Rafid Sagban

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Ant colony optimization is one of the most successful examples of swarm intelligent systems. The exploration and exploitation are the main mechanisms in controlling search within the ACO. Reactive search is a framework for automating the exploration and exploitation in stochastic algorithms. Restarting the search with the aid of memorizing the search(More)
A statistical machine learning indicator, ACOustic, is proposed to evaluate the exploration behavior in the iterations of ant colony optimization algorithms. This idea is inspired by the behavior of some parasites in their mimicry to the queens' acoustics of their ant hosts. The parasites' reaction results from their ability to indicate the state of(More)
Travelling salesman problem (TSP) finds applications in wide domains. It is a well known NP Hard problem. In this paper we have proposed nature inspired ant colony optimization on travelling salesman problem. A comparative analysis is done among the standard travelling salesman problem and proposed ant colony based approaches. All the implementations are(More)
This intensification and diversification in Ant Colony Optimization (ACO) is the search strategy to achieve a trade-off between learning a new search experience (exploration) and earning from the previous experience (exploitation). The automation between the two processes is maintained using reactive search. However, existing works in ACO were limited(More)
Ant colony optimization (ACO) is a stochastic search method for solving NP-hard problems. The exploration versus exploitation dilemma rises in ACO search. Reactive max-min ant system algorithm is a recent proposition to automate the exploration and exploitation. It memorizes the search regions in terms of reactive heuristics to be harnessed after restart,(More)
Cloud computing has bring a revolution in the field of computing. Many algorithms are proposed to make it even more efficient. In cloud computing Virtualization plays an important role and whole performance of cloud depends on VM allocation and Migration. As lots of energy is consumed in this technology so algorithms to save energy and improve efficiency(More)
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