Journal articles: 'GA-based techniques'

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Lin, Wen-Pin, and Jih-Gau Juang. "Aircraft Landing Control Based on CMAC and GA Techniques." IFAC Proceedings Volumes 41, no. 2 (2008): 8576–81. http://dx.doi.org/10.3182/20080706-5-kr-1001.01450.

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Abed, Wisam Najm AL-Din, Adham Hadi Saleh, and Abbas Salman Hameed. "Speed Control of PMDCM Based GA and DS Techniques." International Journal of Power Electronics and Drive Systems (IJPEDS) 9, no. 4 (December 1, 2018): 1467. http://dx.doi.org/10.11591/ijpeds.v9.i4.pp1467-1475.

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<span lang="EN-US">Permanent magnet direct current motors (PMDCM) are widely used in various applications such as space technologies, personal computers, medical, military, robotics, electrical vehicles, etc. In this paper, the mathematical model of PMDCM is designed and simulated using MATLAB software. The PMDCM speed is controlled using rate feedback controller due to its ability of improving system damping. To improve the controller performance, it’s parameters are tuned using genetic algorithm (GA) and direct search (DS) techniques. The tuning process based on different performance criteria. The most four common performance criteria used in this paper are JIAE (Integral of Absolute Error), JISE (Integral of Square Error), JITAE (Integral of Time-Weighted Absolute Error), and JITSE (Integral of Time-Weighted Square Error). The results obtained from these evolutionary techniques are compared. The results show an obvious improvement in system performance including enhancing the transient and steady state of PMDCM speed responses for all performance criteria.</span>

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Coello, Carlos A. "An updated survey of GA-based multiobjective optimization techniques." ACM Computing Surveys 32, no. 2 (June 2000): 109–43. http://dx.doi.org/10.1145/358923.358929.

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Chen, Shyi-Ming, and Yu-Chuan Chang. "Weighted Fuzzy Rule Interpolation Based on GA-Based Weight-Learning Techniques." IEEE Transactions on Fuzzy Systems 19, no. 4 (August 2011): 729–44. http://dx.doi.org/10.1109/tfuzz.2011.2142314.

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Safari, Amin, Ali Ahmadian, and Masoud Aliakbar Golkar. "Comparison of Honey Bee Mating Optimization and Genetic Algorithm for Coordinated Design of Pss and Statcom Based on Damping of Power System Oscillation." Journal of Electrical Engineering 64, no. 3 (May 1, 2013): 133–42. http://dx.doi.org/10.2478/jee-2013-0020.

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Recently, honey bee mating optimization (HBMO) technique and genetic algorithms (GA) have attracted considerable attention among various modern heuristic optimization techniques. This paper presents the application and performance comparison of HBMO and GA optimization techniques, for coordinated design of STATCOM and PSS. The design objective is to enhance damping of the low frequency oscillations. The design problem of the controller is formulated as an optimization problem and both HBMO and GA optimization techniques are employed to search for optimal controller parameters. The performance of both optimization techniques for damping low frequency oscillations are tested and demonstrated through nonlinear time-domain simulation and some performance indices studies to different disturbances over a wide range of loading conditions. The results show that the designed controller by HBMO performs better than GA in finding the solution. Moreover, the system performance analysis under different operating conditions show that the φ based controller is superior to the C based controller.

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Priya, A. Saravana, and Dr Rajeswari Mukesh. "GA based Feature Selection for Multimodal Biometric Authentication." Indian Journal of Computer Science and Engineering 12, no. 2 (April 20, 2021): 526–38. http://dx.doi.org/10.21817/indjcse/2021/v12i2/211202163.

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Multi-modal biometric authentication effectively replaces uni-modal biometric authentication system towards addressing a wide range of technical glitches in identity management and authentication. Legitimacy is playing a vital role in banking, military, and healthcare sectors where highly secure, strategic and confidential data transmission is involved. By integrating many independent biometric systems, one can overcome the problems of spoofing. However, there is lack of a simple, efficient and sufficient biometric authentication. Hence, the present study focuses on designing and implementing a multi-modal biometric authentication using a Genetic Algorithm (GA) based feature extraction method. The proposed research focuses on extracting human Skeleton and Human face feature using 3D Imaging technology. This modelling technique is used to capture human joints including the depth data to improve the efficiency of the system. The proposed research is subdivided into three phases. These are, image preprocessing (MinMax method), feature extraction using Heuristic Optimization Techniques (HOT), and Personnel recognition via the Artificial Neural Network (ANN). The Performance of the proposed method is evaluated based on the measure of FAR, FRR and accuracy. Finally, the performance of proposed approach is compared with existing techniques like GA, Neural network, etc. Combined Biometric is done in an unobtrusive way whereas other human recognition needs physical contact.

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Anitha, J., C. Kezi Selva Vijila, and D. Jude Hemanth. "A Hybrid Genetic Algorithm based Fuzzy Approach for Abnormal Retinal Image Classification." International Journal of Cognitive Informatics and Natural Intelligence 4, no. 3 (July 2010): 29–43. http://dx.doi.org/10.4018/jcini.2010070103.

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Fuzzy approaches are one of the widely used artificial intelligence techniques in the field of ophthalmology. These techniques are used for classifying the abnormal retinal images into different categories that assist in treatment planning. The main characteristic feature that makes the fuzzy techniques highly popular is their accuracy. But, the accuracy of these fuzzy logic techniques depends on the expertise knowledge, which indirectly relies on the input samples. Insignificant input samples may reduce the accuracy that further reduces the efficiency of the fuzzy technique. In this work, the application of Genetic Algorithm (GA) for optimizing the input samples is explored in the context of abnormal retinal image classification. Abnormal retinal images from four different classes are used in this work and a comprehensive feature set is extracted from these images as classification is performed with the fuzzy classifier and also with the GA optimized fuzzy classifier. Experimental results suggest highly accurate results for the GA based classifier than the conventional fuzzy classifier.

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ZAKA, IMRAN, HABIB UR REHMAN, SYED ISMAIL SHAH, and JAMIL AHMAD. "PSO- AND GA-BASED NARROWBAND JAMMER EXCISION IN CDMA." Journal of Circuits, Systems and Computers 19, no. 01 (February 2010): 123–38. http://dx.doi.org/10.1142/s0218126610005949.

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Narrowband jammer excision is formulated as an optimization problem in this paper. Optimal filter weights are calculated/searched for by the computational intelligence techniques. We compare the error rate performance, complexity, and implementation issues of various computational intelligence techniques like Particle Swarm Optimization (PSO), Genetic Algorithm, and Least Mean Square (LMS). These techniques update the excision filter weights iteratively till the convergence criteria has been achieved. Bit Error Rate performance shows these techniques effectively suppress the Narrow Band Interference. It has been observed that PSO-based algorithm with tuned parameters outperforms other schemes of PSO and the other algorithms. It approaches the optimum performance in fewer iterations with ease in implementation.

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Al-Dallal, Ammar, Rasha S. Abdulwahab, and Ramzi El-Haddadeh. "IR with and without GA." International Journal of Applied Metaheuristic Computing 4, no. 1 (January 2013): 1–20. http://dx.doi.org/10.4018/jamc.2013010101.

