To see the other types of publications on this topic, follow the link: Intelligence algorithm.
Journal articles on the topic 'Intelligence algorithm'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 journal articles for your research on the topic 'Intelligence algorithm.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.
1
Shi, Yuhui. "Developmental Swarm Intelligence." International Journal of Swarm Intelligence Research 5, no. 1 (2014): 36–54. http://dx.doi.org/10.4018/ijsir.2014010102.
Full textAbstract:
In this paper, the necessity of having developmental learning embedded in a swarm intelligence algorithm is confirmed by briefly considering brain evolution, brain development, brainstorming process, etc. Several swarm intelligence algorithms are looked at from developmental learning perspective. Finally, a framework of a developmental swarm intelligence algorithm is given to help understand developmental swarm intelligence algorithms, and to guide to design and/or implement any new developmental swarm intelligence algorithm and/or any developmental evolutionary algorithm.
2
Navarrete-Dechent, Cristian. "Teledermatology and Artificial Intelligence." Iproceedings 8, no. 1 (2022): e36894. http://dx.doi.org/10.2196/36894.
Full textAbstract:
Background The use of artificial intelligence (AI) algorithms for the diagnosis of skin diseases has shown promise in experimental settings but has not yet been tested in real-life conditions. The COVID-19 pandemic led to a worldwide disruption of health systems, increasing the use of telemedicine. There is an opportunity to include AI algorithms in the teledermatology workflow. Objective The aim of this study is to test the performance of and physicians’ preferences regarding an AI algorithm during the evaluation of patients via teledermatology. Methods We performed a prospective study in 340 cases from 281 patients using patient-taken photos during teledermatology encounters. The photos were evaluated by an AI algorithm and the diagnosis was compared with the clinician’s diagnosis. Physicians also reported whether the AI algorithm was useful or not. Results The balanced (in-distribution) top-1 accuracy of the algorithm (47.6%) was comparable to the dermatologists (49.7%) and residents (47.7%) but superior to the general practitioners (39.7%; P=.049). Exposure to the AI algorithm results was considered useful in 11.8% of visits (n=40) and the teledermatologist correctly modified the real-time diagnosis in 0.6% (n=2) of cases. Algorithm performance was associated with patient skin type and image quality. Conclusions AI algorithms appear to be a promising tool in the triage and evaluation of lesions in patient-taken photographs via telemedicine. Conflicts of Interest None declared.
3
Reis, Cecília, and J. A. Tenreiro Machado. "Computational Intelligence in Circuit Synthesis." Journal of Advanced Computational Intelligence and Intelligent Informatics 11, no. 9 (2007): 1122–27. http://dx.doi.org/10.20965/jaciii.2007.p1122.
Full textAbstract:
This paper is devoted to the synthesis of combinational logic circuits through computational intelligence or, more precisely, using evolutionary computation techniques. Are studied two evolutionary algorithms, the Genetic and the Memetic Algorithm (GAs, MAs) and one swarm intelligence algorithm, the Particle Swarm Optimization (PSO). GAs are optimization and search techniques based on the principles of genetics and natural selection. MAs are evolutionary algorithms that include a stage of individual optimization as part of its search strategy, being the individual optimization in the form of a local search. The PSO is a population-based search algorithm that starts with a population of random solutions called particles. This paper presents the results for digital circuits design using the three above algorithms. The results show the statistical characteristics of this algorithms with respect to the number of generations required to achieve the solutions. The article analyzes also a new fitness function that includes an error discontinuity measure, which demonstrated to improve significantly the performance of the algorithm.
4
Al-Obaidi, Ahmed T. Sadiq, Hasanen S. Abdullah, and Zied O. Ahmed. "Meerkat Clan Algorithm: A New Swarm Intelligence Algorithm." Indonesian Journal of Electrical Engineering and Computer Science 10, no. 1 (2018): 354. http://dx.doi.org/10.11591/ijeecs.v10.i1.pp354-360.
Full textAbstract:
<p>Evolutionary computation and swarm intelligence meta-heuristics are exceptional instances that environment has been a never-ending source of creativeness. The behavior of bees, bacteria, glow-worms, fireflies and other beings have stirred swarm intelligence scholars to create innovative optimization algorithms. This paper proposes the Meerkat Clan Algorithm (MCA) that is a novel swarm intelligence algorithm resulting from watchful observation of the Meerkat (Suricata suricatta) in the Kalahari Desert in southern Africa. This animal shows an exceptional intelligence, tactical organizational skills, and remarkable directional cleverness in its traversal of the desert when searching for food. A Meerkat Clan Algorithm (MCA) proposed to solve the optimization problems through reach the optimal solution by efficient way comparing with another swarm intelligence. Traveling Salesman Problem uses as a case study to measure the capacity of the proposed algorithm through comparing its results with another swarm intelligence. MCA shows its capacity to solve the Traveling Salesman’s Problem. Its dived the solutions group to sub-group depend of meerkat behavior that gives a good diversity to reach an optimal solution. Paralleled with the current algorithms for resolving TSP by swarm intelligence, it has been displayed that the size of the resolved problems could be enlarged by adopting the algorithm proposed here.</p>
5
Ahmed, T. Sadiq Al-Obaidi, S. Abdullah Hasanen, and O. Ahmed Zied. "Meerkat Clan Algorithm: A New Swarm Intelligence Algorithm." Indonesian Journal of Electrical Engineering and Computer Science 10, no. 1 (2018): 354–60. https://doi.org/10.11591/ijeecs.v10.i1.pp354-360.
Full textAbstract:
Evolutionary computation and swarm intelligence meta-heuristics are exceptional instances that environment has been a never-ending source of creativeness. The behavior of bees, bacteria, glow-worms, fireflies and other beings have stirred swarm intelligence scholars to create innovative optimization algorithms. This paper proposes the Meerkat Clan Algorithm (MCA) that is a novel swarm intelligence algorithm resulting from watchful observation of the Meerkat (Suricata suricatta) in the Kalahari Desert in southern Africa. This animal shows an exceptional intelligence, tactical organizational skills, and remarkable directional cleverness in its traversal of the desert when searching for food. A Meerkat Clan Algorithm (MCA) proposed to solve the optimization problems through reach the optimal solution by efficient way comparing with another swarm intelligence. Traveling Salesman Problem uses as a case study to measure the capacity of the proposed algorithm through comparing its results with another swarm intelligence. MCA shows its capacity to solve the Traveling Salesman’s Problem. Its dived the solutions group to sub-group depend of meerkat behavior that gives a good diversity to reach an optimal solution. Paralleled with the current algorithms for resolving TSP by swarm intelligence, it has been displayed that the size of the resolved problems could be enlarged by adopting the algorithm proposed here.
6
Peng, Qiang, Renjun Zhan, Husheng Wu, and Meimei Shi. "Comparative Study of Wolf Pack Algorithm and Artificial Bee Colony Algorithm." International Journal of Swarm Intelligence Research 15, no. 1 (2024): 1–24. http://dx.doi.org/10.4018/ijsir.352061.
Full textAbstract:
Swarm intelligence optimization algorithms have been widely used in the fields of machine learning, process control and engineering prediction, among which common algorithms include ant colony algorithm (ACO), artificial bee colony algorithm (ABC) and particle swarm optimization (PSO). Wolf pack algorithm (WPA) as a newer swarm intelligence optimization algorithm has many similarities with ABC. In this paper, the basic principles, algorithm implementation processes, and related improvement strategies of these two algorithms were described in detail; A comparative analysis of their performance in solving different feature-based standard CEC test functions was conducted, with a focus on optimization ability and convergence speed, re-validating the unique characteristics of these two algorithms in searching. In the end, the future development trend and prospect of intelligent optimization algorithms was discussed, which is of great reference significance for the research and application of swarm intelligence optimization algorithms.
