A Novel Artificial Neural Network (ANN) Using The Mayfly Algorithm for Classification

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Feature selection of unbalanced breast cancer data using particle swarm optimization

Breast cancer is one of the significant deaths causing diseases of women around the globe. Therefore, high accuracy in cancer prediction models is vital to improving patients’ treatment quality and survivability rate. In this work, we presented a new method namely improved balancing particle swarm optimization (IBPSO) algorithm to predict the stage of breast cancer using unbalanced surveillance epidemiology and end result (USEER) data. The work contributes in two directions. First, design and implement an improved particle swarm optimization (IPSO) algorithm to avoid the local minima while reducing USEER data’s dimensionality. The improvement comes primarily through employing the cross-over ability of the genetic algorithm as a fitness function while using the correlation-based function to guide the selection task to a minimal feature subset of USEER sufficiently to describe the universe. Second, develop an improved synthetic minority over-sampling technique (ISMOTE) that avoid overfitting problem while efficiently balance USEER. ISMOTE generates the new objects based on the average of the two objects with the smallest and largest distance from the centroid object of the minority class. The experiments and analysis show that the proposed IBPSO is feasible and effective, outperforms other state-of-the-art methods; in minimizing the features with an accuracy of 98.45%

Optimization of multi-holes drilling path using particle swarm optimization

Multi-hole drilling is a manufacturing process that is commonly used in industries. In this process, the tool movement and switching, on average, take 70% of the total machining time. There are many applications of multi-hole drilling, such as in mould, die-making and printed circuit board (PCB). One way to improve the multi-hole drilling is by optimising the tool path in the process. This research aims to model and optimise multi-hole drilling problems using Particle Swarm Optimisation (PSO) algorithm. The study begins by modelling the multi-hole drilling problems using the Travelling Salesman Problem (TSP) concept. The objective function was set to minimise the total tool path distance. Then, the PSO was formulated to minimise total length in multi-hole drilling. The main issue in this stage was to convert the continuous encoding in PSO to permutation problems as in multi-hole drilling. For this purpose, a topological sorting procedure based on the most prominent particle rule was implemented. The algorithm was tested on 15 test problems where between 10 to 150 holes were randomly generated. The performance of PSO was then compared with other meta-heuristic algorithms, including Genetic Algorithm (GA) and Ant Colony Optimisation (ACO), Whale Optimisation Algorithm (WOA), Ant Lion Optimiser (ALO), Dragonfly Algorithm (DA), Grasshopper Optimisation Algorithm (GOA), Moth Flame Optimisation (MFO) and Sine Cosine Algorithm (SCA). Then, a validation experiment was conducted by implementing the PSO generated tool path against the commercial CAD-CAM path. In this stage, the machining time was measured. The results from the computational experiment indicated that the proposed PSO algorithm came out with the best solution in 10 out of the 15 test problems. In the meantime, the validation experiment result proved that the PSO generated tool path provides faster machining time compared with the commercial CAD-CAM path by 5% on average. The results clearly showed that PSO has a great potential to be applied in the multi-hole drilling process. The findings from this research could benefit the manufacturing industry to improve their productivity using existing resources

A NOVEL DISCRETE RAT SWARM OPTIMIZATION ALGORITHM FOR THE QUADRATIC ASSIGNMENT PROBLEM

The quadratic assignment problem (QAP) is an NP-hard problem with a wide range of applications in many real-world applications. This study introduces a discrete rat swarm optimizer (DRSO)algorithm for the first time as a solution to the QAP and demonstrates its effectiveness in terms of solution quality and computational efficiency. To address the combinatorial nature of the QAP, a mapping strategy is introduced to convert real values into discrete values, and mathematical operators are redefined to make then suitable for combinatorial problems. Additionally, a solution quality improvement strategy based on local search heuristics such as 2-opt and 3-opt is proposed. Simulations with test instances from the QAPLIB test library validate the effectiveness of the DRSO algorithm, and statistical analysis using the Wilcoxon parametric test confirms its performance. Comparative analysis with other algorithms demonstrates the superior performance of DRSO in terms of solution quality, convergence speed, and deviation from the best-known values, making it a promising approach for solving the QAP

30th Anniversary of Applied Intelligence: A combination of bibliometrics and thematic analysis using SciMAT