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This paper proposes two IR approaches; the first is IR with GA, which is a GA-based IR approach. This approach introduces modified GA operators that allow IR with GA to achieve high performance. The second IR model is IR without GA, which is based on traditional IR approach. Both enhance the precision and recall of the web search by improving the document representation where an enhanced inverted index is developed for this purpose. Moreover, these two models use the same proposed evaluation function for measuring the document relativity to the user query. A number of experiments were conducted to compare the performance of the two suggested approaches with existing techniques. The two suggested approaches were then compared experimentally with another two techniques of classical IR namely Okapi-BM25 fitness function and Bayesian inference network model from documents quality of retrieval perspective. The obtained results demonstrate a good level of enhancement to the recall and precision times. In addition, the documents retrieved by IR with and without GA are more accurate and relevant to the queries than that retrieved by other techniques. Overall, the two suggested approaches provide a promising technique in web search domain delivering a high quality search results in terms of recall and precision.

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Jerop, Brenda, and Davies Rene Segera. "An Efficient PCA-GA-HKSVM-Based Disease Diagnostic Assistant." BioMed Research International 2021 (October 20, 2021): 1–10. http://dx.doi.org/10.1155/2021/4784057.

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Disease diagnosis faces challenges such as misdiagnosis, lack of diagnosis, and slow diagnosis. There are several machine learning techniques that have been applied to address these challenges, where a set of symptoms is applied to a classification model that predicts the presence or absence of a disease. To improve on the performance of these techniques, this paper presents a technique which involves feature selection using principal component analysis (PCA), a hybrid kernel-based support vector machine (HKSVM) classification model and hyperparameter optimization using genetic algorithm (GA). The HKSVM in this paper introduces a new way of combining three kernels: Radial basis function (RBF), linear, and polynomial. Combining local (RBF) and global (linear and polynomial) kernels has the effect of improved model performance. This is because the local kernels are better able to distinguish points closer to each other while the global kernels are more suited to distinguish points that are far away from each other. The PCA-GA-HKSVM is used on 7 different medical datasets, with two datasets being multiclass datasets and 5 datasets being binary. Performance evaluation metrics used were accuracy, precision, and recall. It was observed that the PCA-GA-HKSVM offered better performance than the single kernel support vector machines (SVMs).

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Juhaniya, Ahamed Ibrahim Sithy, Ahmad Asrul Ibrahim, Muhammad Ammirrul Atiqi Mohd Zainuri, Mohd Asyraf Zulkifley, and Muhammad Akmal Remli. "Optimal Stator and Rotor Slots Design of Induction Motors for Electric Vehicles Using Opposition-Based Jellyfish Search Optimization." Machines 10, no. 12 (December 14, 2022): 1217. http://dx.doi.org/10.3390/machines10121217.

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This study presents a hybrid optimization technique to optimize stator and rotor slots of induction motor (IM) design for electric vehicle (EV) applications. The existing meta-heuristic optimization techniques for the IM design, such as genetic algorithm (GA) and particle swarm optimization (PSO), suffer premature convergence, exploration and exploitation imbalance, and computational burden. Therefore, this study proposes a new hybrid optimization technique called opposition-based jellyfish search optimization (OBJSO). This technique adopts opposition-based learning (OBL) into a jellyfish search optimization (JSO). Apart from that, a multi-objective formulation is derived to maximize the main performance indicators of EVs, including efficiency, breakdown torque, and power factor. The proposed OBJSO is used to solve the optimal design of stator and rotor slots based on the formulated multi-objective. The performance is compared with conventional optimization techniques, such as GA, PSO, and JSO. OBJSO outperforms three other optimization techniques in terms of average fitness by 2.2% (GA), 1.3% (PSO), and 0.17% (JSO). Furthermore, the convergence rate of OBJSO is improved tremendously, where up to 13.6% reduction in average can be achieved compared with JSO. In conclusion, the proposed technique can be used to help engineers in the automotive industry design a high-performance IM for EVs as an alternative to the existing motor.

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Din, Maiya, Saibal K. Pal, S. K. Muttoo, and Sushila Madan. "A Hybrid Computational Intelligence based Technique for Automatic Cryptanalysis of Playfair Ciphers." Defence Science Journal 70, no. 6 (October 12, 2020): 612–18. http://dx.doi.org/10.14429/dsj.70.15749.

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The Playfair cipher is a symmetric key cryptosystem-based on encryption of digrams of letters. The cipher shows higher cryptanalytic complexity compared to mono-alphabetic cipher due to the use of 625 different letter-digrams in encryption instead of 26 letters from Roman alphabets. Population-based techniques like Genetic algorithm (GA) and Swarm intelligence (SI) are more suitable compared to the Brute force approach for cryptanalysis of cipher because of specific and unique structure of its Key Table. This work is an attempt to automate the process of cryptanalysis using hybrid computational intelligence. Multiple particle swarm optimization (MPSO) and GA-based hybrid technique (MPSO-GA) have been proposed and applied in solving Playfair ciphers. The authors have attempted to find the solution key applied in generating Playfair crypts by using the proposed hybrid technique to reduce the exhaustive search space. As per the computed results of the MPSO-GA technique, correct solution was obtained for the Playfair ciphers of 100 to 200 letters length. The proposed technique provided better results compared to either GA or PSO-based technique. Furthermore, the technique was also able to recover partial English text message for short Playfair ciphers of 80 to 120 characters length.

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Jia, Rui Yu, Zhao Hong Jia, and Jin Wei Geng. "Case-Based Reasoning Based on Good-Point-Set Genetic Algorithm and its Application." Advanced Materials Research 217-218 (March 2011): 886–92. http://dx.doi.org/10.4028/www.scientific.net/amr.217-218.886.

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This paper discusses two intelligent learning methods—Genetic Algorithms (GA) and Case- based Reasoning (CBR), and analyses the shortcomings of the current techniques of CBR. The new idea is put forward by using the GA and good-point-set to solve the problems of CBR. An improved GA and Good-point-set GA are quoted here. We have applied the two GAs to assign the weights automatically of attributes in CBR systems.

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Bektas, Enes, and Hulusi Karaca. "GA Based Selective Harmonic Elimination for Multilevel Inverter with Reduced Number of Switches: An Experimental Study." Elektronika ir Elektrotechnika 25, no. 3 (June 25, 2019): 10–17. http://dx.doi.org/10.5755/j01.eie.25.3.23670.

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In power electronic applications, especially high power and medium voltage, multilevel inverters (MLIs) have been commonly used. MLIs ensure high quality load voltage and lower Total Harmonic Distortion (THD) than traditional inverter. In this paper, a multilevel inverter structure with reduced number of power switches is proposed. The proposed multilevel inverter is lower costed than conventional MLI. Also, a Genetic Algorithm (GA) based Selective Harmonic Elimination (SHE) technique has been used for the first time in the proposed MLI structure with reduced number of switches. The proposed GA based SHE technique computes the optimum switching angles by solving nonlinear harmonic equations of multilevel inverter. Both Isochronous switching (IS) and SHE techniques have been applied to proposed MLI to demonstrate the effectiveness of the GA based SHE technique. Simulation and experimental results for 7, 11 and 13-level have been obtained. Results of 11-level inverter is analysed and given in detail. Results have clearly proved that desired order harmonics in proposed MLI topology can be eliminated by using GA based SHE technique and lower THD on the load voltage has been provided.

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Sakakura, Yoshiaki, Noriyuki Taniguchi, Yukinobu Hoshino, and Katsuari Kamei. "A Proposal of GA Using Symbiotic Evolutionary Viruses and its Virus Evaluation Techniques." Journal of Advanced Computational Intelligence and Intelligent Informatics 8, no. 4 (July 20, 2004): 421–25. http://dx.doi.org/10.20965/jaciii.2004.p0421.