7
Pu, Kang. "Intellectual property protection for AI algorithms." Frontiers in Computing and Intelligent Systems 2, no. 3 (2023): 44–47. http://dx.doi.org/10.54097/fcis.v2i3.5210.
Full textAbstract:
In the 21st century, artificial intelligence technology is developing rapidly. Artificial intelligence technology combines multiple disciplines, involves multiple industries, and absorbs multiple talents, which is an extremely complex and huge technology. As the core technology of artificial intelligence, artificial intelligence algorithms have the ability to train and learn human-like self-training, and have become one of the most concerned fields. In recent years, there has also been an increasing discussion of artificial intelligence algorithms. How AI algorithms are protected has also become a matter of great concern for many companies and creators. In this context, this paper will take artificial intelligence algorithms as the research object to carry out the paper. This paper will introduce the legal nature of algorithms, reveal the introduction and ethical dilemma of the existing artificial intelligence algorithm rights ownership dilemma of artificial intelligence algorithm intellectual property protection, and put forward feasible suggestions for the construction of China's artificial intelligence algorithm intellectual property system. It should be made clear that China's existing patent protection path cannot fully respond to all the needs that artificial intelligence algorithms want to be protected, so some adjustments need to be made to the patent law. First of all, it should be clarified which artificial intelligence algorithms can and cannot become the object of patent authorization. As a new thing, artificial intelligence algorithms are different from patents that are obviously novel, practical and inventive in the traditional sense. In order to better protect artificial intelligence algorithms and promote the better development of artificial intelligence algorithm technology, in view of these special differences, certain adjustments have been made to the examination standards of China's patent substantive elements according to the characteristics of patentable artificial intelligence algorithm objects.
8
Wisam, Abdulelah Qasim. "A HYBRID ALGORITHM BASED ON INVASIVE WEED OPTIMIZATION ALGORITHM AND GREY WOLF OPTIMIZATION ALGORITHM." International Journal of Artificial Intelligence and Applications (IJAIA) 11, January (2020): 31–44. https://doi.org/10.5281/zenodo.3690787.
Full textAbstract:
In this research, two algorithms first, considered to be one of hybrid algorithms. And it is algorithm represents invasive weed optimization. This algorithm is a random numerical algorithm and the second algorithm representing the grey wolves optimization. This algorithm is one of the algorithms of swarm intelligence in intelligent optimization. The algorithm of invasive weed optimization is inspired by nature as the weeds have colonial behavior and were introduced by Mehrabian and Lucas in 2006. Invasive weeds are a serious threat to cultivated plants because of their adaptability and are a threat to the overall planting process. The behavior of these weeds has been studied and applied in the invasive weed algorithm. The algorithm of grey wolves, which is considered as a swarm intelligence algorithm, has been used to reach the goal and reach the best solution. The algorithm was designed by SeyedaliMirijalili in 2014 and taking advantage of the intelligence of the squadrons is to avoid falling into local solutions so the new hybridization process between the previous algorithms GWO and IWO and we will symbolize the new algorithm IWOGWO.Comparing the suggested hybrid algorithm with the original algorithms it results were excellent. The optimum solution was found in most of test functions.
9
Xu, Minghai, Li Cao, Dongwan Lu, Zhongyi Hu, and Yinggao Yue. "Application of Swarm Intelligence Optimization Algorithms in Image Processing: A Comprehensive Review of Analysis, Synthesis, and Optimization." Biomimetics 8, no. 2 (2023): 235. http://dx.doi.org/10.3390/biomimetics8020235.
Full textAbstract:
Image processing technology has always been a hot and difficult topic in the field of artificial intelligence. With the rise and development of machine learning and deep learning methods, swarm intelligence algorithms have become a hot research direction, and combining image processing technology with swarm intelligence algorithms has become a new and effective improvement method. Swarm intelligence algorithm refers to an intelligent computing method formed by simulating the evolutionary laws, behavior characteristics, and thinking patterns of insects, birds, natural phenomena, and other biological populations. It has efficient and parallel global optimization capabilities and strong optimization performance. In this paper, the ant colony algorithm, particle swarm optimization algorithm, sparrow search algorithm, bat algorithm, thimble colony algorithm, and other swarm intelligent optimization algorithms are deeply studied. The model, features, improvement strategies, and application fields of the algorithm in image processing, such as image segmentation, image matching, image classification, image feature extraction, and image edge detection, are comprehensively reviewed. The theoretical research, improvement strategies, and application research of image processing are comprehensively analyzed and compared. Combined with the current literature, the improvement methods of the above algorithms and the comprehensive improvement and application of image processing technology are analyzed and summarized. The representative algorithms of the swarm intelligence algorithm combined with image segmentation technology are extracted for list analysis and summary. Then, the unified framework, common characteristics, different differences of the swarm intelligence algorithm are summarized, existing problems are raised, and finally, the future trend is projected.
10
Thanoon, Radhwan Basim. "Hybrid Inverse Weed Optimization Algorithm with Math-Flame Optimization Algorithm." sinkron 8, no. 3 (2024): 2008–21. http://dx.doi.org/10.33395/sinkron.v8i3.13755.
Full textAbstract:
In this work, two Meta-Heuristic Algorithms were hybridized, the first is the Invers Weed optimization algorithm (IWO), which is a passing multiple algorithm, and the second is the Moth-flame Optimization Algorithm (MFO). Which depend in their behavior on the intelligence of the swarm and the intelligence of society, and they have unique characteristics that exceed the characteristics of the intelligence of other swarms because they are efficient in achieving the right balance between exploration and exploitation. So the new algorithm improves the initial population that is randomly generated, A process of hybridization was made between the IWO and MFO Algorithm to call The new hybrid algorithm (IWOMFO). The new hybrid algorithm was used for 16 high-scaling optimization functions with different community sizes and 250 repetitions. The Algorithm showed access to optimal solutions by achieving the value Minority () for most of these functions and the results of this algorithm are compared with the basic algorithms IWO, MFO
11
Lê Ngọc, Hiếu, and Thanh Luong Van. "A Counseling System of Multiple Intelligence Theory Combined With kNN Classification Algorithm." Journal of Computer Science and Technology Studies 3, no. 2 (2021): 10–30. http://dx.doi.org/10.32996/jcsts.2021.3.2.2.
Full textAbstract:
Choosing the right career is always a big issue, an important concern for everyone. To have a job, which is suitable for you, firstly you must look at yourself, called the self, and you should be aware of what the self is then you can promote the strength of your own self and avoid your weakness. To help discover more about yourself, during researching and studying, we come up with the idea that we would propose a career counseling system based on Howard Gardner's theory. The system uses the theory of multiple intelligences (Abenti & Daradoumis, 2020) which is combined with the K-nearest neighbors (KNN) (Tang, Ying; Tang, Ying; Hare, Ryan; Wang, Fei-Yue;, 2020) algorithm to assist people and to give out a suitable suggestion about career path for them. We use the results of the eight intelligences retrieved from the KNN classification algorithm to give users the consulting for their career paths. This system is built with a dataset based on 56 multiple-choice questions. These include 48 multiple choice questions based on Howard Gardner's theory of multiple intelligences (Bravo, Leonardo Emiro Contreras; Molano, Jose Ignacio Rodriguez; Trujillo, Edwin Rivas, 2020), (businessballs, 2017) and 8 multiple choice questions which are the labels of the classifier. We divided the dataset into 8 subsets corresponding with 8 Intelligences defined by Howard Gardner with the collected dataset. In each subset, we build the KNN classifier model using KNN classification algorithm. This processing of 8 subsets come out with the results accuracy for the 8 Intelligences: linguistic intelligence (80.95%), logical-mathematical intelligence (82.14%), musical intelligence (96.43%), bodily-kinesthetic intelligence (82.14%), spatial-visual intelligence (82.14%), interpersonal intelligence (89.29%), intrapersonal intelligence (88.1%), existential intelligence (78.57%). With the outcome of 8 models, we have tested with 5 students and compared them to their actual intelligences. The comparison results tell us about the valuable potential in career path of the proposed counselling system, the advantages of this combination between Multiple Intelligence and KNN classifier.
