Stefanie Niklander

Online control of enumeration strategies via bat algorithm and black hole optimization

Constraint programming is an efficient and powerful paradigm for solving constraint satisfaction and optimization problems. Under this paradigm, problems are modeled as a sequence of variables and a set of constraints. The variables have a non-empty domain of candidate values and constraints restrict the values that variables can adopt. The solving process operates by assigning values to variables in order to produce potential solutions which are then evaluated. A main component in this process is the enumeration strategy, which decides the order in which variables and values are chosen to produce such potential solutions. There exist different ways to perform this selection, and depending on the quality of this decision, the efficiency of the solving process may dramatically vary. Unfortunately, selecting the proper strategy is known to be a hard task, as its behavior during search is generally unpredictable and certainly depends on the problem at hand. A recent trend to handle this concern, is to interleave a set of different strategies instead of using a single one during the whole process. The strategies are evaluated according to process indicators in order to use the most promising one on each part of the search process. This process is known as online control of enumeration strategies and its correct configuration can be seen itself as an optimization problem. In this paper, we present two new systems for online control of enumeration strategies based on recent nature-inspired metaheuristics: bat algorithm and black hole optimization. The bat algorithm mimics the location capabilities of bats that employ echoes to identify the objects in their surrounding areas, while black hole optimization inspires its behavior on the gravitational pull of black holes in space. We perform different experimental results by using different enumeration strategies and well-known benchmarks, where the proposed approaches are able to noticeably outperform previous work on online control.

A matheuristic approach combining local search and mathematical programming

A novel matheuristic approach is presented and tested on a well-known optimisation problem, namely, capacitated facility location problem (CFLP). The algorithm combines local search and mathematical programming. While the local search algorithm is used to select a subset of promising facilities, mathematical programming strategies are used to solve the subproblem to optimality. Proposed local search is influenced by instance-specific information such as installation cost and the distance between customers and facilities. The algorithm is tested on large instances of the CFLP, where neither local search nor mathematical programming is able to find good quality solutions within acceptable computational times. Our approach is shown to be a very competitive alternative to solve large-scale instances for the CFLP.

Autonomous Search in Constraint Satisfaction via Black Hole: A Performance Evaluation Using Different Choice Functions

Autonomous Search is a modern technique aimed at introducing self-adjusting features to problem-solvers. In the context of constraint satisfaction, the idea is to let the solver engine to autonomously replace its solving strategies by more promising ones when poor performances are identified. The replacement is controlled by a choice function, which takes decisions based on information collected during solving time. However, the design of choice functions can be done in very different ways, leading of course to very different resolution processes. In this paper, we present a performance evaluation of 16 rigorously designed choice functions. Our goal is to provide new and interesting knowledge about the behavior of such functions in autonomous search architectures. To this end, we employ a set of well-known benchmarks that share general features that may be present on most constraint satisfaction and optimization problems. We believe this information will be useful in order to design better autonomous search systems for constraint satisfaction.

Emotional Computing and Discourse Analysis: A Case Study About Brexit in Twitter.

During the last years, emotional computing has emerged as a field of Human Computer Interaction, where algorithms are able to recognize emotions in order to take better decisions in a given context. However correctly recognizing emotions is known to be a difficult task, specially in social networks which is plenty of stereotypes, metaphors, ironies and multi-word expressions that make the process hard to succeed. In this paper, we propose to pre-process the data by using emotional computing algorithms to then employ discourse analysis for the study of the information viralyzed through social networks. We provide interesting results using as case study the Brexit.

Resolving the Manufacturing Cell Design Problem Using the Flower Pollination Algorithm

The Manufacturing cell design problem focuses on the creation of an optimal distribution of the machinery on a productive plant, through the creation of highly independent cells where the parts of certain products are processed. The main objective is to reduce the movements between this cells, decreasing production times, costs and getting other advantages. To find solutions to this problem, in this paper, the usage of the Flower Pollination Algorithm is proposed, which is one of the many nature-based algorithms, which in this case is inspired in the Pollination of the flowers, and has shown great capacities in the resolution of complex problems. Experimental results are shown, with 90 instances taken from Boctor’s experiments, where the optimum is achieved in all them.