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In this paper, we propose a Genetic Algorithm (GA) using symbiotic evolutionary viruses based on both the building block hypothesis and the virus theory of evolution. The proposed GA aims to control the destruction of building blocks by discovery, maintenance, and propagation of building blocks based on virus operation. We prepare a group of individuals and a group of viruses. The group of individuals searches for solutions and the group of viruses searches for building blocks. These searches are done based on the symbiotic relation of both groups. Also, our GA has two types of virus evaluation technique. In one type, each virus is evaluated by the difference of the fitness of an individual before and after virus infection. In the other type, all viruses are evaluated by the difference of the fitness of an individual before and after infection of all viruses. We applied the proposed GA to a minimum value search problem using a test function with some local minima far from the optimum. Finally, we also discuss the search behavior in the proposed GA based on each virus evaluation technique.

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Ahmed S. Al-Abdulwahab, Ahmed S. Al-Abdulwahab. "Generating System Wellbeing Index Evaluation Using Genetic Algorithm." journal of King Abdulaziz University Engineering Sciences 23, no. 2 (February 4, 2012): 37–53. http://dx.doi.org/10.4197/eng.23-2.3.

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Reliability assessment of generation system is a crucial task used to be done using deterministic approaches. However, due to the practical limitations of these approaches, they have been gradually replaced by probabilistic techniques. Nevertheless, there is a considerable reluctance in many electric power utilities to completely abandon deterministic considerations. To fulfill the industry need, wellbeing analysis has been developed to combine the deterministic and the probabilistic approaches in a single framework. Analytical techniques or Monte Carlo Simulation have been used to evaluate wellbeing indices. However, analytical approaches are complicated and mathematically demanding and simulation technique requires a huge amount of computing time, and large memory size. This still prevents the utilities to benefit from the wellbeing framework. This paper presents a novel Genetic Algorithm (GA) based technique to calculate the wellbeing indices. Hopefully, this will encourage the industry to benefit from the wellbeing analysis. The features of the GA are utilized to collect and identify the health, marginal and at risk wellbeing states and to calculate the associated wellbeing indices. The proposed technique is applied to the IEEE-RBTS and the resulting wellbeing indices are compared to those obtained using a conventional analytical technique. The results show that the outcome of both techniques is virtually identical. The effect of the GA parameters on the wellbeing indices is examined. The proposed GA based technique in the manner applied in this study is simple, practical and valid to calculate the wellbeing indices.

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Muhammad Ajmal, Aidha, Vigna K. Ramachandaramurthy, Amirreza Naderipour, and Janaka B. Ekanayake. "Comparative analysis of two-step GA-based PV array reconfiguration technique and other reconfiguration techniques." Energy Conversion and Management 230 (February 2021): 113806. http://dx.doi.org/10.1016/j.enconman.2020.113806.

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Hassan, Azmi, Muhammad Ridwan Andi Purnomo, and Putri Dwi Annisa. "Clustering Using Genetic Algorithm-Based Self-Organising Map." Advanced Materials Research 1115 (July 2015): 573–77. http://dx.doi.org/10.4028/www.scientific.net/amr.1115.573.

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This paper presents a comparative study of clustering using Artificial Intelligence (AI) techniques. There are 3 methods to be compared, two methods are pure method, called Self Organising Map (SOM) which is branch of Artificial Neural Network (ANN) and Genetic Algorithm (GA), while one method is hybrid between GA and SOM, called GA-based SOM. SOM is one of the most popular method for cluster analysis. SOM will group objects based on the nearest distance between object and updateable cluster centres. However, there are disadvantages of SOM. Solution quality is depend on initial cluster centres that are generated randomly and cluster centres update algorithm is just based on a delta value without considering the searching direction. Basically, clustering case could be modelled as optimisation case. The objective function is to minimise total distance of all data to their cluster centre. Hence, GA has potentiality to be applied for clustering. Advantage of GA is it has multi searching points in finding the solution and stochastic movement from a phase to the next phase. Therefore, possibility of GA to find global optimum solution will be higher. However, there is still some possibility of GA just find near-optimum solution. The advantage of SOM is the smooth iterative procedure to improve existing cluster centres. Hybridisation of GA and SOM believed could provide better solution. In this study, there are 2 data sets used to test the performance of the three techniques. The study shows that when the solution domain is very wide then SOM and GA-based SOM perform better compared to GA while when the solution domain is not very wide then GA performs better.

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Suraj, Purnendu Tiwari, Subhojit Ghosh, and Rakesh Kumar Sinha. "Classification of Two Class Motor Imagery Tasks Using Hybrid GA-PSO BasedK-Means Clustering." Computational Intelligence and Neuroscience 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/945729.

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Transferring the brain computer interface (BCI) from laboratory condition to meet the real world application needs BCI to be applied asynchronously without any time constraint. High level of dynamism in the electroencephalogram (EEG) signal reasons us to look toward evolutionary algorithm (EA). Motivated by these two facts, in this work a hybrid GA-PSO basedK-means clustering technique has been used to distinguish two class motor imagery (MI) tasks. The proposed hybrid GA-PSO basedK-means clustering is found to outperform genetic algorithm (GA) and particle swarm optimization (PSO) basedK-means clustering techniques in terms of both accuracy and execution time. The lesser execution time of hybrid GA-PSO technique makes it suitable for real time BCI application. Time frequency representation (TFR) techniques have been used to extract the feature of the signal under investigation. TFRs based features are extracted and relying on the concept of event related synchronization (ERD) and desynchronization (ERD) feature vector is formed.

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Mawla, Noura A., and Hussein K. Khafaji. "Protein Motifs to Hide GA-Based Encrypted Data." Scientific Programming 2022 (September 25, 2022): 1–14. http://dx.doi.org/10.1155/2022/1846788.

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The arms of the Internet octopus have reached the ends of the planet. As it has become indispensable in our daily lives, huge amounts of information are transmitted through this network, and it is growing momentarily, which has led to an increase in the number of attacks on this information. Keeping the security of this information has become a necessity today. Therefore, the scientists of cryptography and steganography have seen a great and rapid development in the previous years to the present day, where various security and protection techniques have been used in these two technologies. In this research, it was emphasized to secure the confidentiality and security of the transmitted data between the sending and receiving parties by using both techniques of encryption and steganography. In contrast, where genetic algorithms and logic gates are exploited in an encryption process, in an unprecedented approach, protein motifs are used to mask the encoded message, gaining more dispersion because there are 20 bases used to represent the protein. The real payload gained ranges between 0.8 and 2.666, which outperforms the algorithms that depend on DNA sequences.

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Mhadhbi, Imene, Slim Ben Othman, and Slim Ben Saoud. "An Efficient Technique for Hardware/Software Partitioning Process in Codesign." Scientific Programming 2016 (2016): 1–11. http://dx.doi.org/10.1155/2016/6382765.

