artificial bee colony algorithm

Efficient use of energy in paddy production can lower greenhouse gas emissions, safeguard agricultural ecosystems, and promote the growth of sustainable agriculture. Meanwhile, intelligent agriculture has come to the aid of farmers and policy-makers by harnessing cutting-edge technologies, which will lead to sustainable welfare and the comfort of human society in the present and the future. Therefore, this study aimed to analyze energy consumption and production, as well as model and optimize the yield of two paddy cultivars using Artificial Bee Colony (ABC) and Genetic Algorithms (GA).

Extensive research data was collected by thoroughly examining documentary and library resources, as well as conducting face-to-face questionnaires with 120 paddy farmers and farm owners in Rezvanshahr city, located in the province of Guilan, Iran, during the 2019-2020 production year. The farms consisted of 80 high-grading and 40 high-yielding paddies. The independent variables were machinery, diesel and gasoline fuels, electricity, seed, compost and straw, biocides, fertilizers, and labor. The dependent variable was paddy yield per hectare [of the farm area]. In the first step, energy consumption and production were calculated by multiplying the variables by their corresponding coefficients. In the second step, all the variables that maximize paddy yield were entered into MATLAB software. An artificial bee colony (ABC) algorithm with a novel and straightforward elitism structure was utilized to enhance the fitness function of the genetic algorithm (GA). The Sphere, Repmat, and Unifrnd functions were employed to determine the objective function, define the position of the bee colony, and quantify the position of the bee colony, respectively. In each generation, 900 new solutions were created, and the algorithm iterated 200 times. For the genetic algorithm, the population was defined as a double vector with a size of 100.

The findings revealed that the Hashemi (high-grading) paddy cultivar had an average energy consumption and production of 55.973 and 30.742 GJ·ha-1, respectively. The Jamshidi (high-yielding) paddy cultivar had an average energy consumption of 54.796 GJ·ha-1 and double the energy production of the Hashemi at 62.522 GJ·ha-1. In both cultivars, agricultural machinery consumed the highest amount of energy, while straw consumed the lowest amount. The average energy consumption of tractors in the Hashemi and Jamshidi cultivars was 25.111 and 25.865 GJ·ha-1, respectively, accounting for 44.862% and 47.202% of the total average consumed energy. This undoubtedly demonstrates the significant effect of this input and reflects the operators' skill and experiential knowledge. The evaluation indexes, including R², RMSE, MAPE, and EF, as well as statistical comparisons such as mean, STD, and distribution, consistently demonstrated that the ABC algorithm provided the essential conditions for the fitness function. The results of the bee-genetic algorithm optimization revealed that the majority of the consumed resources could be effectively managed on the farm to closely match optimal conditions. Through this optimization, energy consumption in the Hashemi and Jamshidi cultivars was reduced by 53.96% and 39.41%, respectively.

Given its impressive performance and potential for minimizing energy consumption, the ABC-GA algorithm offers an opportunity for policymakers in energy resource management and rice industry managers to develop innovative strategies for significantly reducing energy usage in rice production. This approach could lead to more sustainable and efficient practices in the agricultural sector.

Urban Water Distribution Networks are the most important and costly network in each city. The Major part of the urban water distribution network costs is related to the executive progress and purchase of water distribution network accessories. Therefore, the cost of the water distribution network can be reduced by reducing and optimizing this part. For this purpose, the problem of water distribution network design should be defined as an optimization model and solved it using an efficient method. Nowadays, Meta-heuristics algorithms are the most efficient methods for solving optimization models. In this research, two sample problems mean Schaake and Lai (1969) water distribution network and NEWYORK city are modeled in the EPANET software and solved using an improved version of the artificial bee colony algorithm that is called MATLAB software. To evaluate the efficiency of the proposed algorithm, the results are presented and compared with the basic and standard version of the artificial bee colony algorithm and other results. Using an improved artificial bee colony algorithm for the two-loop network (NEWYORK), the objective function value and computational cost are 419000 $ (38.13 M$) and 2800 (3800), respectively. A comparison of the results shows that the construction and computed costs are reduced compared with the result of the standard version. Therefore, this algorithm is an efficient algorithm for solving optimization problems such as the design of a water distribution network.

One of the key factors affecting leakage in water distribution systems is network pressure management. The main objective of pressure management in water distribution system is to minimize water leakages along with maintaining the required pressure at every node. A common way to reduce pressure is to locate flow or pressure reducing valves (PRV) and optimal regulation of these vales in water networks.This study aimed at investigating optimal pressure management problems so as to minimize leakage in water distribution networks. To do so, a two-phase approach was proposed for both optimal positioning and setting of pressure reduction valves (PRVs), where location optimization was addressed at the first stage and optimal operation issue of valves was considered in the next step. In the present research, in order to resolve these problems, an optimization model—a simulation based on an emerging algorithms inspired by honeybees’ behavior called as Artificial Bee Colony (ABC)—was utilized. In this model, ABC optimization in MATLAB environment was integrated with hydraulic simulation of EPANET model. In the next step, the obtained results were compared with those of the previous studies. The results revealed that in case all the limitations of the problem are observed, employing this method to determine the position of the pressure reduction valves and regulate them lowered the mean leakage rate of the network from 82.28 to 72.15 lit/sec by 14.75% in three phases of maximum, average and minimum aquatic need periods.This means that the proposed method was effective in regulating the pressure level to minimize leakage in networks. Comparing the results of the present method with those of the previous approaches revealed that GA algorithm reached better response in fewer times than ICA, CA and ABC methods.

The optimization algorithms inspired by honey bee’s social behavior are among the most recent optimization techniques. Artificial bee colony algorithm (ABC) is one of these algorithms. Today, considering the dramatic increase in pumping energy prices in water conveyance systems, problem of optimal operation of pumping stations is one of the very hottest research areas. In many pumping stations there are no specific guidelines for the operation of the existing pumps and station ope rator acts to turn on and turn of the pumps based on experience and need will be announced him. This traditional method impose a lot of extra cost to sys tem. Therefore, in addition to proper design of pumping stations, operation of these stations is also extremely important. The most important factor affecting the pu mping station performance is the per formance of the pumps, thus pumps should be used as much as possible in their maximum efficiency.
In this research a pump operation schedule is represented as a string of binary values with each bit representing pump on and off status during a particular time interval and pump optimal scheduling problem is coded as a problem of finding the best binary string which results in the least energy price. In this study a Binary Arti ficial Bee C olony Optimiz ation algorithm based simulation- optimization model has been developed for optimal scheduling of serial pumping stations. The model integrates ABC optimizer and EPA NET hydraulic network solver in MAT LAB software. The proposed model is applied to find the optima l pump operation schedule of Shiraz water conveyance system from Dor oudzan Dam in an ordinary day of the year. Then, the optimal operation mode on this special day was compared with a non-optimal utilization scenarios. The average cost of electrical energy was considered equal to 275 Rials in this study.
The results showed that having regard to all the constraints of the problem, the energy cost in the optimal operation was 32% less than average one in an ordinary day. Bit ABC algorithm also caused about 8 percent improvement in optimal algorithm of - PSO with the general neighborhood, but the cost of response obtained in this study was about 2 percent higher than the - PSO algorithm with local neighborhood.
The comparison between the optimal operation program and the previous researches results showed the model’s abilities.