Perturbation strength and the global structure of qap fitness landscapes

We study the effect of increasing the perturbation strength on the global structure of QAP fitness landscapes induced by Iterated Local Search (ILS). The global structure is captured with Local Optima Networks. Our analysis concentrates on the number, characteristics and distribution of funnels in the landscape, and how they change with increasing perturbation strengths. Well-known QAP instance types are considered. Our results confirm the multi-funnel structure of QAP fitness landscapes and clearly explain, visually and quantitatively, why ILS with large perturbation strengths produces better results. Moreover, we found striking differences between randomly generated and real-world instances, which warns about using synthetic benchmarks for (manual or automatic) algorithm design and tuning

The challenges facing public libraries in the Big Society: The role of volunteers, and the issues that surround their use in England

The use of volunteers in English public libraries is nothing new, however their use is becoming ever greater and one may argue that we are increasingly seeing a mixed economy of public library provision, in the wider arena of the Big Society. This paper presents the findings of a Delphi Study of 15 library managers undertaken as part of a Professional Doctorate exploring the challenges facing public libraries in England today, particularly focusing on volunteer use. An overview of relevant supporting literature is provided to help contextualize the research, particularly focusing on concepts such as the political background surrounding policy development, community engagement, the Big Society, and volunteering. Explanation of how the Delphi Study was conducted is given, together with a discussion of the key findings. Results show that opinions of library managers cover a broad spectrum. Although volunteer use is generally viewed by the respondents as a good thing, with potential to further enhance a service and aid community engagement, there are also a number of concerns. These concerns particularly relate to the idea of the volunteer as a replacement to paid staff rather than an enhancement to the service. Other key concerns relate to the quality of service provision, the rationale behind volunteer use, and the capacity of communities to deliver. Volunteer use in public libraries on this scale is a new phenomenon, and the longevity of such a development is largely unknown. This raises the question as to whether this is simply a large scale ideological experiment, or a move to even greater community engagement

Improving a branch-and-bound approach for the degree-constrained minimum spanning tree problem with LKH

The degree-constrained minimum spanning tree problem, which involves finding a minimum spanning tree of a given graph with upper bounds on the vertex degrees, has found multiple applications in several domains. In this paper, we propose a novel CP approach to tackle this problem where we extend a recent branch-and-bound approach with an adaptation of the LKH local search heuristic to deal with trees instead of tours. Every time a solution is found, it is locally optimised by our new heuristic, thus yielding a tightened cut. Our experimental evaluation shows that this significantly speeds up the branch-and-bound search and hence closes the performance gap to the state-of-the-art bottom-up CP approach

Future librarians digitising the past

Library studies students at the University of Strathclyde are working alongside public library services on digitisation projects. Paul F. Burton and David McMenemy describe how these 'real-world' practical ICT projects are incorporated into the curriculum, and how they are working successfully

A novel encoding for separable large-scale multi-objective problems and its application to the optimisation of housing stock improvements

Large-scale optimisation problems, having thousands of decision variables, are difficult as they have vast search spaces and the objectives lack sensitivity to each decision variable. Metaheuristics work well for large-scale single-objective optimisation, but there has been little work for large-scale, multi-objective optimisation. We show that, for the special case problem where the objectives are each additively-separable in isolation and share the same separability, the problem is not separable when considering the objectives together. We define a problem with this property: optimisation of housing stock improvements, which seeks to distribute limited public investment to achieve the optimal reduction in the housing stock's energy demand. We then present a two-stage approach to encoding solutions for additively-separable, large-scale, multi-objective problems called Sequential Pareto Optimisation (SPO), which reformulates the global problem into a search over Pareto-optimal solutions for each sub-problem. SPO encoding is demonstrated for two popular MOEAs (NSGA-II and MOEA/D), and their relative performance is systematically analysed and explained using synthetic benchmark problems. We also show that reallocating seed solutions to the most appropriate sub-problems substantially improves the performance of MOEA/D, but overall NSGA-II still performs best. SPO outperforms a naive single-stage approach, in terms of the optimality of the solutions and the computational load, using both algorithms. SPO is then applied to a real-world housing stock optimisation problem with 4424 binary variables. SPO finds solutions that save 20% of the cost of seed solutions yet obtain the same reduction in energy consumption. We also show how application of different intervention types vary along the Pareto front as cost increases but energy use decreases; e.g., solid wall insulation replacing cavity wall insulation, and condensing boilers giving way to heat pumps. We conclude with proposals for how this approach may be extended to non-separable and many-objective problems