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Codesign methodology deals with the problem of designing complex embedded systems, where automatic hardware/software partitioning is one key issue. The research efforts in this issue are focused on exploring new automatic partitioning methods which consider only binary or extended partitioning problems. The main contribution of this paper is to propose a hybrid FCMPSO partitioning technique, based on Fuzzy C-Means (FCM) and Particle Swarm Optimization (PSO) algorithms suitable for mapping embedded applications for both binary and multicores target architecture. Our FCMPSO optimization technique has been compared using different graphical models with a large number of instances. Performance analysis reveals that FCMPSO outperforms PSO algorithm as well as the Genetic Algorithm (GA), Simulated Annealing (SA), Ant Colony Optimization (ACO), and FCM standard metaheuristic based techniques and also hybrid solutions including PSO then GA, GA then SA, GA then ACO, ACO then SA, FCM then GA, FCM then SA, and finally ACO followed by FCM.

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Paul, Victer, Ganeshkumar C, and Jayakumar L. "Performance Evaluation of Population Seeding Techniques of Permutation-Coded GA Traveling Salesman Problems Based Assessment." International Journal of Applied Metaheuristic Computing 10, no. 2 (April 2019): 55–92. http://dx.doi.org/10.4018/ijamc.2019040103.

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Genetic algorithms (GAs) are a population-based meta-heuristic global optimization technique for dealing with complex problems with a very large search space. The population initialization is a crucial task in GAs because it plays a vital role in the convergence speed, problem search space exploration, and also the quality of the final optimal solution. Though the importance of deciding problem-specific population initialization in GA is widely recognized, it is hardly addressed in the literature. In this article, different population seeding techniques for permutation-coded genetic algorithms such as random, nearest neighbor (NN), gene bank (GB), sorted population (SP), and selective initialization (SI), along with three newly proposed ordered-distance-vector-based initialization techniques have been extensively studied. The ability of each population seeding technique has been examined in terms of a set of performance criteria, such as computation time, convergence rate, error rate, average convergence, convergence diversity, nearest-neighbor ratio, average distinct solutions and distribution of individuals. One of the famous combinatorial hard problems of the traveling salesman problem (TSP) is being chosen as the testbed and the experiments are performed on large-sized benchmark TSP instances obtained from standard TSPLIB. The scope of the experiments in this article is limited to the initialization phase of the GA and this restricted scope helps to assess the performance of the population seeding techniques in their intended phase alone. The experimentation analyses are carried out using statistical tools to claim the unique performance characteristic of each population seeding techniques and best performing techniques are identified based on the assessment criteria defined and the nature of the application.

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Feroz Mirza, Adeel, Majad Mansoor, Qiang Ling, Muhammad Imran Khan, and Omar M. Aldossary. "Advanced Variable Step Size Incremental Conductance MPPT for a Standalone PV System Utilizing a GA-Tuned PID Controller." Energies 13, no. 16 (August 11, 2020): 4153. http://dx.doi.org/10.3390/en13164153.

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In this article, a novel maximum power point tracking (MPPT) controller for the fast-changing irradiance of photovoltaic (PV) systems is introduced. Our technique utilizes a modified incremental conductance (IC) algorithm for the efficient and fast tracking of MPP. The proposed system has a simple implementation, fast tracking, and achieved steady-state oscillation. Traditional MPPT techniques use a tradeoff between steady-state and transition-state parameters. The shortfalls of various techniques are studied. A comprehensive comparative study is done to test various existing techniques against the proposed technique. The common parameters discussed in this study are fast convergence, efficiency, and reduced oscillations. The proposed method successfully addresses these issues and improves the results significantly by using a proportional integral deferential (PID) controller with a genetic algorithm (GA) to predict the variable step size of the IC-based MPPT technique. The system is designed and tested against the perturbation and observation (P&O)-based MPPT technique. Our technique effectively detects global maxima (GM) for fast-changing irradiance due to the adopted GA-based tuning of the controller. A comparative analysis of the results proves the superior performance and capabilities to track GM in fewer iterations.

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Abdolrasol, Maher G. M., S. M. Suhail Hussain, Taha Selim Ustun, Mahidur R. Sarker, Mahammad A. Hannan, Ramizi Mohamed, Jamal Abd Ali, Saad Mekhilef, and Abdalrhman Milad. "Artificial Neural Networks Based Optimization Techniques: A Review." Electronics 10, no. 21 (November 3, 2021): 2689. http://dx.doi.org/10.3390/electronics10212689.

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In the last few years, intensive research has been done to enhance artificial intelligence (AI) using optimization techniques. In this paper, we present an extensive review of artificial neural networks (ANNs) based optimization algorithm techniques with some of the famous optimization techniques, e.g., genetic algorithm (GA), particle swarm optimization (PSO), artificial bee colony (ABC), and backtracking search algorithm (BSA) and some modern developed techniques, e.g., the lightning search algorithm (LSA) and whale optimization algorithm (WOA), and many more. The entire set of such techniques is classified as algorithms based on a population where the initial population is randomly created. Input parameters are initialized within the specified range, and they can provide optimal solutions. This paper emphasizes enhancing the neural network via optimization algorithms by manipulating its tuned parameters or training parameters to obtain the best structure network pattern to dissolve the problems in the best way. This paper includes some results for improving the ANN performance by PSO, GA, ABC, and BSA optimization techniques, respectively, to search for optimal parameters, e.g., the number of neurons in the hidden layers and learning rate. The obtained neural net is used for solving energy management problems in the virtual power plant system.

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Mishra, Annapurna, and Satchidananda Dehuri. "Real-time online fingerprint image classification using adaptive hybrid techniques." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 5 (October 1, 2019): 4372. http://dx.doi.org/10.11591/ijece.v9i5.pp4372-4381.

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<p class="Abstract">This paper presents three different hybrid classification techniques applied for the first time in real-time online fingerprint classification. Classification of online real time fingerprints is a complex task as it involves adaptation and tuning of classifier parameters for better classification accuracy. To accomplish the optimal adaptation of parameters of functional link artificial neural network (FLANN) for real-time online fingerprint classification, proven and established optimizers, such as Biogeography based optimizer (BBO), Genetic algorithm (GA), and Particle swarm optimizer (PSO) are intelligently infused with it to form hybrid classifiers. The global features of the real-time fingerprints are extracted using a Gabor filter-bank and then passed into adaptive hybrid classifiers for the desired classification as per the Henry system. Three hybrid classifiers, the optimized weight adapted Biogeography based optimized functional link artificial neural network (BBO-FLANN), Genetic algorithm based functional link artificial neural network (GA-FLANN) and Particle swarm optimized functional link artificial neural network (PSO-FLANN), are explored for real-time online fingerprint classification, where the PSO-FLANN technique is showing superior performance as compared to GA-FLANN and BBO-FLANN techniques. The best accuracy observed by the application of PSO-FLANN, is 98% for real-time online fingerprint classification.</p>

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Banda, Santhoshini, U. Sri Lakshmi, and P. Victer Paul. "A Future Perspective Survey on Bio-inspired algorithms based self-organization techniques for GA." International Journal of Engineering & Technology 7, no. 4.6 (September 25, 2018): 4. http://dx.doi.org/10.14419/ijet.v7i4.6.20222.

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Genetic algorithms (GAs) are the most important evolutionary computation technique that is used to solve various complex problems that involve a large search space. To have a performance improvement over GA the concept of Hybrid genetic algorithms that were inspired by the biological behavior of different living beings was put to use to solve the NP-completeness problems. In this paper, a survey on the various recent working HGA with bio-inspired algorithms that exhibits self-organization behavior is performed. This paper discusses the various Biological self-organization behaviors and the generalized self-organization behaviors that are used to solve combinatorial optimization problems. This paper helps the scholars and researchers to have a better understanding on the bio-inspired based self-organization techniques for Genetic algorithm so that they can formulate new algorithms based on existing SO techniques.

