Abstract
Finding maximum independent set (MIS) in a graph is considered one of the fundamental problems in the computer science field, where it can be used to provide solutions for various real life applications. For example, it can be used to provide solutions in scheduling and prioritization problems. Unfortunately, this problem is one of the NP-problems of computer science, which limit its usage in providing solution for such problems with large sizes. This leads the scientists to find a way to provide solutions of such problems using fast algorithms to provide some near optimal solutions. One of the techniques used to provide solutions is to use metaheuristic algorithms. In this paper, a metaheuristic algorithm based on Chemical Reaction Optimization (CRO) is applied with various techniques to find MIS for application represented by a graph. The suggested CRO algorithm achieves accuracy percentages that reach 100% in some cases. This variation depends on the overall structure of the graph along with the picked parameters and colliding molecule selection criteria during the reaction operations of the CRO algorithm.
Highlights
In this paper, a metaheuristic Chemical Reaction Optimization (CRO) algorithm has been utilized to find out maximum independent set (MIS) in a graph
The generated graphs are saved on permanent storage to insure the execution of various CRO versions on the same graphs for more accurate comparison
As in [7], most of these graphs are manipulated using Modified Wilf algorithm to find out their exact MIS solution
Summary
A metaheuristic Chemical Reaction Optimization (CRO) algorithm has been utilized to find out maximum independent set (MIS) in a graph. In this approach, computational steps are formulated as a set of molecules reactions that leads toward approximated solution. Chemical Reaction Optimization (CRO) is defined in [1,15] as a metaheuristic approach that mimics the process of chemical reactions in the field of Computer Science It relays on minimizing the potential energy to the minimal value without sticking in local minima. Finding a maximum independent set with near optimal results would be used to provide a solution of many real-life applications; such as prioritization and scheduling applications
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