The Impact of Using Different Choice Functions When Solving CSPs with Autonomous Search

Constraint programming is a powerful technology for the efficient solving of optimization and constraint satisfaction problems (CSPs). A main concern of this technology is that the efficient problem resolution usually relies on the employed solving strategy. Unfortunately, selecting the proper one is known to be complex as the behavior of strategies is commonly unpredictable. Recently, Autonomous Search appeared as a new technique to tackle this concern. The idea is to let the solver adapt its strategy during solving time in order to improve performance. This task is controlled by a choice function which decides, based on performance information, how the strategy must be updated. However, choice functions can be constructed in several manners variating the information used to take decisions. Such variations may certainly conduct to very different resolution processes. In this paper, we study the impact on the solving phase of 16 different carefully constructed choice functions. We employ as test bed a set of well-known benchmarks that collect general features present on most CSPs. Interesting experimental results are obtained in order to provide the best-performing choice functions for solving CSPs.

Evaluation of choice functions to self-adaptive on constraint programming via the black hole algorithm

In operation research and optimization area, Autonomous Search is a technique that provides the solver the auto-adaptive capability, during search process. This technique aims to improve performance in the exploration of search tree, updating the enumeration strategy online. This task is controlled by a choice function (CF) which decides, based on performance indicators given from the solver, how the strategy must be updated. The relevance of indicators is handled via back hole algorithm, inspired on natural phenomenon that occurs in outer space. If choice function exhibits a poor performance, the strategy is replacement and solver continue exploring the search tree under new enumeration strategy. In this paper, we present an evaluation of the impact and efficient using 16 different carefully constructed choice functions. We employ as test bed a set of well-known constrain satisfaction problems. Encouraging experimental results are obtained in order to show which using choice functions is highly efficient, if want to control the search process, online way.

Facilitating Analysis of Mass Media Influence Through Content Analysis and Emotional Computing

During the last years, different HCI applications have successfully employed sentimental, emotional, and affective computing algorithms for solving various recognition, interpretation and simulations tasks related to the study of human affects. In this paper, we combine content and sentimental analysis to facilitate the understanding of how mass media may influence and/or control a given information context. We employ as case study the army and police corruption information. We analyze the speeches constructed by the press and the comments that users post on the mass medias web sites. Interesting results are obtained where all topics that readers visibilize and/or invisibilize when constructing their representations about the study cases are precisely detected.

Combining Sentimental and Content Analysis for Recognizing and Interpreting Human Affects

During the last years, sentimental computing has gained special attention as the improvements achieved related to human affects, which are required abilities for many HCI applications. Particularly, sentimental analysis has successfully been used on social networks to extract useful information for different purposes. However the task remain difficult due to the several complex requirements that the correct human affect analysis implies. In this paper we propose a combination of sentimental and content analysis for the recognition and interpretation of human affects. We provide interesting results using as case study the #NiUnaMenos (Not One Less) social movement, which demands for an end to femicide and violence against women.

Solving Manufacturing Cell Design Problems Using the Black Hole Algorithm.

In this paper we solve the Manufacturing Cell Design Problem. This problem considers the grouping of different machines into sets or cells with the objective of minimizing the movement of material. To solve this problem we use the Black Hole algorithm, a modern population-based metaheuristic that is inspired by the phenomenon of the same name. At each iteration of the search, the best candidate solution is selected to be the black hole and other candidate solutions, known as stars, are attracted by the black hole. If one of these stars get too close to the black hole it disappears, generating a new random star (solution). Our approach has been tested by using a well-known set of benchmark instances, reaching optimal values in all of them.