12
Gou, Pingzhang, Bo He, and Zhaoyang Yu. "A Node Location Algorithm Based on Improved Whale Optimization in Wireless Sensor Networks." Wireless Communications and Mobile Computing 2021 (September 16, 2021): 1–17. http://dx.doi.org/10.1155/2021/7523938.
Full textAbstract:
With the popularity of swarm intelligence algorithms, the positioning of nodes to be located in wireless sensor networks (WSNs) has received more and more attention. To overcome the disadvantage of large ranging error and low positioning accuracy caused by the positioning algorithm of the received signal strength indication (RSSI) ranging model, we use the RSSI modified by Gaussian to reduce the distance measurement error and introduce an improved whale optimization algorithm to optimize the location of the nodes to be positioned to improve the positioning accuracy. The experimental results show that the improved whale algorithm performs better than the whale optimization algorithm and other swarm intelligence algorithms under 20 different types of benchmark function tests. The positioning accuracy of the proposed location algorithm is better than that of the original RSSI algorithm, the hybrid exponential and polynomial particle swarm optimization (HPSO) positioning algorithms, the whale optimization, and the quasiaffine transformation evolutionary (WOA-QT) positioning algorithm. It can be concluded that the cluster intelligence algorithm has better advantages than the original RSSI in WSN node positioning, and the improved algorithm in this paper has more advantages than several other cluster intelligence algorithms, which can effectively solve the positioning requirements in practical applications.
13
Gajawada, Satish, and Hassan M. H. Mustafa. "Out of the Box Artificial Intelligence (OBAI): The Beginning of a New Era in Artificial Intelligence." Computer and Information Science 15, no. 2 (2022): 6. http://dx.doi.org/10.5539/cis.v15n2p6.
Full textAbstract:
The main purpose of writing this article is to unify all the OUT OF THE BOX ideas (under Artificial Intelligence) invented by the corresponding author of this work during the period (2013-2022) under a single umbrella titled &ldquo;Out of the BOX Artificial Intelligence Field (OBAI Field)&rdquo;. All the OUT OF THE BOX ideas which are proposed under Artificial Intelligence will come under new field titled OBAI Field which is defined in this work. A new Artificial Intelligence field titled &ldquo;Artificial Cartoon Algorithms (ACA)&rdquo; is invented in this work. ACA is a sub-field of OBAI field as it is an OUT OF THE BOX idea. Four new algorithms titled &ldquo;Artificial Cartoon Popeye Algorithm&rdquo;, &ldquo;Artificial Cartoon Chhota Bheem Algorithm&rdquo;, &ldquo;Artificial Cartoon Jerry Algorithm&rdquo; and &ldquo;Artificial Cartoon Happy Kid Algorithm&rdquo; are designed in this work.
14
Xu, Xing, Na Hu, and Wei Qin Ying. "A Survey of Intelligence Optimization Algorithm with Thermodynamics." Applied Mechanics and Materials 513-517 (February 2014): 386–90. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.386.
Full textAbstract:
There are many similarities between the intelligence algorithms and the Statistical Physics and Thermodynamics in many aspects, such as the research object, tasks and methods. Firstly, this paper presents the hybrid intelligence algorithms improved by Thermodynamics. Then the theory analysis of intelligence algorithms by Thermodynamics is presented. Finally, a new research direction, that is the novel intelligence algorithm based on statistical physics and thermodynamics, is proposed for the future.
15
Xiong, Wei. "Initial Clustering Based on the Swarm Intelligence Algorithm for Computing a Data Density Parameter." Computational Intelligence and Neuroscience 2022 (June 10, 2022): 1–8. http://dx.doi.org/10.1155/2022/6408949.
Full textAbstract:
To improve the accuracy and efficiency of cluster startup using data density parameters, the author proposes a large data cluster extraction algorithm based on a herd intelligence algorithm. Since clustering to initiate data density parameters is primarily data mining, the author explores data mining clustering based primarily on herd intelligence algorithms. First, the obscure c-key cluster algorithm in the clustering algorithm is analyzed, and then the hybrid jump algorithm in the sub-heuristic herd intelligence optimization technology is optimized in the case of a few parameters by combining the obscure c-means cluster algorithm. The simulation results show that the convergence speed of the fuzzy C-means clustering algorithm and hybrid leapfrog algorithm is slow; the convergence rate of the PSO-FCM algorithm has been improved. Since the fusion algorithm requires fewer adjustment parameters, the cluster centers can be obtained more accurately and quickly with strong robustness and fast convergence. Compared with other algorithms, the fusion algorithm proposed by the author has the best performance in clustering effect, accuracy, convergence rate, and robustness. It is proved that the swarm intelligence algorithm can effectively perform density parameter initialization clustering on computational data.
16
Liu, Rui, Yuanbin Mo, Yanyue Lu, Yucheng Lyu, Yuedong Zhang, and Haidong Guo. "Swarm-Intelligence Optimization Method for Dynamic Optimization Problem." Mathematics 10, no. 11 (2022): 1803. http://dx.doi.org/10.3390/math10111803.
Full textAbstract:
In recent years, the vigorous rise in computational intelligence has opened up new research ideas for solving chemical dynamic optimization problems, making the application of swarm-intelligence optimization techniques more and more widespread. However, the potential for algorithms with different performances still needs to be further investigated in this context. On this premise, this paper puts forward a universal swarm-intelligence dynamic optimization framework, which transforms the infinite-dimensional dynamic optimization problem into the finite-dimensional nonlinear programming problem through control variable parameterization. In order to improve the efficiency and accuracy of dynamic optimization, an improved version of the multi-strategy enhanced sparrow search algorithm is proposed from the application side, including good-point set initialization, hybrid algorithm strategy, Lévy flight mechanism, and Student’s t-distribution model. The resulting augmented algorithm is theoretically tested on ten benchmark functions, and compared with the whale optimization algorithm, marine predators algorithm, harris hawks optimization, social group optimization, and the basic sparrow search algorithm, statistical results verify that the improved algorithm has advantages in most tests. Finally, the six algorithms are further applied to three typical dynamic optimization problems under a universal swarm-intelligence dynamic optimization framework. The proposed algorithm achieves optimal results and has higher accuracy than methods in other references.
17
Zheng, Wei. "Improvement of Wolf Pack Algorithm and Its Application to Logistics Distribution Problems." Scientific Programming 2022 (September 13, 2022): 1–12. http://dx.doi.org/10.1155/2022/7532076.
