artificial ecosystem
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Electronics ◽  
2022 ◽  
Vol 11 (2) ◽  
pp. 204
Author(s):  
Hammed Olabisi Omotoso ◽  
Abdullah M. Al-Shaalan ◽  
Hassan M. H. Farh ◽  
Abdullrahman A. Al-Shamma’a

Electrification of remote rural areas by adopting renewable energy technologies through the advancement of smart micro-grids is indispensable for the achievement of continuous development goals. Satisfying the electricity demand of consumers while adhering to reliability constraints with docile computation analysis is challenging for the optimal sizing of a Hybrid Energy System (HES). This study proposes the new application of an Artificial Ecosystem-based Optimization (AEO) algorithm for the optimal sizing of a HES while satisfying Loss of Power Supply Probability (LPSP) and Renewable Energy Fraction (REF) reliability indices. Furthermore, reduction of surplus energy is achieved by adopting Demand Side Management (DSM), which increases the utilization of renewable energy. By adopting DSM, 28.38%, 43.05%, and 65.37% were achieved for the Cost of Energy (COE) saving at 40%, 60%, and 80% REF, respectively. The simulation and optimization results demonstrate the most cost-competitive system configuration that is viable for remote-area utilization. The proposed AEO algorithm is further compared to Harris Hawk Optimization (HHO) and the Future Search Algorithm (FSA) for validation purpose. The obtained results demonstrate the efficacy of AEO to achieve the optimal sizing of HES with the lowest COE, the highest consistent level, and minimal standard deviation compared with HHO and FSA. The proposed model was developed and simulated using the MATLAB/code environment.


2021 ◽  
Vol 5 (4) ◽  
pp. 49-54
Author(s):  
Ihor Hryhorenko ◽  
Serhii Kondrashov ◽  
Svіtlana Hryhorenko

The paper considers the solution of scientific and practical problem of development and research of control system of parameters of environment of artificial ecosystem, creation of structural and basic electric scheme of system, drawing up of algorithm of its work. The study consists of statistical processing of the results of direct repeated measurements of soluble oxygen level, pH, temperature in the aquarium of the artificial ecosystem, analysis of errors and total standard uncertainty of measurement results, construction of a system with fuzzy logic to determine the impact of aquatic parameters on aquarium water quality. The system makes it possible to measure illuminance up to 45,000 lux, air temperature in the range from 12 to 42 0C, water temperature in the range from 15 to 28 0C, pH level from 5 to 9, dissolved oxygen level from 5 to 10 mg / l, has a proximity sensor , has the ability to turn on, if necessary, heating, water aeration, additional light sources. The measurement error on each of the channels does not exceed 2.5%. The need to create a control system arose due to the fact that there is a need to ensure the natural development of plants and fish in an artificial ecosystem that mimics the environment as close as possible to the natural one. In order for the ecosystem to perform its functions, it is necessary to timely control these parameters and respond quickly to the parameters exceeding the critical values. This task can be accomplished only by creating a control system. In order to bring people closer to the wildlife of exotic countries of the world, you can create corners of wildlife at school, enterprise, institution. An artificial ecosystem, which is a clear and versatile example of wildlife, will help students in the formation of a new culture of relationships with nature, the environment, and allow workers to relax morally by observing wildlife. Such a fruitful rest affects the recovery of people. The artificial ecosystem helps to involve children with talent in research work, in designing projects, performing works related to creativity.


Author(s):  
Quyen Thi Nguyen ◽  
Minh-Phung Bui

This paper presents a new method based on the artificial ecosystem optimization (AEO) algorithm for finding the shortest tour of the travelling salesman problem (TSP). Wherein, AEO is a newly developed algorithm based on the idea of the energy flow of living organisms in the ecosystem consisting of production, consumption and decomposition mechanisms. In order to improve the efficiency of the AEO for the TSP problem, the 2-opt movement technique is equipped to enhance the quality of the solutions created by the AEO. The effectiveness of AEO for the TSP problem has been verified on four TSP instances consisting of the 14, 30, 48 and 52 cities. Based on the calculated results and the compared results with the previous methods, the proposed AEO method is one of the effective approaches for solving the TSP problem.


Mathematics ◽  
2021 ◽  
Vol 9 (19) ◽  
pp. 2363
Author(s):  
Ahmed A. Ewees ◽  
Laith Abualigah ◽  
Dalia Yousri ◽  
Ahmed T. Sahlol ◽  
Mohammed A. A. Al-qaness ◽  
...  

Multilevel thresholding is one of the most effective image segmentation methods, due to its efficiency and easy implementation. This study presents a new multilevel thresholding method based on a modified artificial ecosystem-based optimization (AEO). The differential evolution (DE) is applied to overcome the shortcomings of the original AEO. The main idea of the proposed method, artificial ecosystem-based optimization differential evolution (AEODE), is to employ the operators of the DE as a local search of the AEO to improve the ecosystem of solutions. We used benchmark images to test the performance of the AEODE, and we compared it to several existing approaches. The proposed AEODE achieved a high performance when evaluated by the structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), and fitness values. Moreover, the AEODE outperformed the basic version of the AEO concerning SSIM and PSNR by 78% and 82%, respectively, which reserves the best features for each of AEO and DE.


Author(s):  
Souhil Mouassa ◽  
Marcos Tostado-Véliz ◽  
Francisco Jurado

Abstract With emergence of automated environments, energy demand increased with unexpected ratio, especially total electricity consumed in the residential sector. This unexpected increase in demand in energy brings a challenging task of maintaining the balance between supply and demand. In this work, a robust artificial ecosystem-inspired optimizer based on demand-side management is proposed to provide the optimal scheduling pattern of smart homes. More precisely, the main objectives of the developed framework are: i) Shifting load from on-peak hours to off-peak hours while fulfilling the consumer intends to reduce electricity-bills. ii) Protect users comfort by improving the appliances waiting time. Artificial ecosystem optimizer (AEO) algorithm is a novel optimization technique inspired by the energy flocking between all living organisms in the ecosystem on earth. Demand side management (DSM) program is modeled as an optimization problem with constraints of starting and ending of appliances. The proposed optimization technique based DSM program is evaluated on two different pricing schemes with considering two operational time intervals (OTI). Extensive simulation cases are carried out to validate the effectiveness of the proposed optimizer based energy management scheme. AEO minimizes total electricity-bills while keeping the user comfort by producing optimum appliances scheduling pattern. Simulation results revealed that the proposed AEO achieved a minimization electricity-bill up to 10.95, 10.2% for RTP and 37.05% for CPP for the 12 and 60 min operational time interval (OTI), respectively, in comparison to other results achieved by other optimizers. On the other hand peak to average ratio (PAR) is reduced to 32.9% using RTP and 31.25% using CPP tariff.


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