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It is now widely accepted that ontologies play a critical role in achieving the goal of machine understandable web, also known as semantic web. In order to develop ontologies, several methodologies have been proposed during the last two decades. Despite the fact, that quite a number of ontology engineering methodologies have been proposed, still the field lacks widely accepted and mature methodologies. Most methodologies lack sufficient details of techniques and activities employed in them. However, some methodologies provide sufficient details including METHONTOLOGY. This article discusses and reports a critical analysis and comparison of these methodologies. The analysis is performed based on a criterion, derived from related literature, trends and needs which evolved over the years. The results of the analysis showed that there is no completely mature methodology. Therefore, this research may act as a preliminary guide to come with a state of art ontology engineering methodology, bridging up the existing gaps and shortfalls.

Until now, an effective defense method against Distributed Denial of Service (DDoS) attacks is yet to be offered by security systems. Incidents of serious damage due to DDoS attacks have been increasing, thereby leading to an urgent need for new attack identification, mitigation, and prevention mechanisms. To prevent DDoS attacks, the basic features of the attacks need to be dynamically analyzed because their patterns, ports, and protocols or operation mechanisms are rapidly changed and manipulated. Most of the proposed DDoS defense methods have different types of drawbacks and limitations. Some of these methods have signature-based defense mechanisms that fail to identify new attacks and others have anomaly-based defense mechanisms that are limited to specific types of DDoS attacks and yet to be applied in open environments. Subsequently, extensive research on applying artificial intelligence and statistical techniques in the defense methods has been conducted in order to identify, mitigate, and prevent these attacks. However, the most appropriate and effective defense features, mechanisms, techniques, and methods for handling such attacks remain to be an open question. This review paper focuses on the most common defense methods against DDoS attacks that adopt artificial intelligence and statistical approaches. Additionally, the review classifies and illustrates the attack types, the testing properties, the evaluation methods and the testing datasets that are utilized in the methodology of the proposed defense methods. Finally, this review provides a guideline and possible points of encampments for developing improved solution models of defense methods against DDoS attacks. INDEX TERMS DDoS attack, DDoS defense, artificial intelligence technique, statistical technique.

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Norms and normative multiagent systems have become the subjects of interest for many researchers. Such interest is caused by the need for agents to exploit the norms in enhancing their performance in a community. The term norm is used to characterize the behaviours of community members. The concept of normative multiagent systems is used to facilitate collaboration and coordination among social groups of agents. Many researches have been conducted on norms that investigate the fundamental concepts, definitions, classification, and types of norms and normative multiagent systems including normative architectures and normative processes. However, very few researches have been found to comprehensively study and analyze the literature in advancing the current state of norms and normative multiagent systems. Consequently, this paper attempts to present the current state of research on norms and normative multiagent systems and propose a norm's life cycle model based on the review of the literature. Subsequently, this paper highlights the significant areas for future work.

Abstract-Genetic algorithms (GA) are stimulated by population genetics and evolution at the population level where crossover and mutation comes from random variables. The problems of slow and premature convergence to suboptimal solution remain an existing struggle that GA is facing. Due to lower diversity in a population, it becomes challenging to locally exploit the solutions. In order to resolve these issues, the focus is now on reaching equilibrium between the explorative and exploitative features of GA. Therefore, the search process can be prompted to produce suitable GA solutions. This paper begins with an introduction, Section 2 describes the GA exploration and exploitation strategies to locate the optimum solutions. Section 3 and 4 present the lists of some prevalent mutation and crossover operators. This paper concludes that the key issue in developing a GA is to deliver a balance between explorative and exploitative features that complies with the combination of operators in order to produce exceptional performance as a GA as a whole.Index Terms-Crossover operator, mutation operator, exploitation, exploration. I. INTRODUCTIONThe main search operator in Genetic algorithms (GA) is the crossover operator which equally as significant as mutation, selection and coding in GA. The crossover operator functions primarily in the survey of information that is accessible through the search space, which inadvertently improves the behavior of the GA. On another note, mutation is a secondary operator. It functions to alter the genes of the offspring. A mutator will diversify the existing population and this inadvertently allows GAs to exploit promising areas of the search space thus avoiding local solutions [1]. Some of the mutation operators are designed to explicitly overcome certain types of issues over others [2]. The performance among all the comparative of GA operators are easily validated and compared through unbiased test problems from the literature, which are diverse in properties in terms of complexity and modality. This study substantially contributes in reviewing some prevalent mutation and crossover operators. The operators maintain a good balance between explorative and exploitative strategies while manufacturing the optimum GA solutions. II. ACHIEVING EXPLORATION AND EXPLOITATION IN GENETIC ALGORITHMA crossover or mutation can function as an exploration or exploitation operator [3], [4]. Although optimization algorithms with higher degree of exploitation may have Manuscript received August 30, 2016; revised December 8, 2016. Siew Mooi Lim is with University Malaysia of Computer Science and Engineering, Malaysia (e-mail: limsm66@gmail.com).higher convergence speed, the challenge lies in locating the optimal solution and chances are it may not get past a local optimum. On the other hand, algorithms that favor exploration over exploitation might consume more time in locating the global optimum, that is, coincidentally, due to its less sophisticated candidate solutions. A comprehensive survey in exploratio...

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