Full textAbstract:
In logistics distribution systems, the constrained optimisation of the cargo dispensing problem has been the focus of research in related fields. At present, many scholars try to solve the problem by introducing swarm intelligence algorithms, including genetic algorithm, particle swarm algorithm, bee swarm algorithm, fish swarm algorithm, etc. Each swarm intelligence algorithm has different characteristics, but they all have certain advantages for the optimisation of complex problems. In recent years, the Wolf Pack algorithm, an emerging swarm intelligence algorithm, has shown good global convergence and computational robustness in solving complex high-dimensional functions. Therefore, this article chooses to use the Wolf Pack algorithm to solve a multi-vehicle and multi-goods dispensing problem model. First, the principle and process of the Wolf Pack algorithm are introduced, and two improvements are proposed for the way of location update and the way of step update. Then, a mathematical model of the multi-vehicle and multi-goods dispensing problem is developed. Next, the mathematical model is solved using the proposed improved Wolf Pack algorithm. The experimental results show that the proposed improved Wolf Pack algorithm effectively solves the cargo dispatching problem. In addition, the proposed improved Wolf Pack algorithm can effectively reduce the number of vehicles to be dispatched compared with other swarm intelligence algorithms.
18
Fountas, Nikolaos A., John D. Kechagias, and Nikolaos M. Vaxevanidis. "Swarm intelligence algorithms for optimising sliding wear of nanocomposites." Tribology and Materials 3, no. 1 (2024): 44–50. http://dx.doi.org/10.46793/tribomat.2024.004.
Full textAbstract:
This paper presents simulation results obtained by a set of modern algorithms adhering to swarm intelligence for minimising wear rate in the case of A356/Al2O3 nanocomposites produced using a compocasting process. Grey wolf optimisation (GWO) algorithm, moth-flame optimisation (MFO) algorithm, dragonfly algorithm (DA) and whale optimisation algorithm (WOA) were the algorithms under examination. A full quadratic regression equation that predicts wear rate, as the optimisation objective by considering reinforcement content, sliding speed, normal load and reinforcement size as the independent process parameters, was utilised as the objective function. Simulation results obtained by the selected algorithms were quite promising in terms of fast convergence and global optimum result arrival, thus prompting to further investigation of applying swarm intelligence to general problem-solving aspects related to tribology.
19
Рабійчук, І. О., and А. В. Фечан. "The main challenges of adaptability of swarm intelligence algorithms." Scientific Bulletin of UNFU 34, no. 5 (2024): 97–103. http://dx.doi.org/10.36930/40340513.
Full textAbstract:
Analyzed three swarm intelligence algorithms, namely Ant Colony Optimization (ACO), Bee Colony Optimization (BCO), Particle Swarm Optimization (PSO) and the adaptability of these algorithms to a dynamic environment. Firstly, the ACO algorithm was analyzed, the behavior of ants in nature, the purpose of the algorithm, and its shortcomings in a dynamic environment. Then the existing modifications of this algorithm to changing environments were investigated, namely AСO with dynamic pheromone updating (AACO), ACO with adaptive memory (ACO-AP), ACO with multi-agent system (MAS-ACO), ACO with machine learning algorithms (MLACO). The advantages and disadvantages of these modifications are also discussed in detail. The software tools that implement the functionality of this algorithm, such as AntTweakBar, AntOpt, EasyAnt have been mentioned. These software tools provide an opportunity to develop new modifications of the ACO algorithms and to study existing ones. Furthermore, the capabilities of the BCO algorithm were clarified and the behavior and parameters of this algorithm were described, its pros and cons in a dynamic environment were investigated. The following BCO modifications were considered: Group Bee Algorithm (GBA), Artificial Bee Colony (ABC), and open source software: PySwarms, PyABC. The third part of the article investigates the work of the PSO algorithm, its advantages and disadvantages of adaptation to dynamic environments. Dynamic Particle Swarm Optimization with Permutation (DPSO-P), Dynamic Multi-swarm Particle Swarm Optimization Based on Elite Learning (DMS-P50-EL) are considered as modifications of PSO to adapt to dynamic environments. The libraries for work such as SciPy, DEAP, PyGAD, Particleswarm, JSwarm (has a wide API and well-written documentation), Dlib have been mentioned. Finally, a comparative table with the most important properties (resistance to environmental changes, complexity of implementation, the possibility of using for a UAV swarm, etc.) for all three algorithms was created, a brief description of similar articles comparing algorithms of swarm intelligence was also made, and the conclusions of the study were drawn.
20
Kayım, Furkan, and Atınç Yılmaz. "Financial Instrument Forecast with Artificial Intelligence." EMAJ: Emerging Markets Journal 11, no. 2 (2021): 16–24. http://dx.doi.org/10.5195/emaj.2021.229.
Full textAbstract:
In ancient times, trade was carried out by barter. With the use of money and similar means, the concept of financial instruments emerged. Financial instruments are tools and documents used in the economy. Financial instruments can be foreign exchange rates, securities, crypto currency, index and funds. There are many methods used in financial instrument forecast. These methods include technical analysis methods, basic analysis methods, forecasts carried out using variables and formulas, time-series algorithms and artificial intelligence algorithms. Within the scope of this study, the importance of the use of artificial intelligence algorithms in the financial instrument forecast is studied. Since financial instruments are used as a means of investment and trade by all sections of the society, namely individuals, families, institutions, and states, it is highly important to know about their future. Financial instrument forecast can bring about profitability such as increased income welfare, more economical adjustment of maturities, creation of large finances, minimization of risks, spreading of ownership to the grassroots, and more balanced income distribution. Within the scope of this study, financial instrument forecast is carried out by applying a new methods of Long Short Term Memory (LSTM), Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), Autoregressive Integrated Moving Average (ARIMA) algorithms and Ensemble Classification Boosting Method. Financial instrument forecast is carried out by creating a network compromising LSTM and RNN algorithm, an LSTM layer, and an RNN output layer. With the ensemble classification boosting method, a new method that gives a more successful result compared to the other algorithm forecast results was applied. At the conclusion of the study, alternative algorithm forecast results were competed against each other and the algorithm that gave the most successful forecast was suggested. The success rate of the forecast results was increased by comparing the results with different time intervals and training data sets. Furthermore, a new method was developed using the ensemble classification boosting method, and this method yielded a more successful result than the most successful algorithm result.
21
Belkadi, Mohamed, and Abdelhamid Daamouche. "Swarm Intelligence Approach to QRS Detection." International Arab Journal of Information Technology 17, no. 4 (2020): 480–87. http://dx.doi.org/10.34028/iajit/17/4/6.
Full textAbstract:
The QRS detection is a crucial step in ECG signal analysis; it has a great impact on the beats segmentation and in the final classification of the ECG signal. The Pan-Tompkins is one of the first and best-performing algorithms for QRS detection. It performs filtering for noise suppression, differentiation for slope dominance, and thresholding for decision making. All of the parameters of the Pan-Tompkins algorithm are selected empirically. However, we think that the Pan-Tompkins method can achieve better performance if the parameters were optimized. Therefore, we propose an adaptive algorithm that looks for the best set of parameters that improves the Pan-Tompkins algorithm performance. For this purpose, we formulate the parameter design as an optimization problem within a particle swarm optimization framework. Experiments conducted on the 24 hours recording of the MIT/BIH arrhythmia benchmark dataset achieved an overall accuracy of 99.83% which outperforms the state-of-the-art time-domain algorithms
22
Kulkarni, Vaishali R., Veena Desai, and Raghavendra Kulkarni. "A Comparative Study of Computational Intelligence Algorithms for Sensor Localization." International Journal of Sensors, Wireless Communications and Control 9, no. 2 (2019): 224–36. http://dx.doi.org/10.2174/2210327909666181206103304.
