Abstract It is well known that the performance of a stochastic local search proceduredepends upon the setting of its noise parameter, and that the optimal setting varies with the problem distribution. It is therefore desirable to develop general prinici-ples for tuning the procedures. We present two statistical measures of the local search process that allow one to quickly find the optimal noise settings. Theseproperties are independent of the fine details of the local search strategies, and appear to be relatively independent of the structure of the problem domains. Weapplied these principles to the problem of evaluating new search heuristics, and discovered two promising new strategies.

The Guided Local Search method has been successfully applied to a number of hard combinatorial optimisation problems from the well-known TSP and QAP to real world problems such as Frequency Assignment and Workforce Scheduling. In this paper, we are demonstrating that the potential applications of GLS are not limited to optimisation problems of discrete nature but also to difficult continuous optimisation problems. Continuous optimisation problems arise in many engineering disciplines (such as electrical and mechanical engineering) in the context of analysis, design or simulation tasks. The problem examined gives an illustrative example of the behaviour of GLS, providing insights on the mechanisms of the algorithm. 1.

A common weakness of local search metaheuristics, such as Simulated Annealing, in solving combinatorial optimisation problems, is the necessity of setting a certain number of parameters. This tends to make significantly increase the total amount of time required to solve the problem and often requires a high level of experience from the user. This paper is motivated by the goal of overcoming this drawback by employing "parameter-free" techniques in the context of automatically solving course timetabling problems.

Meta-heuristics support managers in decision-making with robust tools that provide high-quality solutions to important applications in business, engineering, economics and science in reasonable time horizons. In this paper we give some insight into the state of the art of meta-heuristics. This primarily focuses on the significant progress which general frames within the meta-heuristics field have implied for solving combinatorial optimization problems, mainly those for planning and scheduling.

This paper formulates the shelf space allocation problem as a non-linear function of the product net profit and store-inventory. We show that this model is an extension of multi-knapsack problem, which is itself an NP-hard problem. A two-stage relaxation is carried out to get an upper bound of the model. A simulated annealing based hyper-heuristic algorithm is proposed to solve several problem instances with different problem sizes and space ratios. The results show that the simulated annealing hyper-heuristic significantly outperforms two conventional simulated annealing algorithms and other hyper-heuristics for all problem instances. The experimental results show that our approach is a robust and efficient approach for the shelf space allocation problem. hyper-heuristics, simulated annealing, shelf space allocation, planograms 1.

Abstract: Many engineering tasks involve the search for good solutions among many possibilities. In most cases, tasks are too complex to be modeled completely and their solution spaces often contain local minima. Therefore, classical optimization techniques cannot, in general, be applied effectively. This paper studies two stochastic search methods, one well-established �simulated annealing � and one recently developed �probabilistic global search Lausanne�, applied to structural shape control. Search results are applied to control the quasistatic displacement of a tensegrity structure with multiple objectives and interdependent actuator effects. The best method depends on the accuracy related to requirements defined by the objective function and the maximum number of evaluations that are allowed.

Metaheuristics have received considerable interest these recent years in the field of combinatorial optimization. However, the choice of a particular algorithm to optimize a certain problem is still mainly driven by some sort of devotion of its author to a certain technique rather than by a rationalistic choice driven by reason. Hybrid algorithms have shown their ability to provide local optima of high quality. Hybridization of algorithms is still in its infancy: certain combinations of algorithms have experimentally shown their performance, though the reasons of their success is not always really clear. In order to add some rational to these issues, we study the structure of search spaces and attempt to relate it to the performance of algorithms. We wish to explain the behavior of search algorithms with this knowledge and provide guidelines in the design of hybrid algorithms. This paper briefly reviews the current knowledge we have on search spaces of combinatorial optimization problems. Then, we discuss hybridization and present a general classification of the way hybridization can be conducted in the light of our knowledge of the structure of search spaces.

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We consider the problem of designing ring networks for metropolitan area networks. Given a number of nodes representing locations that may be connected, the task is to construct a ring network by selecting a node subset and corresponding direct links. Any two nodes on the ring are enabled to communicate with each other so that the network provider gains a certain revenue. On the other hand, construction costs are incurred for the design of each direct link. The basic objective is to maximize the sum of all revenues minus the construction costs while building a ring network. We discuss certain relationships to other problems. Mathematical models are presented and used to obtain optimal solutions for small problem instances and upper bounds. We focus on the application of modern heuristic search concepts by means of a framework with generic components for heuristic search which enables the efficient adaptation to real world problems.

Location determination of mobile users within a building has attracted much attention lately due to its many applications in mobile networking including network intrusion detection problems. However, it is challenging due to the complexities of the indoor radio propagation characteristics exacerbated by the mobility of the user. A common practice is to mechanically generate a table showing the radio signal strength at different known locations in the building. A mobile user’s location at an arbitrary point in the building is determined by measuring the signal strength at the location in question and determining the location by referring to the above table using a LMSE (least mean square error) criterion. Obviously, this is a very tedious and time consuming task. This paper proposes a novel and automated location determination method called ARIADNE. Using a two dimensional construction floor plan and only a single actual signal strength measurement, ARIADNE generates an estimated signal strength map comparable to those generated manually by actual measurements. Given the signal measurements for a mobile, a proposed clustering algorithm searches that signal strength map to determine the current mobile’s location. The results from ARIADNE are comparable and may even be superior to those from existing localization schemes.