Wednesday, September 26, 2018 2:18 PM

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Keynote Speakers


Keynote Speakers

Dr. Alireza Asgarizade 

Graduate University of Advanced Technology, Iran, Kerman


Harmony search algorithm: Basic concepts and engineering applications
Abstract:

Harmony search (HS) is a meta-heuristic search algorithm which tries to mimic the improvisation process of musicians in finding a pleasing harmony. In recent years, due to some advantages, HS has received a significant attention. HS is easy to implement, converges quickly to the optimal solution and finds a good enough solution in a reasonable amount of computational time. The merits of HS algorithm have led to its application to optimization problems of different engineering areas. For this aim, the concepts and performance of HS algorithm are shown and some engineering applications are mentioned and reviewed. It is seen that HS has shown promising performance in solving difficult optimization problems and different versions of this algorithm have been developed. In the next years, it is expected that HS is applied to more real optimization problems.




Dr. Esmat Rashedi

Graduate University of Advanced Technology, Iran, Kerman 


Innovative optimization algorithms: Challenges and Prospects

Abstract:
Nowadays, real-word optimization problems are complicated and hard to solve. Furthermore, they are defined in high dimensional space, and in some cases, not enough information is available about the problem. Due to these reasons, traditional methods cannot produce proper solutions. So, there is a lot of attraction toward non-exact inventive optimization algorithms called metaheuristic algorithms. Metaheuristics randomly search the problem space through an iterative heuristic process and yield a sub-optimum solution.  There are many proposed metaheuristic algorithms presented in the literature. A metaheuristic algorithm is a set of ideas, concepts, and operators. However, some researchers claim that some of these heuristic algorithms is not novel. There are three types of metaheuristic optimization algorithms: trajectory based and multi-trajectory based and population based algorithms. The trajectory based algorithms start from an initial random point. After that, the new point is generated using the previous one in each iteration. Some examples are hill climbing, simulated annealing, and tabu search. In multi-trajectory based methods, a trajectory-based method is repeated by a multi-start mechanism. Like greedy randomized adaptive search procedure. Population based methods start from several initial solutions. Then an iterative algorithm is performed to reach the sub-optimum. During the iterations, the population progressively move toward better solutions using probabilistic nature inspired operators.

 




Dr. Saeed Saryazdi
Shahid Bahonar University of Kerman, Kerman, Iran

Graph Modeling and Analyzing and its Application to Citation Networks 

Abstract:

The communication pattern of a system can be expressed as a graph: the graph nodes represent the components of the system and the branches of the graph represent the relationships between the components of the system. In fact, the graph is a simplified expression of the whole system. These systems may be studied in a variety of ways. One way is to examine the components of the system (such as the performance of computers on the Internet or the behavior of individuals in the human community). Another way is to examine “how communication and interactions occur” (such as communication protocols used on the Internet or the familiarity and friendship dynamics of individuals in the community). But there is a third aspect of the attitude to these systems that is sometimes neglected; but in most cases it has a decisive role in determining the behavior of a system. This approach examines the pattern of communication between components of the system.

Centrality is one of the most important and practical criteria in the graph theory. This criterion specifies the degree of impact and degree of importance of each node in the graph. Sometimes, in different applications, it is necessary to know which nodes in a graph are more important than others. Different methods of centrality seek to answer this question.

The Eigenfactor score is a rating of the total importance of a scientific journal. It indicates the importance and validity of a journal in the scientific community. Citations from highly ranked journals weighted to make a larger contribution to the eigenfactor than those from poorly ranked journals.






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3rd Conference on Swarm Intelligence and Evolutionary Computation