Ant colony optimization with partial order reduction for discovering safety property violations in concurrent models

In this article we analyze the combination of ACOhg, a new metaheuristic algorithm, plus partial order reduction applied to the problem of finding safety property violations in concurrent models using a model checking approach. ACOhg is a new kind of ant colony optimization algorithm inspired by the foraging behavior of real ants equipped with internal resorts to search in very large search landscapes. We here apply ACOhg to concurrent models in scenarios located near the edge of the existing knowledge in detecting property violations. The results state that the combination is computationally beneficial for the search and represents a considerable step forward in this field with respect to exact and other heuristic techniques.     

A new modeling and solution approach for the set-partitioning problem

The set-partitioning problem (SPP) is widely known for both its application potential and its computational challenge. This NP-hard problem is known to pose considerable difficulty for classical solution methods such as those based on LP technologies. In recent years, the unconstrained binary quadratic program has proven to perform well as a unified modeling and solution framework for a variety of IP problems. In this paper we illustrate how this unified framework can be applied to SPP. Computational experience is presented, illustrating the attractiveness of the approach.     

A systematic mapping study of search-based software engineering for software product lines

ContextSearch-Based Software Engineering (SBSE) is an emerging discipline that focuses on the application of search-based optimization techniques to software engineering problems. Software Product Lines (SPLs) are families of related software systems whose members are distinguished by the set of features each one provides. SPL development practices have proven benefits such as improved software reuse, better customization, and faster time to market. A typical SPL usually involves a large number of systems and features, a fact that makes them attractive for the application of SBSE techniques which are able to tackle problems that involve large search spaces.ObjectiveThe main objective of our work is to identify the quantity and the type of research on the application of SBSE techniques to SPL problems. More concretely, the SBSE techniques that have been used and at what stage of the SPL life cycle, the type of case studies employed and their empirical analysis, and the fora where the research has been published.MethodA systematic mapping study was conducted with five research questions and assessed 77 publications from 2001, when the term SBSE was coined, until 2014.ResultsThe most common application of SBSE techniques found was testing followed by product configuration, with genetic algorithms and multi-objective evolutionary algorithms being the two most commonly used techniques. Our study identified the need to improve the robustness of the empirical evaluation of existing research, a lack of extensive and robust tool support, and multiple avenues worthy of further investigation.ConclusionsOur study attested the great synergy existing between both fields, corroborated the increasing and ongoing interest in research on the subject, and revealed challenging open research questions.     

Maximizing cover probability by using multivariate normal distributions

In this paper we consider the problem of covering the most probability of a multivariate normal distribution by selection of a given number of hypercubes of a given size. The problem is formulated and metaheuristic procedures proposed for its solution.This complete issue was revised and published online in November 2004. The previous version contained a false date. Correspondence to: Zvi DreznerThis research was supported, in part, by the Natural Sciences and Engineering Research Council of Canada.     

A priori reformulations for joint rolling-horizon scheduling of materials processing and lot-sizing problem

In many production processes, a key material is prepared and then transformed into different final products. The lot sizing decisions concern not only the production of final products, but also that of material preparation in order to take account of their sequence-dependent setup costs and times. The amount of research in recent years indicates the relevance of this problem in various industrial settings. In this paper, facility location reformulation and strengthening constraints are newly applied to a previous lot-sizing model in order to improve solution quality and computing time. Three alternative metaheuristics are used to fix the setup variables, resulting in much improved performance over previous research, especially regarding the use of the metaheuristics for larger instances.     

An effective hybrid algorithm for university course timetabling

The university course timetabling problem is an optimisation problem in which a set of events has to be scheduled in timeslots and located in suitable rooms. Recently, a set of benchmark instances was introduced and used for an ‘International Timetabling Competition’ to which 24 algorithms were submitted by various research groups active in the field of timetabling. We describe and analyse a hybrid metaheuristic algorithm which was developed under the very same rules and deadlines imposed by the competition and outperformed the official winner. It combines various construction heuristics, tabu search, variable neighbourhood descent and simulated annealing. Due to the complexity of developing hybrid metaheuristics, we strongly relied on an experimental methodology for configuring the algorithms as well as for choosing proper parameter settings. In particular, we used racing procedures that allow an automatic or semi-automatic configuration of algorithms with a good save in time. Our successful example shows that the systematic design of hybrid algorithms through an experimental methodology leads to high performing algorithms for hard combinatorial optimisation problems.     

Asymptotic convergence of metaheuristics for multiobjective optimization problems

This paper analyzes the convergence of metaheuristics used for multiobjective optimization problems in which the transition probabilities use a uniform mutation rule. We prove that these algorithms converge only if elitism is used.     

Environmental impact of rock excavation in urban areas: comparison between blasting and hydraulic breaker hammer

This paper compares the two most used rock excavation techniques in urban areas: blasting with explosives and using breaker hammers. From the follow-up of several works, data has been obtained that allow us to compare both techniques and their effects over the surrounding environment, mainly the noise production and ground vibrations. Results show that although the use of explosives is not popular due to security reasons, it allows faster excavations, is safer for the surrounding structures and has less environmental impact than the breaker hammers. However, we will also show a particular case where the complementary use of both techniques is successful.     

Multipoint combinatorial optimization method with a search strategy in higher structure solution space

We propose a multipoint combinatorial optimization method that aims to realize efficient search in consideration of intensification and diversification in higher structure solution space by sharing information between search points. We evaluated the performance of the proposed method through numerical experiments using benchmark problems.     

Integrating a multi-objective optimization framework into a structural design software

In this paper, we present a tool combining two software applications aimed at optimizing structural design problems of the civil engineering domain. Our approach lies in integrating an application for designing 2D and 3D bar structures, called Ebes, with the jMetal multi-objective optimization framework. The result is a software package that helps civil engineers to create bar structures which can be optimized further with multi-objective metaheuristics according to different goals, such as minimizing the structure weight and minimizing the deformation. The main features of both Ebes and jMetal are described and how they are combined together in one single tool is explained. Finally a case study to illustrate how the application works is presented.