per Iranian Research Institute for Electrical Engineeringفصلنامه مهندسی برق و مهندسی کامپيوتر ايران16823745168237452026-10241113articleSina Samadi Gharehverans.samadi@tabrizu.ac.ir1Kimia Shiriniki.shirini@tabrizu.ac.ir2MirReza SeyedGhoreyshireza.ghoreyshi1400@ms.tabrizu.ac.ir3Sadra Amirzehnisadra.amirzehni@gmail.com4Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, IranTabriz Islamic Art University, Multimedia Faculty, Tabriz, IranFaculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, IranFaculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran<div class="flex flex-col text-sm pb-25"> <article class="text-token-text-primary w-full focus:outline-none [--shadow-height:45px] has-data-writing-block:pointer-events-none has-data-writing-block:-mt-(--shadow-height) has-data-writing-block:pt-(--shadow-height) [&amp;:has([data-writing-block])&gt;*]:pointer-events-auto scroll-mt-[calc(var(--header-height)+min(200px,max(70px,20svh)))]" dir="auto" tabindex="-1" data-turn-id="request-WEB:19144214-9180-4633-9acb-84768e0e0beb-2" data-testid="conversation-turn-6" data-scroll-anchor="true" data-turn="assistant"> <div class="text-base my-auto mx-auto pb-10 [--thread-content-margin:--spacing(4)] @w-sm/main:[--thread-content-margin:--spacing(6)] @w-lg/main:[--thread-content-margin:--spacing(16)] px-(--thread-content-margin)"> <div class="[--thread-content-max-width:40rem] @w-lg/main:[--thread-content-max-width:48rem] mx-auto max-w-(--thread-content-max-width) flex-1 group/turn-messages focus-visible:outline-hidden relative flex w-full min-w-0 flex-col agent-turn" tabindex="-1"> <div class="flex max-w-full flex-col grow"> <div class="min-h-8 text-message relative flex w-full flex-col items-end gap-2 text-start break-words whitespace-normal [.text-message+&amp;]:mt-1" dir="auto" data-message-author-role="assistant" data-message-id="cab128fb-9af1-4d43-ad7a-59bb4ab2649f" data-message-model-slug="gpt-5-2"> <div class="flex w-full flex-col gap-1 empty:hidden first:pt-[1px]"> <div class="markdown prose dark:prose-invert w-full break-words light markdown-new-styling"> <p data-start="26" data-end="1489" data-is-last-node="" data-is-only-node="">Given the growing concerns about the increase in environmental pollutants from fossil-fuel power plants, numerous economic challenges related to optimizing generation and consumption costs in energy distribution networks have emerged. This paper presents a multi-objective optimization model for the operational management of microgrids considering the growing demand for hybrid electric vehicles. The objective of the proposed model is to reduce total operating costs and enhance network reliability by minimizing unserved energy. To achieve these goals, a multi-objective Seagull Optimization Algorithm, inspired by the search behavior of seagulls, is employed to obtain optimal solutions. Electric vehicles and demand response programs are incorporated into the model to reduce carbon dioxide emissions and smooth distribution locational prices. The main contribution of this research is the development of a hybrid optimization framework for coordinating electric vehicles, renewable energy resources, and demand response management. Performance evaluation using the 69-bus distribution network demonstrates that the proposed method reduces operating costs by 12.5%, decreases unserved energy from 300 kW to 150 kW, reduces network power losses by 15%, and improves the voltage profile within stability limits. Results confirm the effectiveness of the proposed approach in optimizing network performance and reducing operational costs.</p> </div> </div> </div> </div> </div> </div> </article> </div>http://ijece.org/Article/52603Seagull Optimization Algorithm Demand Response Program Microgrid Electric Vehicle Distribution Locational Marginal Pricesper Iranian Research Institute for Electrical Engineeringفصلنامه مهندسی برق و مهندسی کامپيوتر ايران16823745168237452026-102411426articleAli Namdarali.namdar@gmail.com1maryam mohitpourmaryammohitpour@gmail.com2Gohar VaraminiGohar.Varamini@gmail.com3Department of Electrical Engineering, Shi.C., Islamic Azad University, Shiraz, Iran.Department of Electrical Engineering, Shi.C., Islamic Azad University, Shiraz, Iran.Department of Electrical Engineering, Bey.C., Islamic Azad University, Beyza, Iran.<p style="text-align: left;">Research on the subject of studying and optimizing plasmonic logic gates is carried out in order to increase the speed and efficiency of information processing and communication between electronic devices and networks. In this research, plasmonic technology is used to design and manufacture logic gates in COMSOL software. The main goal of this research is to improve the performance of electronic devices and increase their efficiency. In this regard, the leading neural network with the help of MATLAB software is also used to optimize plasmonic logic gates. Research conducted in this field has shown that the use of plasmonic logic gates increases the speed and reduces the energy consumption in information processing. However, in order to achieve an optimal parameter, some of the other parameters are inevitably ignored. In general, this research is carried out in order to improve the performance of electronic devices and increase their efficiency in the most optimal way possible and to examine all the basic parameters available with the help of the powerful neural network tool.&nbsp; . .