Supplementation of water for irrigation in needed in south India due to uncertainty of monsoon rainfall. This paper proposes a support system to manage the irrigation system based on the information provided by humidity, temperature, soil moisture and weather information. The temperature, humidity and soil moisture data were collected by sensors. The proposed ANFIS based system consists of N inputs and a single output which determines the irrigation time needed for the crop. The experimentation is carried out using real time data collected from the region of VALLAM, located near THANJAVUR. The result helps in determining the time for irrigation which helps in increasing the yield of the crop.
This article addresses the problem of converting a single-objective combinatorial problem into a multi-objective one using the Pareto front approach. Although existing algorithms can identify the optimal solution in a multi-objective space, they fail to satisfy constraints while achieving optimal performance. To address this issue, we propose a multi-objective artificial bee colony optimization algorithm with a classical multi-objective theme called fitness sharing. This approach helps the convergence of the Pareto solution set towards a single optimal solution that satisfies multiple objectives. This article introduces multi-objective optimization with an example of a non-dominated sequencing technique and fitness sharing approach. The experimentation is carried out in MATLAB 2018a. In addition, we applied the proposed algorithm to two different real-time datasets, namely the knapsack problem and the nurse scheduling problem (NSP). The outcome of the proposed MBABC-NM algorithm is evaluated using standard performance indicators such as average distance, number of reference solutions (NRS), overall count of attained solutions (TNS), and overall non-dominated generation volume (ONGV). The results show that it outperforms other algorithms.
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