Multi-objective Service Composition Optimization in Smart Agriculture Using Fuzzy-Evolutionary Algorithm

Agricultural applications can take advantage of improved services provided by the Internet of Things paradigms to manage data effectively. It is necessary to manage Quality of Service (QoS) characteristics to effectively monitor and measure the given services. Given how challenging it is to satisfy a user’s complicated requirements with a single service, this paper presents a QoS-aware method for sending agricultural information as a service and then combining those services, thus, known as service composition. The proposed work is divided into two phases. In the first phase, a fuzzy inference set is used to initialize the population whereas, in the second phase, the multi-objective evolutionary algorithm NSGA-II (Non-dominated sorting genetic algorithm) has been used to optimize the cost and time of services involved in apple crop production. Since evolutionary algorithms have a problem dealing with uncertainties so modification using fuzzy logic has been proposed to check its effectiveness in Service Composition Problem (SCP). In order to demonstrate the persuasiveness of our work, the proposed method is compared with the multi-objective genetic algorithm (MOGA), Gaining sharing knowledge (GSK) algorithm, and NSGA-II and it has been found that NSGA-II is giving more diversified and near to true Pareto solutions.