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Venu Gopal, B. T., and E. G. Shivakumar. "A Comparative Performance Analysis of Indirect Vector Controlled Induction Motor Drive Using Optimized AI Techniques." Journal of Computational and Theoretical Nanoscience 17, no. 1 (January 1, 2020): 464–72. http://dx.doi.org/10.1166/jctn.2020.8692.

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This paper exhibits a point by point comparison between Neuro Fuzzy and Genetic Algorithm GA based control systems of Induction Motor drive, underlining favorable circumstances and drawbacks. Industries are advancing and upgrading generation line to enhance efficiency and quality. Induction machines are considered by nonlinear, time varying dynamics, inaccessibility of few states and thus can be considered as a challenging issue. In this paper, a novel method using modified GA is presented to limit electric losses of Induction Motor and it is compared with Neuro Fuzzy Controller. GA is a subordinate of AI, whose principle relies upon Darwin’s theory—struggle for existence and the survival of the fittest. The technique for deciding the gain parameters of PI controller utilizing GA whose output is utilized to control the torque applied to the Induction Motor in this way controlling its speed. The gains of PI controller are improved with the assistance of GA to upgrade the performance of IM drive. The results are simulated in MATLAB Simulink and are related with the conventional PI controller and Adaptive Neuro Fuzzy controller (NFC). NFC is less complicated and gives great speed precision yet GA based PI controller produces significantly reduced torque and speed ripples compared with other controllers, in this way limiting losses in IM drives.

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ROMERO, Julian A., Luis A. DIAGO, Junichi SHINODA, and Ichiro HAGIWARA. "Scale House-Model Construction by GA-Based Polygon Matching and Origami Techniques." Proceedings of the Dynamics & Design Conference 2017 (2017): 805. http://dx.doi.org/10.1299/jsmedmc.2017.805.

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Kumar, Amarjeet, Vijay Kumar Singh, Bhagwat Saran, Nadhir Al-Ansari, Vinay Pratap Singh, Sneha Adhikari, Anjali Joshi, Narendra Kumar Singh, and Dinesh Kumar Vishwakarma. "Development of Novel Hybrid Models for Prediction of Drought- and Stress-Tolerance Indices in Teosinte Introgressed Maize Lines Using Artificial Intelligence Techniques." Sustainability 14, no. 4 (February 17, 2022): 2287. http://dx.doi.org/10.3390/su14042287.

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Maize (Zea mays subsp. mays) is a staple food crop in the world. Drought is one of the most common abiotic challenges that maize faces when it comes to growth, development, and production. Further knowledge of drought tolerance could aid with maize production. However, there has been less study focused on investigating in depth the drought tolerance of inbred maize lines using artificial intelligence techniques. In this study, multi-layer perceptron (MLP), support vector machine (SVM), genetic algorithm-based multi-layer perceptron (MLP-GA), and genetic algorithm-based support vector machine (SVM-GA) hybrid artificial intelligence algorithms were used for the prediction of drought tolerance and stress tolerance indices in teosinte maize lines. Correspondingly, the gamma test technique was applied to determine efficient input and output vectors. The potential of the developed models was evaluated based on statistical indices and graphical representations. The results of the gamma test based on the least value of gamma and standard error indices show that days of anthesis (DOA), days of silking (DOS), yield index (YI), and gross yield per plant (GYP) information vector arrangements were determined to be an efficient information vector combination for the drought-tolerance index (DTI) as well as the stress-tolerance index (STI). The MLP, SVM, MLP-GA, and SVM-GA algorithms’ results were compared based on statistical indices and visual interpretations that have satisfactorily predict the drought-tolerance index and stress-tolerance index in maize crops. The genetic algorithm-based hybrid models (MLP-GA and SVM-GA) were found to better predict the drought-tolerance index and stress-tolerance index in maize crops. Similarly, the SVM-GA model was found to have the highest potential to forecast the DTI and STI in maize crops, compared to the MLP, SVM, and MLP-GA models.

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YING, WEIQIN, YUANXIANG LI, and PHILLIP C. Y. SHEU. "A GA-BASED APPROACH TO OPTIMIZING COMBINATIONAL QUERIES IN SCDL." International Journal of Semantic Computing 02, no. 02 (June 2008): 273–89. http://dx.doi.org/10.1142/s1793351x08000464.

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The semantic capability description language (SCDL) allows users to describe combinatorial optimization problems declaratively. We employ genetic algorithms (GA) and penalty techniques to process unconstrained SCDL queries and singly constrained SCDL queries, and determine a "one-to-one" mapping between queries and algorithms. Based on such, we develop a GA-based "one-to-many" mapping to process and integrate multi-constrained SCDL queries.

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ABO-HAMMOUR, ZÁER S., OTHMAN M. K. ALSMADI, and ADNAN M. AL-SMADI. "MULTI-TIME-SCALE SYSTEMS MODEL ORDER REDUCTION VIA GENETIC ALGORITHMS WITH EIGENVALUE PRESERVATION." Journal of Circuits, Systems and Computers 20, no. 07 (November 2011): 1403–18. http://dx.doi.org/10.1142/s0218126611007943.

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A novel substructure (dominant eigenvalue) preserving genetic algorithm approach for model order reduction (MOR) of multi-time-scale systems is presented in this paper. The new technique is formulated based on genetic algorithms (GAs), sub-optimization and estimation. The GA predicts the elements of an upper triangular matrix form of the system state matrix A, defined in state space representation along with the elements of B, C, and D matrices. The GA procedure is based on maximizing the fitness function corresponding to the reciprocal response deviation between the full order model and the reduced order model. The proposed GA model order reduction method is compared to well-known reduction techniques such as the Balanced Schur Decomposition (BSD), proper orthogonal decomposition (POD), and state elimination through balanced realization. Simulation results validate the robustness of the new technique for MOR with eigenvalue preservation.

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Lau, T. L., and E. P. K. Tsang. "Solving the Processor Configuration Problems with a Mutation-Based Genetic Algorithm." International Journal on Artificial Intelligence Tools 06, no. 04 (December 1997): 567–85. http://dx.doi.org/10.1142/s0218213097000281.

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The Processor Configuration Problem (PCP) is a real life Constraint Optimization Problem. The task is to link up a finite set of processors into a network, whilst minimizing the maximum distance between these processors. Since each processor has a limited number of communication channels, a carefully planned layout will help reduce the overhead for message switching. In this paper, we present a Genetic Algorithm (GA) approach to the PCP. Our technique uses a mutation-based GA, a function that produces schemata by analyzing previous solutions, and an efficient data representation. Our approach has been shown to out-perform other published techniques in this problem.

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Mirhosseini, Hossein, Ramya Kormath Madam Raghupathy, Sudhir K. Sahoo, Hendrik Wiebeler, Manjusha Chugh, and Thomas D. Kühne. "In silico investigation of Cu(In,Ga)Se2-based solar cells." Physical Chemistry Chemical Physics 22, no. 46 (2020): 26682–701. http://dx.doi.org/10.1039/d0cp04712k.

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D'Angelo, Donna J., Judy L. Meyer, Leslie M. Howard, Stanley V. Gregory, and Linda R. Ashkenas. "Ecological uses for genetic algorithms: predicting fish distributions in complex physical habitats." Canadian Journal of Fisheries and Aquatic Sciences 52, no. 9 (September 1, 1995): 1893–908. http://dx.doi.org/10.1139/f95-782.

