanent function, in a self-contained way accessible to
theoretical computer scientists.Comment: 94 pages, 4 figure
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Improving probability selection based weights for satisfiability problems
Boolean Satisfiability problem (SAT) plays a prominent role in many domains of computer science and artificial intelligence due to its significant importance in both theory and applications. Algorithms for solving SAT problems can be categorized into two main classes: complete algorithms and incomplete algorithms (typically stochastic local search (SLS) algorithms). SLS algorithms are among the most effective for solving uniform random SAT problems, while hybrid algorithms achieved great breakthroughs for solving hard random SAT (HRS) problem recently. However, there is a lack of algorithms that can effectively solve both uniform random SAT and HRS problems. In this paper, a new SLS algorithm named SelectNTS is proposed aiming at solving both uniform random SAT and HRS problem effectively. SelectNTS is essentially an improved probability selection based local search algorithm, the core of which includes new clause and variable selection heuristics: a new clause weighting scheme and a biased random walk strategy are utilized to select a clause, while a new probability selection strategy with the variation of configuration checking strategy is used to select a variable. Extensive experimental results show that SelectNTS outperforms the state-of-the-art random SAT algorithms and hybrid algorithms in solving both uniform random SAT and HRS problems effectively
Coverage & cooperation: Completing complex tasks as quickly as possible using teams of robots
As the robotics industry grows and robots enter our homes and public spaces, they are increasingly expected to work in cooperation with each other. My thesis focuses on multirobot planning, specifically in the context of coverage robots, such as robotic lawnmowers and vacuum cleaners.
Two problems unique to multirobot teams are task allocation and search. I present a task allocation algorithm which balances the workload amongst all robots in the team with the objective of minimizing the overall mission time. I also present a search algorithm which robots can use to find lost teammates. It uses a probabilistic belief of a target robot’s position to create a planning tree and then searches by following the best path in the tree.
For robust multirobot coverage, I use both the task allocation and search algorithms. First the coverage region is divided into a set of small coverage tasks which minimize the number of turns the robots will need to take. These tasks are then allocated to individual robots. During the mission, robots replan with nearby robots to rebalance the workload and, once a robot has finished its tasks, it searches for teammates to help them finish their tasks faster
Use of behaviour change techniques in lifestyle change interventions for people with intellectual disabilities: A systematic review
Background:
People with intellectual disabilities (ID) experience more health problems and have different lifestyle change needs, compared with the general population.
Aims:
To improve lifestyle change interventions for people with ID, this review examined how behaviour change techniques (BCTs) were applied in interventions aimed at physical activity, nutrition or physical activity and nutrition, and described their quality.
Methods and procedures:
After a broad search and detailed selection process, 45 studies were included in the review. For coding BCTs, the CALO-RE taxonomy was used. To assess the quality of the interventions, the Physiotherapy Evidence Database (PEDro) scale was used. Extracted data included general study characteristics and intervention characteristics.
Outcomes and results:
All interventions used BCTs, although theory-driven BCTs were rarely used. The most frequently used BCTs were ‘provide information on consequences of behaviour in general’ and ‘plan social support/social change’. Most studies were of low quality and a theoretical framework was often missing.
Conclusion and implications:
This review shows that BCTs are frequently applied in lifestyle change interventions. To further improve effectiveness, these lifestyle change interventions could benefit from using a theoretical framework, a detailed intervention description and an appropriate and reliable intervention design which is tailored to people with ID
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Variable neighbourhood search based heuristic for K-harmonic means clustering
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Although there has been a rapid development of technology and increase of computation speeds, most of the real-world optimization problems still cannot be solved in a reasonable time. Some times it is impossible for them to be optimally solved, as there are many instances of real problems which cannot be addressed by computers at their present speed. In such cases, the heuristic approach can be used. Heuristic research has been used by many researchers to supply this need. It gives a sufficient solution in reasonable time. The clustering problem is one example of this, formed in many applications.
In this thesis, I suggest a Variable Neighbourhood Search (VNS) to improve a recent clustering local search called K-Harmonic Means (KHM).Many experiments are presented to show the strength of my code compared with some algorithms from the literature.
Some counter-examples are introduced to show that KHM may degenerate entirely, in either one or more runs. Furthermore, it degenerates and then stops in some familiar datasets, which significantly affects the final solution. Hence, I present a removing degeneracy code for KHM. I also apply VNS to improve the code of KHM after removing the evidence of degeneracy
Performance Analysis of Tree Seed Algorithm for Small Dimension Optimization Functions
Tree-Seed Algorithm (TSA) simulates the growth of trees and seeds on a land. TSA is a method proposed to solve continuous optimization problems. Trees and seeds indicate possible solutions in the search space for optimization problems. Trees are planted in the ground at the beginning of the search and each tree produces several seeds during iterations. While the trees were selected randomly during seed formation, the tournament selection method was used and also hybridized by adding the C parameter, which is the acceleration coefficient calculated according to the size of the problem. In this study, continuous optimization problem has been solved by the hybrid method. First, the performance analyses of the five best known numerical benchmark functions have been done, in both TSA and hybrid method TSA with 2, 3, 4 and 5 dimensions, and 10-50 population numbers. After that, well-known algorithms in the literature like Particle Swarm Optimization (PSO), TSA, Artificial Bee Colony (ABC), Harmony Search (HS), as well as hybrid method TSA (HTSA) have been applied to twenty-four numerical benchmark functions and the performance analyses of algorithms have been done. Hopeful and comparable conclusions based on solution quality and robustness can be obtained with the hybrid method
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