Venue : Coral Deira - April 23 ~ 24, 2016, Dubai, UAE

Accepted Papers

  • Web Service Composition in Dynamic Environment: A Comparative Study
    Aram AlSedrani and Ameur Touir, King Saud University, KSA
    Web service composition development is a complex and dynamic process. It is one of the challenges in distributed dynamic environments. Although, SOA (Service Oriented Architecture) facilitates service composition process through standard protocols in searching and binding with web services. Yet composition in SOA paradigm faces many challenges. One of the main challenges is the environment in which composition services are developed. Nowadays the environment becomes more dynamic due to the increase in the number of web services that are frequently changing. Therefore, the need for self-adapted composition methods that acts according to environment changes is advocated. In this paper, we will study the existed researches that address the web service composition in adynamic environment to state the art in this area and assist future research.
  • Combination of Genetic Algorithm with Dynamic Programming for Solving TSP
    Hemmak Allaoua and Bouderah Brahim, University of Bejaia, Algeria
    This paper presents a combination of Genetic Algorithm (GA) with Dynamic Programming (DP) to solve the well-known Travelling Salesman Problem (TSP). In this work, DP is integrated as a GA operator with a certain probability. In specific, at a given GA generation, the individuals are subdivided into a number of equal segments of genes, and the shortest path on each segment is obtained by applying a DP algorithm. Since the computational complexity of the DP is O (k22k), it becomes of O(1) when k is small. Experimental analyses are conducted to investigate the impact and trade-offs among DP probability, segment size and time processing on the solution quality and computational effort. Also we will implement a basic GA approach to compare results and show the contribution of combination of combination approach. Experimental results on benchmark instances showed that the combined GA-DP algorithm reduces significantly the computational effort, produces a clearly improved solution quality and avoids early premature convergence of GA.