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Genetic algorithms (GA) are artificial intelligence techniques based on the theory of evolution that through the process of natural selection evolve formulae to solve problems or develop control strategies. We designed a GA to examine relationships between stream physical characteristics and trout distribution data for 3rd-, 5th-, and 7th-order stream sites in the Cascade Mountains, Oregon. Although traditional multivariate statistical techniques can perform this particular task, GAs are not constrained by assumptions of independence and linearity and therefore provide a useful alternative. To help gauge the effectiveness of the GA, we compared GA results with results from proportional trout distributions and multiple linear regression equations. The GA was a more effective predictor of trout distributions (paired t test, P < 0.05) than other methods and also provided new insights into relationships between stream geomorphology and trout distributions. Most importantly, GA equations emphasized the nonindependence of stream channel units by revealing that (i) the factors that influence trout distributions change along a downstream continuum, and (ii) channel unit sequence can be critical. Superior performance of the GA, along with the new information it provided, indicates that genetic algorithms may provide a useful alternative or supportive method to statistical techniques.

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Sabri, Norlina M., Mazidah Puteh, and Mohamad Rusop Mahmood. "Utilization of Soft Computing Techniques in Sputtering Processes: A Review." Advanced Materials Research 832 (November 2013): 260–65. http://dx.doi.org/10.4028/www.scientific.net/amr.832.260.

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This paper presents an overview of research works on the utilizing of soft computing in the optimization of process parameters and in the prediction of thin film properties in sputtering processes. The papers from this review were obtained from relevant databases and from various scientific journals. The papers collected were published from 2008 to 2012. The focus of the review is to provide an outlook on the utilization of soft computing techniques in sputtering processes. Based on the review, the soft computing techniques which have been applied so far are ANN, GA and Fuzzy Logic. The first finding of this review is that soft computing technique is a promising and more reliable approach to optimize and predict process parameters compared to the traditional methods. The second finding is that the utilizing of soft computing techniques in sputtering processes are still limited and still in exploratory phase as they have not yet been extensively and stably applied. The techniques applied are also limited to ANN, GA and Fuzzy, whereas the exploration into other techniques is also necessary to be conducted in order to seek the most reliable technique and so as to expand the application of soft computing approach. Future research could focus on the exploration of other soft computing techniques for optimization in order to find the best optimization techniques based on the specific processes.

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Gupta, Sandeep, and Ramesh Kumar Tripathi. "Optimal LQR controller in CSC based STATCOM using GA and PSO." Archives of Electrical Engineering 63, no. 3 (September 1, 2014): 469–87. http://dx.doi.org/10.2478/aee-2014-0034.

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Abstract The static synchronous compensator (STATCOM) is the multipurpose FACTS device with the multiple input and multiple output system for the enhancement of its dynamic performance in power system. Based on artificial intelligence (AI) optimization technique, a novel controller is proposed for CSC based STATCOM. In this paper, the CSC based STATCOM is controlled by the LQR. But the best constant values for LQR controller's parameters are obtained laboriously through trial and error method, although time consuming. So the goal of this paper is to investigate the ability of AI techniques such as genetic algorithm (GA) and particle swarm optimization (PSO) methods to search the best values of LQR controller's parameters in a very short time with the desired criterion for the test system. Performances of the GA, PSO & ABC based LQR controllers are also compared. Applicability of the proposed scheme is demonstrated through simulation in MATLAB and the simulation results are shown an improvement in the input-output response of CSC-STATCOM

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Meena, Ritu, and Kamal K. Bharadwaj. "A Genetic Algorithm Approach for Group Recommender System Based on Partial Rankings." Journal of Intelligent Systems 29, no. 1 (June 20, 2018): 653–63. http://dx.doi.org/10.1515/jisys-2017-0561.

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Abstract Many recommender systems frequently make suggestions for group consumable items to the individual users. There has been much work done in group recommender systems (GRSs) with full ranking, but partial ranking (PR) where items are partially ranked still remains a challenge. The ultimate objective of this work is to propose rank aggregation technique for effectively handling the PR problem. Additionally, in real applications, most of the studies have focused on PR without ties (PRWOT). However, the rankings may have ties where some items are placed in the same position, but where some items are partially ranked to be aggregated may not be permutations. In this work, in order to handle problem of PR in GRS for PRWOT and PR with ties (PRWT), we propose a novel approach to GRS based on genetic algorithm (GA) where for PRWOT Spearman foot rule distance and for PRWT Kendall tau distance with bucket order are used as fitness functions. Experimental results are presented that clearly demonstrate that our proposed GRS based on GA for PRWOT (GRS-GA-PRWOT) and PRWT (GRS-GA-PRWT) outperforms well-known baseline GRS techniques.

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Kenjrawy, Hassan, Carlo Makdisie, Issam Housamo, and Hassan Haes Alhelou. "MPPT for Photovoltaic System Located in Latakia Province - Syria by Using Genetic Algorithm." Current World Environment 14, no. 1 (April 25, 2019): 170–81. http://dx.doi.org/10.12944/cwe.14.1.16.

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The use of renewable resources for energizing the modern power systems has recently increased due to its sustainability and low operating costs. Photovoltaic (PV) system appears to be a good solution due to the fact that it can be established and operated locally. However, the maximum output power of these systems is usually achieved by using the maximum sun and power point (MPP) tracking techniques. This paper suggests a novel genetic algorithm (GA)-based technique to obtain the maximum output power of practical PV system located in the Latakia province of Syria. Based on this technique, azimuth and elevation angles of solar panels located in Latakia are first determined to track maximum radiation of the sun for every day of the whole year. After that, a GA-based technique is developed to track the maximum power point corresponding to maximum radiation during the year. Simulation results in MATLAB environment demonstrate the validation and effectiveness of the proposed GA-based technique to obtain the maximum generated power of the PV system. The results of this research can be easily adopted as a database reference to design the PV control system.

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Almaliki, Salim, and Nasim Monjezi. "Using new computer based techniques to optimise energy consumption in agricultural land levelling." Research in Agricultural Engineering 67, No. 4 (December 17, 2021): 149–63. http://dx.doi.org/10.17221/20/2021-rae.

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Land levelling is one of the most energy-demanding steps in soil preparation. There are many limiting factors for a specific land levelling operation, such as fertile topsoil conservation, limited allowed slope, specific cut to fill ratio, etc. These limitations make optimisation problems of land levelling even more complicated. In this research, three computational and evolutionary methods including ICA, PSO, GA along with MLS were utilised as optimisation methods to minimise the soil cut and fill volumes and to determine the preferred levelling plane. The results indicated that ICA had the most efficient solution for the energy optimisation in the land levelling among the other investigated methods by saving 29% (17 GJ) of the total energy consumption compared with MLS. This study deals with optimising the energy consumption during land levelling projects using new computer-based techniques and compares them to the MLS method as a benchmark. All in all, ICA, PSO, and GA performed much better than MLS by saving 29, 17, and 10% of the total energy consumption in their best model (number 1 models), respectively. Nonetheless, with these great capacities for saving energy in developing countries, unfortunately, the lack of education and excess subsidies on fossil fuels nullify these potentials.

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Al-Shamma’a, Abdullrahman A., Hassan M. Hussein Farh, Abdullah M. Noman, Abdullah M. Al-Shaalan, and Abdulaziz Alkuhayli. "Optimal Sizing of a Hybrid Renewable Photovoltaic-Wind System-Based Microgrid Using Harris Hawk Optimizer." International Journal of Photoenergy 2022 (June 23, 2022): 1–13. http://dx.doi.org/10.1155/2022/4825411.