Full textAbstract:
Background & Objective: Location of sensors is an important information in wireless sensor networks for monitoring, tracking and surveillance applications. The accurate and quick estimation of the location of sensor nodes plays an important role. Localization refers to creating location awareness for as many sensor nodes as possible. Multi-stage localization of sensor nodes using bio-inspired, heuristic algorithms is the central theme of this paper. Methodology: Biologically inspired heuristic algorithms offer the advantages of simplicity, resourceefficiency and speed. Four such algorithms have been evaluated in this paper for distributed localization of sensor nodes. Two evolutionary computation-based algorithms, namely cultural algorithm and the genetic algorithm, have been presented to optimize the localization process for minimizing the localization error. The results of these algorithms have been compared with those of swarm intelligence- based optimization algorithms, namely the firefly algorithm and the bee algorithm. Simulation results and analysis of stage-wise localization in terms of number of localized nodes, computing time and accuracy have been presented. The tradeoff between localization accuracy and speed has been investigated. Results: The comparative analysis shows that the firefly algorithm performs the localization in the most accurate manner but takes longest convergence time. Conclusion: Further, the cultural algorithm performs the localization in a very quick time; but, results in high localization error.
23
Sobecki, Janusz. "Comparison of Selected Swarm Intelligence Algorithms in Student Courses Recommendation Application." International Journal of Software Engineering and Knowledge Engineering 24, no. 01 (2014): 91–109. http://dx.doi.org/10.1142/s0218194014500041.
Full textAbstract:
In this paper a comparison of a few swarm intelligence algorithms applied in recommendation of student courses is presented. Swarm intelligence algorithms are nowadays successfully used in many areas, especially in optimization problems. To apply each swarm intelligence algorithm in recommender systems a special representation of the problem space is necessary. Here we present the comparison of efficiency of grade prediction of several evolutionary algorithms, such as: Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Intelligent Weed Optimization (IWO), Bee Colony Optimization (BCO) and Bat Algorithm (BA).
24
Liu, Ningning, Ruige Jiang, and Xiaoxia Tai. "Dialectical Analysis of Comparative Pedagogy Based on Multiple Intelligences Evaluation." Scientific Programming 2022 (January 20, 2022): 1–9. http://dx.doi.org/10.1155/2022/5031639.
Full textAbstract:
As a common and mature algorithm, the neural network algorithm has been widely used in many industries throughout the country. The traditional dialectical analysis method for multiple intelligences evaluation in comparative education cannot meet the dialectical needs with different characteristics, the information big data model of multiple intelligences evaluation based on neural network algorithm has been gradually applied to several evaluation systems of comparative education. This paper studies the application of neural network algorithms in the dialectical analysis of comparative education in China and puts forward multiple intelligences evaluation model based on neural network algorithm, which can realize the intelligent evaluation of comparative education according to the characteristics of teaching behavior. At the same time, the idea of random big data acquisition is combined with digital feature analysis based on neural network algorithm and particle swarm optimization algorithm. Finally, the experimental results show that the dialectical analysis model of comparative education based on multiple intelligences evaluation of neural network algorithm can efficiently process the education data with tracking intelligence, which achieves a new breakthrough in the multiple intelligences evaluation of comparative education in China and saves a lot of time for the dialectical analysis process.
25
Liu, Yang. "Using Deep Learning and Swarm Intelligence to Achieve Personalized English-Speaking Education." International Journal of Swarm Intelligence Research 15, no. 1 (2024): 1–15. http://dx.doi.org/10.4018/ijsir.343989.
Full textAbstract:
This paper presents a pioneering approach to personalized English oral education through the integration of deep learning and swarm intelligence algorithms. Leveraging deep learning techniques, our system offers precise evaluation of various aspects of spoken language, including pronunciation, fluency, and grammatical accuracy. Furthermore, we combine swarm intelligence algorithms to optimize model parameters to achieve optimal performance. We compare the proposed optimization algorithm based on swarm intelligence and its corresponding original algorithm for training comparison to test the effect of the proposed optimizer. Experimental results show that in most cases, the accuracy of the test set using the optimization algorithm based on the swarm intelligence algorithm is better than the corresponding original version, and the training results are more stable. Our experimental results demonstrate the efficacy of the proposed approach in enhancing personalized English oral education, paving the way for transformative advancements in language learning technologies.
26
Deghbouch, Hicham, and Fatima Debbat. "Hybrid Bees Algorithm with Grasshopper Optimization Algorithm for Optimal Deployment of Wireless Sensor Networks." Inteligencia Artificial 24, no. 67 (2021): 18–35. http://dx.doi.org/10.4114/intartif.vol24iss67pp18-35.
Full textAbstract:
This work addresses the deployment problem in Wireless Sensor Networks (WSNs) by hybridizing two metaheuristics, namely the Bees Algorithm (BA) and the Grasshopper Optimization Algorithm (GOA). The BA is an optimization algorithm that demonstrated promising results in solving many engineering problems. However, the local search process of BA lacks efficient exploitation due to the random assignment of search agents inside the neighborhoods, which weakens the algorithm’s accuracy and results in slow convergence especially when solving higher dimension problems. To alleviate this shortcoming, this paper proposes a hybrid algorithm that utilizes the strength of the GOA to enhance the exploitation phase of the BA. To prove the effectiveness of the proposed algorithm, it is applied for WSNs deployment optimization with various deployment settings. Results demonstrate that the proposed hybrid algorithm can optimize the deployment of WSN and outperforms the state-of-the-art algorithms in terms of coverage, overlapping area, average moving distance, and energy consumption.
27
Dou, Siqi, Junjie Li, and Fei Kang. "Parameter identification of concrete dams using swarm intelligence algorithm." Engineering Computations 34, no. 7 (2017): 2358–78. http://dx.doi.org/10.1108/ec-03-2017-0110.
Full textAbstract:
Purpose Parameter identification is an important issue in structural health monitoring and damage identification for concrete dams. The purpose of this paper is to introduce a novel adaptive fireworks algorithm (AFWA) into inverse analysis of parameter identification. Design/methodology/approach Swarm intelligence algorithms and finite element analysis are integrated to identify parameters of hydraulic structures. Three swarm intelligence algorithms: AFWA, standard particle swarm optimization (SPSO) and artificial bee colony algorithm (ABC) are adopted to make a comparative study. These algorithms are introduced briefly and then tested by four standard benchmark functions. Inverse analysis methods based on AFWA, SPSO and ABC are adopted to identify Young’s modulus of a concrete gravity dam and a concrete arch dam. Findings Numerical results show that swarm intelligence algorithms are powerful tools for parameter identification of concrete structures. The proposed AFWA-based inverse analysis algorithm for concrete dams is promising in terms of accuracy and efficiency. Originality/value Fireworks algorithm is applied for inverse analysis of hydraulic structures for the first time, and the problem of parameter selection in AFWA is studied.
28
Han, Yiwei. "Application of Different Artificial Intelligence Methods on Reversi." Highlights in Science, Engineering and Technology 39 (April 1, 2023): 1338–42. http://dx.doi.org/10.54097/hset.v39i.6764.