</p>http://ijece.org/Article/53824Graphene-Photonic Crystal Waveguide Logic Gates Plasmonic Technology Neural Network.per Iranian Research Institute for Electrical Engineeringفصلنامه مهندسی برق و مهندسی کامپيوتر ايران16823745168237452026-102412734articleP. Khavashp.khavash@yahoo.com1A. Ramezaniramezani@modares.ac.ir2S. Ozgoliozgoli@modares.ac.ir3Elec. and Comp. School, Tarbiat Modares University, Tehran, IranElec. and Comp. School, Tarbiat Modares University, Tehran, IranElec. and Comp. School, Tarbiat Modares University, Tehran, Iran<p style="direction: ltr;">Automotive air conditioning works based on refrigeration cycle. The simplest model of this cycle is multi input-multi output and has operator constraints, so model predictive control due to its features is considered as a convenient approach to control the system. But using the exact model of the process is one the explicit needs of this controller, while the automotive air conditioning system is affected by unknown disturbances. For a moving vehicle, the relative speed change of wind could change air mass flow rate in heat exchangers of refrigeration cycle. In this paper, designing the robust model predictive control is done based on linear matrix inequalities to compensate the effects of this disturbance. Other external disturbances that affect the performance of this system, is the increase in ambient temperature, which reduces performance coefficient of automotive air conditioning system and increases the cooling load applied to it. In this paper disturbance because of changing ambient temperature is added to the model then design of the centralized robust model predictive control is done in the presence of both referred disturbances to the system. Thus, predictive control weakness in the rejection of these disturbances is compensated.</p>http://ijece.org/Article/28836Automotive Air Conditioning Refrigeration cycleUnknown disturbancesRobust Model Predictive Controlper Iranian Research Institute for Electrical Engineeringفصلنامه مهندسی برق و مهندسی کامپيوتر ايران16823745168237452026-102413542articleM. Bensaeedmisaghbensaeed@gmail.com1N. Erfani Majdnasser.erfanimajd@gmail.com2Dept. of Elec. Eng., Shohadaye Hoveizeh Campus of Technology, Shahid Chamran University of Ahvaz, Ahvaz, IranDept. of Elec. Eng., Shohadaye Hoveizeh Campus of Technology, Shahid Chamran University of Ahvaz, Ahvaz, Iran<p>With the development of hybrid energy resources and electrical energy storage devices, the operation of distribution networks has become more flexible. In order to reduce operating costs, optimal management of these resources in the distribution network is essential. Since the ownership of these resources is usually private, it is necessary to include the owners' income in the objective function, in addition to the operating cost. This research presents an optimization model for locating and capacity finding hybrid energy resources next to a pumped storage power plant to design a micro-grid with the ability to operate as an island. The proposed model is presented from the perspective of network operation by the energy resource aggregator. The objective function includes reducing the cost of losses, the cost of energy supply, increasing the benefits of the aggregator, and improving the reliability of the network. The uncertainties of the consumed and produced power of the resources are modeled in the form of time scenarios, and the teaching learning based optimization algorithm in MATLAB software is used for optimization. Simulation results on a standard 69-bus distribution network indicate the superiority of the proposed model in reducing costs and increasing resource aggregator revenue.</p>http://ijece.org/Article/49570Micro-grid design Hybrid energy resources Operating cost Resource aggregator revenue Teaching learning-based optimization. per Iranian Research Institute for Electrical Engineeringفصلنامه مهندسی برق و مهندسی کامپيوتر ايران16823745168237452026-102414350articleSeyed Hakim Hosseinimshhmbs@gmail.com1S. J. Javadi Moghadamjavadi5599@gmail.com2M. R. Gholamigholami_0062@pnu.ac.ir3Elec. Eng. Dept., Payam-e Noor University, Tehran, IranElec. Eng. Dept., Payam-e Noor University, Tehran, IranElec. Eng. Dept., Payam-e Noor University, Tehran, Iran<p style="direction: ltr;">The main goal of this study was the simultaneous optimization of environmental-economic dispatch (EED) in power systems using a multi-objective Artificial Bee Colony (MOABC) algorithm. A multi-objective environmental-economic dispatch model for power systems was developed with the objectives of minimizing economic cost and pollution emissions to address the challenges in power system distribution caused by the global energy crisis and global warming, and to promote the realization of the dual carbon goal.</p> <p style="direction: ltr;">A multi-objective Artificial Bee Colony algorithm (MOABC) based on non-dominated sorting and an improved greedy criterion was designed according to the characteristics of the proposed model. In designing the algorithm, the Taguchi method was used for parameter optimization, a heuristic method was applied for dynamic constraint handling, and various comprehensive evaluation indices were employed to assess the algorithm&rsquo;s performance.</p> <p>Simulation analysis was performed on a six-generator power system and a ten-generator power system, and the results were compared with different algorithms to validate the rationality and effectiveness of the proposed model. Finally, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was used to determine the optimal compromise solution as a reference for scientific decision-making by dispatchers.</p>http://ijece.org/Article/51104Economic and environmental optimization power plants power systems honey bee colony algorithmper Iranian Research Institute for Electrical Engineeringفصلنامه مهندسی برق و مهندسی کامپيوتر ايران16823745168237452026-102415158articleZ. Beikiz.beiki@eng.ui.ac.ir1Comp. Eng. Faculty, Isfahan University, Isfahan, Ira<p style="direction: ltr;">Nowdays, the design of molecular processors for disease diagnosis, particularly early cancer detection, has become one of the most active research and technology areas. In this paper, DNA-based molecular processors are introduced. First, controllable and scalable self-assembly tiles were designed. These tiles, implemented using hairpin DNA strands, are capable of receiving single-stranded inputs, generating outputs that can be utilized in subsequent steps, and enabling cascading connections across different levels. In the next step, basic logic gates (AND, OR, NOT) were constructed using the designed tiles, and a processor for detecting 12 microRNAs, aimed at early cancer diagnosis, was implemented. Simulations were performed using the VisualDSD tool under a deterministic model. The simulation results for the considered test set achieved PPV &asymp; 0.91 and NPV &asymp; 0.99, demonstrating the promising performance of the designed processor.</p> <p>&nbsp;</p>http://ijece.org/Article/51618Molecular processors early disease detection DNA self-assembly DNA-based computing.