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In recent days, there is a huge loss in the income of farmers due to the reasons such as low water lever and increased pesticide attack. Therefore, the purpose of this paper is to establish an efficient reliable low-cost information gathering Reliable Low-Cost Information Gathering Protocol (RLCIG) protocol for agricultural water irrigation using optimal clustering and path selection technique where the RCIG protocol wrinkles the expedient statistics about the moisture and temperature of the soil and it will be installed few inches below the pipeline. Thereafter, the congregated data will augment the irrigation of water by using a decision-making algorithm.

The projected model has been inscribed mathematically by underlying the wireless sensor networks (WSN) framework with deliberation of contemporary challenges. Furthermore, the energy, cost and expanse optimization framework in the WSN framework is presented. The projected technique has been tested using network simulator and the results are also integrated MATLAB.

Recently, for efficacious management in the field of agriculture, the WSN has been successfully assimilated. This instigation accomplishes the irrigation management in terms of energy, cost and communication distance. The simulation result shows that the proposed model yields better results in terms of both the transmission range and cost with efficient lifetime improvement in comparisons with existing techniques.

Agriculture is the need of the time whatever invention happens in the scientific world without food production no lives survive on the earth, hence, the scientific invention should also focus on agriculture, in this contrast, the authors have proposed an efficient low-cost information gathering (RCIG) protocol for agricultural water irrigation using optimal clustering and path selection technique.

Generator maintenance scheduling (GMS) is an essential task for electric power utilities as the periodical maintenance activity enhances the lifetime and also ensures the reliable and continuous operation of generating units. Though numerous meta-heuristic algorithms have been reported for the GMS solution, enhancing the existing techniques or developing new optimization procedure is still an interesting research task. The meta-heuristic algorithms are population based and the selection of their algorithmic parameters influences the quality of the solution. This paper aims to propose statistical tests guided meta-heuristic algorithm for solving the GMS problems.

The intricacy characteristics of the GMS problem in power systems necessitate an efficient and robust optimization tool. Though several meta-heuristic algorithms have been applied to solve the chosen power system operational problem, tuning of their control parameters is a protracting process. To prevail over the previously mentioned drawback, the modern meta-heuristic algorithm, namely, ant lion optimizer (ALO), is chosen as the optimization tool for solving the GMS problem.

The meta-heuristic algorithms are population based and require proper selection of algorithmic parameters. In this work, the ANOVA (analysis of variance) tool is proposed for selecting the most feasible decisive parameters in algorithm domain, and the statistical tests-based validation of solution quality is described. The parametric and non-parametric statistical tests are also performed to validate the selection of ALO against the various competing algorithms. The numerical and statistical results confirm that ALO is a promising tool for solving the GMS problems.

As a first attempt, ALO is applied to solve the GMS problem. Moreover, the ANOVA-based parameter selection is proposed and the statistical tests such as Wilcoxon signed rank and one-way ANOVA are conducted to validate the applicability of the intended optimization tool. The contribution of the paper can be summarized in two folds: the ANOVA-based ALO for GMS applications and statistical tests-based performance evaluation of intended algorithm.

This study aims to minimize operating cost, adhere to pollution norms and maintain reserve and voltage levels subject to various operational concerns, including non linear characteristics of generators and fuel limitation issues, which are useful for the current power system applications.

Improved control settings are required while considering multiple conflicting operational objectives that necessitate using the modern bio-inspired algorithm ant lion optimizer (ALO) as the main optimization tool. Fuzzy decision-making mechanism is incorporated in ALO to extract the best compromise solution (BCS) among set of non-dominated solutions.

The BCS records of IEEE-30 bus and JEAS-118 bus systems are updated in this work. Numerical simulation results comparison and comprehensive performance analysis justify the applicability of the intended algorithm to solve multi-objective dynamic optimal power flow (DOPF) problem over the state-of-art methods.

Optimal control settings are obtained for IEEE-30 and JEAS-118 bus systems with the objectives of minimizing fuel cost and emission in dynamic environment considering take-or-pay fuel contract issue. The fuzzy supported ALO (FSALO) is applied first time to solve the DOPF problem.