Full textAbstract:
Using different algorithms in artificial intelligence to perform adversarial agent games. The most popular adversarial game that could be implemented using artificial intelligence algorithms is chess games. The algorithm this research used includes minimax with improvement and deep reinforcement learning. The goal for this research is to compute the popular game Reversi in different artificial intelligence methods successfully. Moreover, the research seeks for improvements in the heuristic part of minimax algorithm and the combination of deep reinforcement learning with Monte Carlo Tree with Neural Network. This paper uses Reversi as an example to analyze different algorithms, including Minimax, Monte Carlo Tree, and Neural Networks. As a result, both algorithms work successfully. The alpha-beta pruning minimax algorithm with improvement in heuristic function and fixed depth cut-off significantly increase the winning probability and time cost of our artificial intelligence agent. The deep reinforcement learning successfully combined MCTS with neural network to train two agents to complete Reversi with great winning probability.
29
Zhang, Guangchao. "Packaging Big Data Visualization Based on Computational Intelligence Information Design." Computational Intelligence and Neuroscience 2022 (April 23, 2022): 1–10. http://dx.doi.org/10.1155/2022/4558839.
Full textAbstract:
A method based on a computational intelligence information model is proposed to study the visualization of large data packages. Since the CAIM algorithm only considers the distribution of the largest number of classes in an interval, it offers an optimization method and simultaneously determines the appropriate stopping conditions to avoid overcrowding. The effectiveness of the improved algorithm has been experimentally proven. Methods of character reduction and weight determination are used to reduce the index and weight, establishing a large packaging information system. Experimental results show that the improved algorithm in this article produces more classification rules than the CAIM algorithm, because the discrete intervals created by the CAIM algorithm are relatively simple, but the classification rules are few, but less than the number of CAIM algorithms. Classification rules are generated by entropy-based sampling algorithms. This can make the classification rules simple and universal, and it is clear that the optimal sampling algorithm is more accurate than the CAIM algorithm.
30
Mashwani, Wali Khan, Ruqayya Haider, and Samir Brahim Belhaouari. "A Multiswarm Intelligence Algorithm for Expensive Bound Constrained Optimization Problems." Complexity 2021 (February 27, 2021): 1–18. http://dx.doi.org/10.1155/2021/5521951.
Full textAbstract:
Constrained optimization plays an important role in many decision-making problems and various real-world applications. In the last two decades, various evolutionary algorithms (EAs) were developed and still are developing under the umbrella of evolutionary computation. In general, EAs are mainly categorized into nature-inspired and swarm-intelligence- (SI-) based paradigms. All these developed algorithms have some merits and also demerits. Particle swarm optimization (PSO), firefly algorithm, ant colony optimization (ACO), and bat algorithm (BA) have gained much popularity and they have successfully tackled various test suites of benchmark functions and real-world problems. These SI-based algorithms follow the social and interactive principles to perform their search process while approximating solution for the given problems. In this paper, a multiswarm-intelligence-based algorithm (MSIA) is developed to cope with bound constrained functions. The suggested algorithm integrates the SI-based algorithms to evolve population and handle exploration versus exploitation issues. Thirty bound constrained benchmark functions are used to evaluate the performance of the proposed algorithm. The test suite of benchmark function is recently designed for the special session of EAs competition in IEEE Congress on Evolutionary Computation (IEEE-CEC′13). The suggested algorithm has approximated promising solutions with good convergence and diversity maintenance for most of the used bound constrained single optimization problems.
31
Qinghua Wu, Haihui Wang, Peng Zhuang, and Fei Liu. "Intelligence Image Matching Algorithm based on Evolutionary Algorithm." INTERNATIONAL JOURNAL ON Advances in Information Sciences and Service Sciences 4, no. 21 (2012): 495–502. http://dx.doi.org/10.4156/aiss.vol4.issue21.63.
Full text
32
Chen, Jun-Feng, and Tie-Jun Wu. "A computational intelligence optimization algorithm: Cloud drops algorithm." Integrated Computer-Aided Engineering 21, no. 2 (2014): 177–88. http://dx.doi.org/10.3233/ica-130459.
Full text
33
WEI, G. E., L. GAO, and F. SHI. "APPLICATION OF DATA MINING ALGORITHM IN INTELLIGENCE ANALYSIS OF ENTERPRISE ECONOMIC INTELLIGENCE." Latin American Applied Research - An international journal 48, no. 4 (2018): 261–66. http://dx.doi.org/10.52292/j.laar.2018.238.
Full textAbstract:
With the continuous development and application of high-speed information technology such as the Internet, the acquisition and utilization of economic intelligence has an important impact on the operation of the national economy and the operation of enterprises. Based on the detailed analysis of data mining algorithms, this paper constructs a user classification model based on clustering algorithm and a user interest feature extraction model based on UR-LDA, and uses the improved K-means algorithm in an unsupervised manner. User clustering was carried out, and data mining experiments were conducted on users of Sina Weibo. The experimental results show that the user data extracted from the interest feature topic is clustered by the improved K-means, and six similar user clusters are obtained. The better clustering results are obtained, which indicates that the classification model constructed in this paper is effective.
34
Liu, Qiaofeng, Jinglun Huang, Bin Zhang, Jihong Zhao, Chengyun Zhang, and Xiang Gao. "Research on Resource Allocation and Optimization of Community Intelligent Sports Service for the Elderly Based on Group Intelligence." Journal of Healthcare Engineering 2021 (December 21, 2021): 1–16. http://dx.doi.org/10.1155/2021/1185533.
Full textAbstract:
Objective. The mainstream development trend in the era of intelligent sports. At present, with the rapid development of science and technology, it is absolutely wise to combine group intelligence with community intelligent sports services for the elderly. Group intelligence has opened a new era of intelligent sports service. Group intelligence has become an important factor in the development and growth of community intelligent sports service for the elderly and has become a hot topic at present. However, intelligence has encountered difficulties on the road of development. At present, the aging of the population is getting worse and worse, and the elderly have higher and higher requirements for fitness and leisure services, which leads to the need for sports services to be continuously strengthened. The distribution of resources is uneven, the data is not clear enough, and the swarm intelligence algorithm is not perfect. With the adaptation of the elderly to intelligence, more intelligent, concise, and personalized services need to be developed. The most important method is to optimize the swarm intelligence algorithm continuously. In this paper, PSO algorithm is optimized and HCSSPSO algorithm is proposed. HCSSPSO algorithm is a combination of PSO algorithm and clonal selection strategy, and test simulation experiments, PSO algorithm, CLPSO algorithm, and HCSSPSO algorithm for comparison. From the experimental results, HCSSPSO algorithm has better convergence speed and stability, whether it is data or comparison graph. The data optimized by HCSSPSO algorithm is higher than the original data and the other two algorithms in terms of satisfaction and resource allocation.
35
Xian, Ning. "Comparative study of swarm intelligence-based saliency computation." International Journal of Intelligent Computing and Cybernetics 10, no. 3 (2017): 348–61. http://dx.doi.org/10.1108/ijicc-03-2017-0024.
Full textAbstract:
Purpose The purpose of this paper is to propose a new algorithm chaotic pigeon-inspired optimization (CPIO), which can effectively improve the computing efficiency of the basic Itti’s model for saliency-based detection. The CPIO algorithm and relevant applications are aimed at air surveillance for target detection. Design/methodology/approach To compare the improvements of the performance on Itti’s model, three bio-inspired algorithms including particle swarm optimization (PSO), brain storm optimization (BSO) and CPIO are applied to optimize the weight coefficients of each feature map in the saliency computation. Findings According to the experimental results in optimized Itti’s model, CPIO outperforms PSO in terms of computing efficiency and is superior to BSO in terms of searching ability. Therefore, CPIO provides the best overall properties among the three algorithms. Practical implications The algorithm proposed in this paper can be extensively applied for fast, accurate and multi-target detections in aerial images. Originality/value CPIO algorithm is originally proposed, which is very promising in solving complicated optimization problems.
