Optimizing the Semantic Web Service Composition Process Using Cuckoo Search
The behavior of biological individuals which efficiently deal with complex life problems represents an inspiration source in the design of meta-heuristics for solving optimization problems. The Cuckoo Search is such a meta-heuristic inspired by the behavior of cuckoos in search for the appropriate nest where to lay eggs. This paper investigates how the Cuckoo Search meta-heuristic can be adapted and enhanced to solve the problem of selecting the optimal solution in semantic Web service composition. To improve the performance of the cuckoo-inspired algorithm we define a 1-OPT heuristic which expands the search space in a controlled way so as to avoid the stagnation on local optimal solutions. The search space is modeled as an Enhanced Planning Graph, dynamically built for each user request. To identify the optimal solution encoded in the graph we define a fitness function which uses the QoS attributes and the semantic quality as selection criteria. The cuckoo-inspired method has been evaluated on a set of scenarios from the trip planning domain.
KeywordsParticle Swarm Optimization Composition Solution User Request Semantic Quality Service Cluster
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