IoT Service Composition — An Estimation of Distribution Algorithm with Adaptive Bias

Service composition in Internet of Things (SCIoT), as an emerging topic in service computing, aims to select optimal services to complete user requests according to various user requirements such as minimizing energy consumption and response time. To solve this NP-hard problem, numerous heuristic methods, e.g., local search and population-based algorithms, have been proposed, wherein Estimation of Distribution Algorithm (EDA) gains increasing attention because of its explicit global probabilistic nature. However, existing EDAs increase solution diversity by using fixed bias, yet interfere the stability of the learned distribution in the later stage of optimization. Therefore, this paper proposes an EDA with an adaptive bias strategy (EDA-AdaBias) to solve the service composition in IoT problem. The decreasing bias value is added onto the probability values for all choices of each solution variable over generations, which improves diversity of sampled solutions and avoids dramatic change of constructed distribution. Experiments indicate that EDA-AdaBias presents promising performance compared to other competitive methods on this problem.