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Hybrid renewable energy microgrid has become an attractive solution to electrify urban areas. This research proposes a microgrid design problem including photovoltaic (PV) arrays, wind turbine, diesel, and batteries for which Harris hawk optimization (HHO), a metaheuristic technique, is applied. Based on a long-term techno-economic assessment, the HHO approach is used to determine the best hybrid microgrid size for a community in Saudi Arabia’s northern region. The efficacy of HHO is investigated, and its performance was compared with seven metaheuristic techniques, grasshopper optimization algorithm (GOA), cuckoo search optimizer (CSO), genetic algorithm (GA), Big Bang–Big Crunch (BBBC), coyote optimizer, crow search, and butterfly optimization algorithm (BOA), to attain the HRE microgrid optimal sizing based on annualized system cost (ASC) reduction. Some benchmarks (optimum and worst solutions, mean, median, standard deviation, and rate of convergence) are used to distinguish and analyze the performance of these eight metaheuristic-based approaches. The HHO surpassed the other seven metaheuristic techniques in achieving the best HRE microgrid solution with the lowest ASC (USD 149229.9) followed by GOA (USD 149380.5) and CSO (USD 149382.5). The findings revealed that the HHO, GOA, CSO, and coyote have acceptable performance in terms of capturing the global solution and the speed of convergence, with only minimal oscillations. The BBBC, crow search, GA, and BA, on the other hand, have unacceptably poor performance, trapping to the local solution, oscillations, and a long convergence time. In terms of optimal solution and convergence rate, the BBBC and GA both perform poorly when compared to the other metaheuristic techniques.

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Wang, Yanhua, Xiyu Liu, and Laisheng Xiang. "GA-Based Membrane Evolutionary Algorithm for Ensemble Clustering." Computational Intelligence and Neuroscience 2017 (2017): 1–11. http://dx.doi.org/10.1155/2017/4367342.

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Ensemble clustering can improve the generalization ability of a single clustering algorithm and generate a more robust clustering result by integrating multiple base clusterings, so it becomes the focus of current clustering research. Ensemble clustering aims at finding a consensus partition which agrees as much as possible with base clusterings. Genetic algorithm is a highly parallel, stochastic, and adaptive search algorithm developed from the natural selection and evolutionary mechanism of biology. In this paper, an improved genetic algorithm is designed by improving the coding of chromosome. A new membrane evolutionary algorithm is constructed by using genetic mechanisms as evolution rules and combines with the communication mechanism of cell-like P system. The proposed algorithm is used to optimize the base clusterings and find the optimal chromosome as the final ensemble clustering result. The global optimization ability of the genetic algorithm and the rapid convergence of the membrane system make membrane evolutionary algorithm perform better than several state-of-the-art techniques on six real-world UCI data sets.

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Roeva, Olympia, Dafina Zoteva, and Velislava Lyubenova. "Escherichia coli Cultivation Process Modelling Using ABC-GA Hybrid Algorithm." Processes 9, no. 8 (August 16, 2021): 1418. http://dx.doi.org/10.3390/pr9081418.

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In this paper, the artificial bee colony (ABC) algorithm is hybridized with the genetic algorithm (GA) for a model parameter identification problem. When dealing with real-world and large-scale problems, it becomes evident that concentrating on a sole metaheuristic algorithm is somewhat restrictive. A skilled combination between metaheuristics or other optimization techniques, a so-called hybrid metaheuristic, can provide more efficient behavior and greater flexibility. Hybrid metaheuristics combine the advantages of one algorithm with the strengths of another. ABC, based on the foraging behavior of honey bees, and GA, based on the mechanics of nature selection, are among the most efficient biologically inspired population-based algorithms. The performance of the proposed ABC-GA hybrid algorithm is examined, including classic benchmark test functions. To demonstrate the effectiveness of ABC-GA for a real-world problem, parameter identification of an Escherichia coli MC4110 fed-batch cultivation process model is considered. The computational results of the designed algorithm are compared to the results of different hybridized biologically inspired techniques (ant colony optimization (ACO) and firefly algorithm (FA))—hybrid algorithms as ACO-GA, GA-ACO and ACO-FA. The algorithms are applied to the same problems—a set of benchmark test functions and the real nonlinear optimization problem. Taking into account the overall searchability and computational efficiency, the results clearly show that the proposed ABC–GA algorithm outperforms the considered hybrid algorithms.

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Coello Coello, Carlos A., Alan D. Christiansen, and Arturo Hernández Aguirre. "Using a new GA-based multiobjective optimization technique for the design of robot arms." Robotica 16, no. 4 (July 1998): 401–14. http://dx.doi.org/10.1017/s0263574798000034.

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This paper presents a hybrid approach to optimize the counterweight balancing of a robot arm. A new technique that combines an artificial intelligence technique called the genetic algorithm (GA) and the weighted min-max multiobjective optimization method is proposed. These techniques are included in a system developed by the authors, called MOSES, which is intended to be used as a tool for engineering design optimization. The results presented here show how the new proposed technique can get better trade-off solutions and a more accurate Pareto front for this highly non-convex problem using an ad-hoc floating point representation and traditional genetic operators. Finally, a methodology to compute the ideal vector using a genetic algorithm is presented. It is shown how with a very simple dynamic approach to adjust the parameters of the GA, it is possible to obtain better results than those previously reported in the literature for this problem.

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Ding, Lian, Yong Yue *, Kemal Ahmet, Mike Jackson, and Robert Parkin. "Global optimization of a feature-based process sequence using GA and ANN techniques." International Journal of Production Research 43, no. 15 (August 2005): 3247–72. http://dx.doi.org/10.1080/00207540500137282.

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Prakash, Prem, Duli Chand Meena, Hasmat Malik, Majed A. Alotaibi, and Irfan Ahmad Khan. "A Novel Hybrid Approach for Optimal Placement of Non-Dispatchable Distributed Generations in Radial Distribution System." Mathematics 9, no. 24 (December 9, 2021): 3171. http://dx.doi.org/10.3390/math9243171.

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The objective of the present paper is to study the optimum installation of Non-dispatchable Distributed Generations (NDG) in the distribution network of given sizes under the given scheme. The uncertainty of various random (uncertain) parameters like load, wind and solar operated DG besides uncertainty of fuel prices has been investigated by the three-point estimate method (3-PEM) and Monte Carlo Simulation (MCS) based methods. Nearly twenty percent of the total number of buses are selected as candidate buses for NDG placement on the basis of system voltage profile to limit the search space. Weibull probability density function (PDF) is considered to address uncertain characteristics of solar radiation and wind speed under different scenarios. Load uncertainty is described by Standard Normal Distribution Function (SNDF). To investigate the solution of optimal probabilistic load flow (OPLF) three-point PEM-based technique was applied. For optimization, Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and GA-PSO hybrid-based Artificial Intelligent (AI) based optimization techniques are employed to achieve the optimum value of the multi-objectives function. The proposed multi-objective function comprises loss and different costs. The proposed methods have been applied to IEEE 33- bus radial distribution network. Simulation results obtained by these techniques are compared based on loss minimization capability, enhancement of system bus voltage profile and reduction of cost and fitness functions. The major findings of the present study are the PEM-based method which provides almost similar results as MCS based method with less computation time and as far as loss minimization capacity, voltage profile improvement etc. is concerned, the hybrid-based optimization methods are compared with GA and PSO based optimization techniques.