36
Ludwig, Simone A., and Deepak Dawar. "Parallelization of Enhanced Firework Algorithm using MapReduce." International Journal of Swarm Intelligence Research 6, no. 2 (2015): 32–51. http://dx.doi.org/10.4018/ijsir.2015040102.
Full textAbstract:
Swarm intelligence algorithms are inherently parallel since different individuals in the swarm perform independent computations at different positions simultaneously. Hence, these algorithms lend themselves well to parallel implementations thereby speeding up the optimization process. FireWorks Algorithm (FWA) is a recently proposed swarm intelligence algorithm for optimization. This work investigates the scalability of the parallelization of the Enhanced FireWorks Algorithm (EFWA), which is an improved version of FWA. The authors use the MapReduce platform for parallelizing EFWA, investigate its ability to scale, and report on the speedup obtained on different benchmark functions for increasing problem dimensions.
37
Vrouva, Sotiria, George A. Koumantakis, Varvara Sopidou, Petros I. Tatsios, Christos Raptis, and Adam Adamopoulos. "Comparison of Machine Learning Algorithms and Hybrid Computational Intelligence Algorithms for Rehabilitation Classification and Prognosis in Reverse Total Shoulder Arthroplasty." Bioengineering 12, no. 2 (2025): 150. https://doi.org/10.3390/bioengineering12020150.
Full textAbstract:
Despite the increasing application of machine learning and computational intelligence algorithms in medicine and physiotherapy, accurate classification and prognosis algorithms for postoperative patients in the rehabilitation phase are still lacking. The present study was carried out in two phases. In Phase I, classification performance of simple machine learning algorithms applied on data of patients suffering of reverse total shoulder arthroplasty (RTSA), examining algorithms’ classification accuracy and patients’ rehabilitation prognosis. In Phase II, hybrid computational intelligence algorithms were developed and applied in order to search for the minimum possible training set that achieves the maximum classification and prognostic performance. The data included features like age and gender, passive range of available motion of all movements (preoperative and postoperative), visual analog pain scale (preoperative and postoperative), and total rehabilitation time. In Phase I, K-nearest neighbors (ΚΝΝ) classification algorithm and K-means clustering algorithm (GAKmeans) were applied. Also, a genetic algorithm (GA)-based clustering algorithm (GAClust) was also applied. To achieve 100% performance on the test set, KNN used 80% of the data in the training set, whereas K-means and GAClust used 90% and 53.3%, respectively. In Phase II, additional computational intelligence algorithms were developed, namely, GAKNN (Genetic Algorithm K-nearest neighbors), GAKmeans, and GA2Clust (genetic algorithm-based clustering algorithm 2), for genetic algorithm optimization of the training set. Genetic algorithm optimization of the training set using hybrid algorithms in Phase II resulted in 100% performance on the test set by using only 35% of the available data for training. The proposed hybrid algorithms can reliably be used for patients’ rehabilitation prognosis.
38
Ejieji, Catherine Ngozi, and Ibukun Isaac Aina. "Optimization of Water Distribution Networks Using Enhanced Heuristic Swarm Intelligence Algorithm." DIU Journal of Science & Technology 19, no. 2 (2024): 15–30. https://doi.org/10.5281/zenodo.13831561.
Full textAbstract:
In this paper, an optimal water distribution network (WDN) model was developed. An existing metaheuristic algorithms, known as the particle swarm optimization (PSO) algorithm was applied on the model to obtain the optimal cost of water distribution. A modified form of PSO called the enhanced heuristic swarm intelligence (EHSI) algorithm was also constructed and used to solve the model. Three case studies were treated. Results obtained show that PSO and EHSI algorithms minimize the total cost of water distribution network using the designed model and the EHSI algorithm performs better than PSO algorithm. present a detailed comparison of result of PSO and HSI algorithm.
39
Wang, Lingling, Yiyang Wei, and Feng Li. "Research on Hexchess Game System based Artificial Intelligence." WSEAS TRANSACTIONS ON BUSINESS AND ECONOMICS 19 (September 21, 2022): 1643–48. http://dx.doi.org/10.37394/23207.2022.19.148.
Full textAbstract:
With the rapid development of computer technology, artificial intelligence is emerging. Hex chess became popular because of its simple rules, but it also brought complex algorithms. Although the simple Monte Carlo tree search can be applied to the Hex game system, the search process is slow due to a large number of calculations. This paper proposes an improved Monte Carlo tree search algorithm based on the Upper Confidence Bound(UCB) formula to optimize the Hex game system and reduce the randomness of the Monte Carlo algorithm. To improve the efficiency of the search algorithm in the Hex game system, an effective system is adopted. Compared with the improved algorithm, not only the searching time of the Monte Carlo algorithm tree is improved, but also the performance of the algorithm is improved. At the same time, the system uses QT Creator to realize graphic interaction and complete the design of each module.
40
Qin, Yuqi. "Research on machine learning Algorithm optimization based on 0-1 coding." Journal of Physics: Conference Series 2083, no. 4 (2021): 042086. http://dx.doi.org/10.1088/1742-6596/2083/4/042086.
Full textAbstract:
Abstract Machine learning algorithm is the core of artificial intelligence, is the fundamental way to make computer intelligent, its application in all fields of artificial intelligence. Aiming at the problems of the existing algorithms in the discrete manufacturing industry, this paper proposes a new 0-1 coding method to optimize the learning algorithm, and finally proposes a learning algorithm of “IG type learning only from the best”.
41
Rao, Mrs Anitha, Monika H R, Rakshitha B C, and Seham Thaseen. "Cattle Disease Prediction Using Artificial Intelligence." International Journal for Research in Applied Science and Engineering Technology 11, no. 4 (2023): 2184–89. http://dx.doi.org/10.22214/ijraset.2023.50535.
Full textAbstract:
Abstract: In today’s world identifying cattle disease and providing proper treatments is a challenging task in the current medical sector. As it is difficult to identify thecattle disease in real time, we require a method topredict cattle disease and related patterns. There are so many research works on this topic. Most of the researchworks just presented the idea of cattle disease prediction. There are many works where implementation is done and many papers predicts cattle disease using efficient data science algorithms. Research works where implementation is done uses PYTHON language or R language as programming language for cattle disease prediction. As PYTHON language and R language supports all ready libraries to process training datasets and to predict cattle disease. Many papers use training datasets from www.kaggle.com, www.dataworld.com etc.Research works uses efficient algorithms for prediction, algorithms such as Naive Bayes algorithm, KNNclassifier, SVM classifier, Decision Tree classifier, Random Forest algorithm etc. Most of the papers got very good results of using these algorithms. So many works on this cattle disease and pattern prediction is done using data science techniques.
42
Wu, Jui-Yu. "Solving Unconstrained Global Optimization Problems via Hybrid Swarm Intelligence Approaches." Mathematical Problems in Engineering 2013 (2013): 1–15. http://dx.doi.org/10.1155/2013/256180.
