Dynamic multi-swarm particle swarm optimizer with harmony search

In this paper, the dynamic multi-swarm particle swarm optimizer (DMS-PSO) is improved by hybridizing it with the harmony search (HS) algorithm and the resulting algorithm is abbreviated as DMS-PSO-HS. We present a novel approach to merge the HS algorithm into each sub-swarm of the DMS-PSO. Combining the exploration capabilities of the DMS-PSO and the stochastic exploitation of the HS, the DMS-PSO-HS is developed. The whole DMS-PSO population is divided into a large number of small and dynamic sub-swarms which are also individual HS populations. These sub-swarms are regrouped frequently and information is exchanged among the particles in the whole swarm. The DMS-PSO-HS demonstrates improved on multimodal and composition test problems when compared with the DMS-PSO and the HS.     

Using Dihedral Angles for Edge Extraction in Range Data

The volume of raw range image data that is required to represent just a single scene can be extensive; hence direct interpretation of range images can incur a very high computational cost. Range image feature extraction has been identified as a mechanism to produce a more compact scene representation, in particular using features such as edges and surfaces, and hence enables less costly scene interpretation for applications such as object recognition and robot navigation. We present an approach to edge detection in range images that can be used directly with any range data, regardless of whether the data have regular or irregular spatial distribution. The approach is evaluated with respect to accuracy of both edge location and visual results are also provided.     

Risk minimization in biometric sensor networks: an evolutionary multi-objective optimization approach

Biometric systems aim at identifying humans by their characteristics or traits. This article addresses the problem of designing a biometric sensor management unit by optimizing the risk, which is modeled as a multi-objective optimization (MO) problem with global false acceptance rate and global false rejection rate as the two objectives. In practice, when multiple biometric sensors are used, the decision is taken locally at each sensor and the data are passed to the sensor manager. At the sensor manager, the data are fused using a fusion rule and the final decision is taken. The optimization process involves designing the data fusion rule and setting of the sensor thresholds. In this work, we employ a fuzzy dominance and decomposition-based multi-objective evolutionary algorithm (MOEA) called MOEA/DFD and compare its performance with two state-of-the-art MO algorithms: MOEA/D and NSGA-II in context to the risk minimization task. The algorithm introduces a fuzzy Pareto dominance concept to compare two solutions and uses the scalar decomposition method only when one of the solutions fails to dominate the other in terms of a fuzzy dominance level. The MO algorithms are simulated on different number of sensor setups consisting of three, six, and eight sensors. The a priori probability of imposter is also varied from 0.1 to 0.9 to verify the performance of the system with varying degrees of threat. One of the most significant advantages of using the MO framework is that with a single run, just by changing the decision-making logic applied to the obtained Pareto front, one can find the required threshold and decision strategies for varying threats of imposter. However, with single-objective optimization, one needs to run the algorithms each time with change in the threat of imposter. Thus, multi-objective formulation of the problem appears to be more useful and better than the single-objective one. In all the test instances, MOEA/DFD performs better than all the other algorithms.     

Specification-based Testing for Gui-based Applications

The development of GUI-based applications has raised a lot of new issues, one of them being how to automate effective testing for applications with complicated graphical user interactions. In this paper, we discuss the architectural issues and the implementation concerns of our approach to an automated specification-based testing technique for GUI-based applications. This approach is carried out by enriching existing architecture for automated specification-based testing. An essential part of our work is a visual environment to obtain test specifications. This environment pre-runs the Application Under Test (AUT) under its own control, with two prominent characteristics: First, testers can edit test specifications within the true GUI environment of the AUT. Second, the recorded input and output contain the same references as those in the AUT, so that the test cases generated from the edited specification can be used directly by test oracles during the automated testing procedure.We present our running prototype of a visual specification editor that allows users to graphically manipulate test specifications when these specifications are given in term of Finite State Machines (FSM) and the implementations of the AUT are GUI-based Java applications.     

Effective heuristics for the no-wait flow shop scheduling problem with total flow time minimization

The no-wait flow shop scheduling problem with total flow time criterion has important applications in industrial systems. Heuristics that explore specific characteristics of the problem are essential to find good solutions in limited computational time for many practical applications. This paper first presents two constructive heuristics, namely improved standard deviation heuristic (ISDH) and improved Bertolissi heuristic (IBH), by combining the standard deviation heuristic (Gao et al., Int J Adv Manf Technol 56:683–692, 2011) and Bertolliso heuristic (Bertolissi, J Mater Process Technol 107:459–465, 2000) with the procedure of the constructive heuristic of Laha (Int J Adv Manf Technol 41:97–109, 2009). Then, four composite heuristics, i.e., ISDH with local search, IBH with local search, ISDH with iteration, and IBH with iteration, are separately proposed using the insertion-based local search method and iteration operator to improve the solutions of the ISDH and IBH. Extensive computational experiments are carried out based on a set of well-known flow shop benchmark instances that are considered as no-wait flow shop instances. Computational results and comparisons show that the proposed composite heuristics perform significantly better than the existing ones, and the proposed composite heuristics further improve the presented constructive heuristics for the no-wait flow shop scheduling problem with total flow time criterion.