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Badis, Afef, Mohamed Habib Boujmil, and Mohamed Nejib Mansouri. "Metaheuristic-Based Control for Three-Phase Grid-Connected Solar Photovoltaic Systems." International Journal of Energy Optimization and Engineering 11, no. 1 (January 1, 2022): 1–24. http://dx.doi.org/10.4018/ijeoe.310003.

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In this paper, a novel cascade control technique is proposed in order to identify the parameters of cascade controllers in a grid-connected photovoltaic (PV) system. Here, tuning of the inner and outer loop controllers is done simultaneously by means of an optimized genetic algorithm-based fractional order PID (GA-FOPID) control. Simulations are conducted using Matlab/Simulink software under different operating conditions, namely under fast-changing weather conditions, sudden parametric variations, and voltage dip, for the purpose of verifying the effectiveness of the proposed control strategy. By comparing the results with recently published optimization techniques such as particle swarm optimization (PSO) and ant colony optimization (ACO), the superiority and effectiveness of the proposed GA-FOPID control have been proven.

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Farid, Zahid, Rosdiadee Nordin, Mahamod Ismail, and Nor Fadzilah Abdullah. "Hybrid Indoor-Based WLAN-WSN Localization Scheme for Improving Accuracy Based on Artificial Neural Network." Mobile Information Systems 2016 (2016): 1–11. http://dx.doi.org/10.1155/2016/6923931.

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In indoor environments, WiFi (RSS) based localization is sensitive to various indoor fading effects and noise during transmission, which are the main causes of localization errors that affect its accuracy. Keeping in view those fading effects, positioning systems based on a single technology are ineffective in performing accurate localization. For this reason, the trend is toward the use of hybrid positioning systems (combination of two or more wireless technologies) in indoor/outdoor localization scenarios for getting better position accuracy. This paper presents a hybrid technique to implement indoor localization that adopts fingerprinting approaches in both WiFi and Wireless Sensor Networks (WSNs). This model exploits machine learning, in particular Artificial Natural Network (ANN) techniques, for position calculation. The experimental results show that the proposed hybrid system improved the accuracy, reducing the average distance error to 1.05 m by using ANN. Applying Genetic Algorithm (GA) based optimization technique did not incur any further improvement to the accuracy. Compared to the performance of GA optimization, the nonoptimized ANN performed better in terms of accuracy, precision, stability, and computational time. The above results show that the proposed hybrid technique is promising for achieving better accuracy in real-world positioning applications.

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C, Christopher Columbus, and Sishaj P. Simon. "Parallel hybrid enhanced inherited GA based scuc in a distributed cluster." Artificial Intelligence Research 1, no. 1 (August 6, 2012): 96. http://dx.doi.org/10.5430/air.v1n1p96.

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In the deregulated electricity market, secure operation is an enduring concern of the independent system operator (ISO). For a secure and economical hourly generation schedule of the day ahead market, ISO executes the security constrained unit commitment (SCUC) problem. In this paper, a new formulation of SCUC problem, considering more practical constraints are presented. The proposed SCUC formulation includes constraints, such as hourly power demand, system reserves, ramp up/down limits, minimum ON/OFF duration limits. Unlike the traditional SCUC techniques the proposed method solves the Security Constrained Economic Dispatch (SCED) from the UC. To solve such SCUC model, a hybrid solution method consists of an enhanced inherited genetic algorithm (EIGA) is used for unit commitment master problem and Lambda relaxation method is used for the economic dispatch sub-problem. The message passing interface (MPI) based technique is used to implement the hybrid EIGA in distributed memory model. The time complexity and the solution quality with respect to the number of processors in a cluster are thoroughly analyzed. The effectiveness of the proposed method to solve the SCUC problem is shown on different test systems.

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Ahmad, Waqas, Nasir Ayub, Tariq Ali, Muhammad Irfan, Muhammad Awais, Muhammad Shiraz, and Adam Glowacz. "Towards Short Term Electricity Load Forecasting Using Improved Support Vector Machine and Extreme Learning Machine." Energies 13, no. 11 (June 5, 2020): 2907. http://dx.doi.org/10.3390/en13112907.

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Forecasting the electricity load provides its future trends, consumption patterns and its usage. There is no proper strategy to monitor the energy consumption and generation; and high variation among them. Many strategies are used to overcome this problem. The correct selection of parameter values of a classifier is still an issue. Therefore, an optimization algorithm is applied with deep learning and machine learning techniques to select the optimized values for the classifier’s hyperparameters. In this paper, a novel deep learning-based method is implemented for electricity load forecasting. A three-step model is also implemented, including feature selection using a hybrid feature selector (XGboost and decision tee), redundancy removal using feature extraction technique (Recursive Feature Elimination) and classification/forecasting using improved Support Vector Machine (SVM) and Extreme Learning Machine (ELM). The hyperparameters of ELM are tuned with a meta-heuristic algorithm, i.e., Genetic Algorithm (GA) and hyperparameters of SVM are tuned with the Grid Search Algorithm. The simulation results are shown in graphs and the values are shown in tabular form and they clearly show that our improved methods outperform State Of The Art (SOTA) methods in terms of accuracy and performance. The forecasting accuracy of Extreme Learning Machine based Genetic Algo (ELM-GA) and Support Vector Machine based Grid Search (SVM-GS) is 96.3% and 93.25%, respectively. The accuracy of our improved techniques, i.e., ELM-GA and SVM-GS is 10% and 7%, respectively, higher than the SOTA techniques.

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Mansur, Ahmed, Md Amin, and Kazi Islam. "Performance Comparison of Mismatch Power Loss Minimization Techniques in Series-Parallel PV Array Configurations." Energies 12, no. 5 (March 6, 2019): 874. http://dx.doi.org/10.3390/en12050874.

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The mismatch in current-voltage (I-V) characteristics of photovoltaic (PV) modules causes significant power loss in a large PV array, which is known as mismatch power loss (MML). The PV array output power generation can be improved by minimizing MML using different techniques. This paper investigates the performance of different module arrangement techniques to minimize MML both for long series string (LSS) and long parallel branch (LPB) in series-parallel (SP) array configurations at uniform irradiance condition. To investigate the significance of MML LSS-SP configuration with dimensions: 1 × 40, 2 × 20, 4 × 10, 5 × 8 and LPB-SP configuration with dimensions: 40 × 1, 20 × 2, 10 × 4, 8 × 5 were used. A comparative analysis is made to find the effectiveness of MML reduction techniques on PV arrays with three different power ratings. Simulation results show that the PV modules arrangement obtained by the genetic algorithm (GA) and current based arrangement (Im) performed better than the arrangements obtained by all other techniques in terms of PV array output power and MML minimization. The performance of the proposed technique was analyzed for both LSS-SP and LPB-SP array configurations in 400 W, 3400 W, and 9880 W arrays. To substantiate the simulation results experiment was performed using a 400 W PV array in outdoor weather condition and obtained similar results. It was also observed that the percentage of recoverable energy (%RE) obtained by arranging the modules using the GA method was higher than Im based method for both LSS-SP and LPB-SP array configurations. A maximum %RE of 4.159 % was recorded for a 5 × 8 LSS-SP array configuration by applying the GA based MML reduction method.

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Journal articles on the topic 'GA-based techniques'

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