Full textAbstract:
Stochastic global optimization (SGO) algorithms such as the particle swarm optimization (PSO) approach have become popular for solving unconstrained global optimization (UGO) problems. The PSO approach, which belongs to the swarm intelligence domain, does not require gradient information, enabling it to overcome this limitation of traditional nonlinear programming methods. Unfortunately, PSO algorithm implementation and performance depend on several parameters, such as cognitive parameter, social parameter, and constriction coefficient. These parameters are tuned by using trial and error. To reduce the parametrization of a PSO method, this work presents two efficient hybrid SGO approaches, namely, a real-coded genetic algorithm-based PSO (RGA-PSO) method and an artificial immune algorithm-based PSO (AIA-PSO) method. The specific parameters of the internal PSO algorithm are optimized using the external RGA and AIA approaches, and then the internal PSO algorithm is applied to solve UGO problems. The performances of the proposed RGA-PSO and AIA-PSO algorithms are then evaluated using a set of benchmark UGO problems. Numerical results indicate that, besides their ability to converge to a global minimum for each test UGO problem, the proposed RGA-PSO and AIA-PSO algorithms outperform many hybrid SGO algorithms. Thus, the RGA-PSO and AIA-PSO approaches can be considered alternative SGO approaches for solving standard-dimensional UGO problems.
43
Nachamada, Blamah. "A Reflective Swarm Intelligence Algorithm." IOSR Journal of Computer Engineering 14, no. 4 (2013): 44–48. http://dx.doi.org/10.9790/0661-1444448.
Full text
44
Aina, Ibukun Isaac, Olumuyiwa James Peter, Abayomi Ayotunde Ayoade, Festus Abiodun Oguntolu, and Matthew Olanrewaju Oluwayemi. "Enhanced Cuckoo Intelligence Search Algorithm." International Journal of Difference Equations 16, no. 1 (2021): 95. http://dx.doi.org/10.37622/ijde/16.1.2021.95-105.
Full text
45
Bacanin, Nebojsa, and Milan Tuba. "Firefly Algorithm for Cardinality Constrained Mean-Variance Portfolio Optimization Problem with Entropy Diversity Constraint." Scientific World Journal 2014 (2014): 1–16. http://dx.doi.org/10.1155/2014/721521.
Full textAbstract:
Portfolio optimization (selection) problem is an important and hard optimization problem that, with the addition of necessary realistic constraints, becomes computationally intractable. Nature-inspired metaheuristics are appropriate for solving such problems; however, literature review shows that there are very few applications of nature-inspired metaheuristics to portfolio optimization problem. This is especially true for swarm intelligence algorithms which represent the newer branch of nature-inspired algorithms. No application of any swarm intelligence metaheuristics to cardinality constrained mean-variance (CCMV) portfolio problem with entropy constraint was found in the literature. This paper introduces modified firefly algorithm (FA) for the CCMV portfolio model with entropy constraint. Firefly algorithm is one of the latest, very successful swarm intelligence algorithm; however, it exhibits some deficiencies when applied to constrained problems. To overcome lack of exploration power during early iterations, we modified the algorithm and tested it on standard portfolio benchmark data sets used in the literature. Our proposed modified firefly algorithm proved to be better than other state-of-the-art algorithms, while introduction of entropy diversity constraint further improved results.
46
AlDeeb, Bashar Abedal Mohdi, Norita Md Norwawi, and Mohammed A. Al-Betar. "A Survey on Intelligent Water Drop Algorithm." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 13, no. 10 (2014): 5075–84. http://dx.doi.org/10.24297/ijct.v13i10.2329.
Full textAbstract:
In the optimization areas, there are different algorithms that have been applied such as swarm intelligence algorithms. The researchers have found different algorithms by simulating the behaviors of various swarms of insects and animals such as fishes, bees, and ants. The intelligent water drops algorithm is one of the recently developed algorithms in the swarm intelligence field; this algorithm mimicked the dynamic of river systems. The natural water drops used to develop Intelligent Water Drop (IWD) algorithm. Therefore, the mechanisms that happen in rivers have inspired the researchers mainly to create new algorithms. IWD is a population-based algorithm where each drop represents a solution and the sharing between the drops during the search lead to a better drops (or solutions). This paper presents recent developments of the IWD algorithms in terms of theory and application. This paper concludes many of research directions that are necessary for the future of IWD algorithm.
47
Sami N. Hussein and Nazar K. Hussein. "Improving Moth-Flame Optimization Algorithm by using Slime-Mould Algorithm." Tikrit Journal of Pure Science 27, no. 1 (2022): 99–109. http://dx.doi.org/10.25130/tjps.v27i1.86.
Full textAbstract:
The MFO algorithm is one of the modern optimization algorithms based on swarm intelligence, and the SMA algorithm is also one of the latest algorithms in the same field and has the advantages of fast convergence, high convergence accuracy, robust and robust. In this research paper, we introduce an optimized algorithm for MFO based on the SMA algorithm to get better performance using the features in the two algorithms, and two different algorithms are proposed in this field. The two predicted new algorithms were tested with standard test functions and the results were encouraging compared to the standard algorithms.
48
Asker, John, Chaim Fershtman, and Ariel Pakes. "Artificial Intelligence, Algorithm Design, and Pricing." AEA Papers and Proceedings 112 (May 1, 2022): 452–56. http://dx.doi.org/10.1257/pandp.20221059.
Full textAbstract:
We calculate the time path of prices generated by algorithmic pricing games that differ in their learning protocols. Asynchronous learning occurs when the algorithm only learns about the return from the action it actually took. Synchronous learning occurs when the artificial intelligence conducts counterfactuals to learn about the returns it would have earned had it taken an alternative action. In a simple market setting, we show that synchronous updating can lead to competitive pricing, while asynchronous updating can lead to pricing close to monopoly levels. However, building simple economic reasoning into the asynchronous algorithms significantly modifies the prices it generates.
49
P., Matrenin, Myasnichenko V., Sdobnyakov N., et al. "Generalized swarm intelligence algorithms with domain-specific heuristics." International Journal of Artificial Intelligence (IJ-AI) 10, no. 1 (2021): 157–65. https://doi.org/10.11591/ijai.v10.i1.pp157-165.
Full textAbstract:
In recent years, hybrid approaches on population-based algorithms are more often applied in industrial settings. In this paper, we present the approach of a combination of universal, problem-free swarm intelligence (SI) algorithms with simple deterministic domain-specific heuristic algorithms. The approach focuses on improving efficiency by sharing the advantages of domainspecific heuristic and swarm algorithms. A heuristic algorithm helps take into account the specifics of the problem and effectively translate the positions of agents (particle, ant, bee) into the problem's solution. And a swarm algorithm provides an increase in the adaptability and efficiency of the approach due to stochastic and self-organized properties. We demonstrate this approach on two non-trivial optimization tasks: scheduling problem and finding the minimum distance between 3D isomers.
50
Alawd, Eng Hassan. "Using Swarm Intelligence Algorithms to Boost AI Performance." Stardom Scientific Journals of Natural and Engineering Sciences 3, no. 2 (2025): 1–39. https://doi.org/10.70170/wbysd9873021.
Full textAbstract:
This research explores Swarm Intelligence algorithms, an advanced field in artificial intelligence that mimics the collective behavior of organisms like ants and bees to solve complex problems. These algorithms are characterized by decentralization, adaptability, and parallel processing, making them effective in optimization tasks. The Ant Colony Optimization (ACO) algorithm models ant foraging behavior to solve path optimization problems, such as the Traveling Salesman Problem (TSP), showcasing scalability and gradual performance improvement. Similarly, the Bee Algorithm mimics bee foraging behavior for resource optimization and multi-dimensional search, offering efficiency in various applications. A case study compares the performance of ACO and the Bee Algorithm in solving a real-world problem, such as optimizing transportation routes. The results evaluate their speed, accuracy, and ability to handle complexity, highlighting the practical potential of Swarm Intelligence in AI-driven solutions.