Due to serving several purposes simultaneously, running scientific workflows on dynamic environments such as cloud computing, has become multi-objective scheduling. Among these purposes, Cost and Makespan are probably the most two primitive objectives. Another critical factor in a large-scale scientific workflow is tremendous amount of data during execution. Therefore, this work also includes Data Movement as an additional objective as it has a major impact on network utilization and energy consumption in network equipment in cloud data center. In considering these three objectives, this work proposes a framework for scheduling solutions which combines a new nodes clustering technique in Directed Acyclic Graph (DAG) model known as Multilevel Dependent Node Clustering (MDNC) and the multi-objective optimization, Extreme Nondominated Sorting Genetic Algorithm-III (E-NSGA-III). E-NSGA-III is the recent extension of Nondominated Sorting Genetic Algorithm (NSGA-III). Five well-known scientific workflows, CyberShake, Epigenomics, LIGO, Montage, and SIPHT are selected as testbeds, while the commonly known Hypervolume is chosen as the performance metric. In this work, MDNC is also experimented with both NSGA-III. Comparison among three approaches, E-NAGA-III alone, E-NAGA-III with Peer-to-Peer clustering and E-NAGA-III with MDNC are carried out. The superiority of the proposed framework among them and its limitation are discussed.

In a round-trip carsharing system, stations must be located in such a way that allow for maximum user coverage with the least walking distance as well as offer certain degrees of flexibility for returning. Therefore, a balance must be stricken between these factors. Providing a satisfactory system can be translated into an optimization problem and belongs to an NP-hard class. In this article, a novel optimization model for the round-trip carsharing fleet placement problem, called Fleet Placement Problem (FPP), is proposed. The optimization in this work is multiobjective and its NP-hard nature is proven. Three different optimization algorithms: PolySCIP (exact method), heuristics, and NSGA-II (metaheuristic) are investigated. This work adopts three real instances for the study, instead of their abstracts where they are most commonly used. They are two instance:, in the city of Luxembourg (smaller and larger) and a much larger instance in the city of Munich. Results from each algorithm are validated and compared with solution from human experts. Superiority of the proposed FPP model over the traditional methods is also demonstrated.

Time Series Classification is one of the areas in data mining which receives some attention recently. Control Chart Patterns (CCPs) can be considered as time series. Monitoring and recognition of CCPs is also an importance process in manufacturing. This implies that ability to classify CCPs with high accuracy is essential. This study attempts to implement CCPs classifiers which are capable of dealing with CCPs with different level of noise. Extracting image processing statistical features is adopted as preprocessing technique. The work also investigates the effect of level of noise in classification. Three different types of techniques for implementing classifiers are selected, these are Decision Tree, Neural network and an evolutionary based program, known as Self-adjusting Association Rules Generator (SARG). It was found that SARG yielded the best performance among them. To date, this study is an attempt to classify particular model of CCPs with highest level of noise.

Graph clustering has received considerable attention recently, and its applications are numerous, ranging from the detection of social communities to the clustering of computer networks. It is classified as an NP-class problem, and several algorithms have been proposed with specific objectives. There also exist various quality metrics for evaluating them. Having clusters with the required density can be beneficial because it permits the effective deployment of resources. This study proposes an approach to partitioning and overlapping clustering of undirected unweighted graphs, allowing users to specify the required density of resultant clusters. This required density is achieved by means of ‘Relative Density’. The proposed algorithm adopts an edge-based approach, commencing with the determination of the edge degree for each edge. The main clustering process is then initiated by an edge with an average degree. A cluster is expanded by considering adjacent edges that can be included while monitoring the relative density of the cluster. Eight empirical networks with diverse characteristics are used to validate the proposed algorithm for both partitioning and overlapping clustering. Their results are assessed using an appropriate metric known as the mean relative density deviation coefficient (MRDDC). This is the first work that attempts to carry out partitioning and overlapping graph clustering, which allows user-specified density.

The problem of generating efficient association rules can seen as search problem since many different sets of rules are possible from a given set of instances. As the application of evolutionary computation in searching is well studied, it is possible to utilize evolutionary computation in mining for efficient association rules. In this paper, a program known as self-adjusting associative rules generator (SARG) is described. SARG is a data mining program which can generate associative rules for classification. It is an improvement of the data mining program called genetic programming for inductive learning (GPIL). Both utilize evolutionary computation in inductive learning. The shortcoming of GPIL lies in the operations crossover and selection. These two operations were inflexible and not able to adjust themselves in order to select suitable methods for the task at hand. SARG introduces new method of crossover known as MaxToMin crossover together with a self-adjusting reproduction. It has been tested on several benchmark data sets available in the public domain. Comparison between GPIL and SARG revealed that SARG achieved better performance and was able to classify these data sets with higher accuracy. The paper also discusses relevant aspects of SARG and suggests directions for future work