Applied Intelligence is one of the most important international scientific journals in the field of artificial intelligence. From 1991, Applied Intelligence has been oriented to support research advances in new and innovative intelligent systems, methodologies, and their applications in solving real-life complex problems. In this way, Applied Intelligence hosts more than 2,400 publications and achieves around 31,800 citations. Moreover, Applied Intelligence is recognized by the industrial, academic, and scientific communities as a source of the latest innovative and advanced solutions in intelligent manufacturing, privacy-preserving systems, risk analysis, knowledge-based management, modern techniques to improve healthcare systems, methods to assist government, and solving industrial problems that are too complex to be solved through conventional approaches. Bearing in mind that Applied Intelligence celebrates its 30th anniversary in 2021, it is appropriate to analyze its bibliometric performance, conceptual structure, and thematic evolution. To do that, this paper conducts a bibliometric performance and conceptual structure analysis of Applied Intelligence from 1991 to 2020 using SciMAT. Firstly, the performance of the journal is analyzed according to the data retrieved from Scopus, putting the focus on the productivity of the authors, citations, countries, organizations, funding agencies, and most relevant publications. Finally, the conceptual structure of the journal is analyzed with the bibliometric software tool SciMAT, identifying the main thematic areas that have been the object of research and their composition, relationship, and evolution during the period analyzed

An Efficient High-Dimensional Gene Selection Approach based on Binary Horse Herd Optimization Algorithm for Biological Data Classification

The Horse Herd Optimization Algorithm (HOA) is a new meta-heuristic algorithm based on the behaviors of horses at different ages. The HOA was introduced recently to solve complex and high-dimensional problems. This paper proposes a binary version of the Horse Herd Optimization Algorithm (BHOA) in order to solve discrete problems and select prominent feature subsets. Moreover, this study provides a novel hybrid feature selection framework based on the BHOA and a minimum Redundancy Maximum Relevance (MRMR) filter method. This hybrid feature selection, which is more computationally efficient, produces a beneficial subset of relevant and informative features. Since feature selection is a binary problem, we have applied a new Transfer Function (TF), called X-shape TF, which transforms continuous problems into binary search spaces. Furthermore, the Support Vector Machine (SVM) is utilized to examine the efficiency of the proposed method on ten microarray datasets, namely Lymphoma, Prostate, Brain-1, DLBCL, SRBCT, Leukemia, Ovarian, Colon, Lung, and MLL. In comparison to other state-of-the-art, such as the Gray Wolf (GW), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA), the proposed hybrid method (MRMR-BHOA) demonstrates superior performance in terms of accuracy and minimum selected features. Also, experimental results prove that the X-Shaped BHOA approach outperforms others methods

A NOVEL APPROACH TO ORBITAL DEBRIS MITIGATION

Since mankind launched the first satellite into orbit in 1957, we have been inadvertently, yet deliberately, creating an environment in space that may ultimately lead to the end of our space exploration. Space debris, more specifically, orbital debris is a growing problem that must be dealt with sooner, rather than later. Several ideas have been developed to address the complex problem of orbital debris mitigation. This research will investigate the possibility of removing orbital debris from the Low Earth Orbit (LEO) regime by using a metaheuristic algorithm to maximize collection of debris resulting from the February 2009 on-orbit collision of Iridium 33 and Cosmos 2251. This treatment will concentrate on the Iridium debris field for analysis. This research is necessary today, more than ever, as we embark on the launch of thousands of LEO spacecraft, which could result in the realization of the Kessler Syndrome, “The certain risk of failure on launch or during operations due to an on-orbit collision with debris” (Kessler & Cour-Palais, 1978)

Revisión de la optimización de Bess en sistemas de potencia

The increasing penetration of Distributed Energy Resources has imposed several challenges in the analysis and operation of power systems, mainly due to the uncertainties in primary resource. In the last decade, implementation of Battery Energy Storage Systems in electric networks has caught the interest in research since the results have shown multiple positive effects when deployed optimally. In this paper, a review in the optimization of battery storage systems in power systems is presented. Firstly, an overview of the context in which battery storage systems are implemented, their operation framework, chemistries and a first glance of optimization is shown. Then, formulations and optimization frameworks are detailed for optimization problems found in recent literature. Next, A review of the optimization techniques implemented or proposed, and a basic explanation of the more recurrent ones is presented. Finally, the results of the review are discussed. It is concluded that optimization problems involving battery storage systems are a trending topic for research, in which a vast quantity of more complex formulations have been proposed for Steady State and Transient Analysis, due to the inclusion of stochasticity, multi-periodicity and multi-objective frameworks. It was found that the use of Metaheuristics is dominant in the analysis of complex, multivariate and multi-objective problems while relaxations, simplifications, linearization, and single objective adaptations have enabled the use of traditional, more efficient, and exact techniques. Hybridization in metaheuristics has been important topic of research that has shown better results in terms of efficiency and solution quality.La creciente penetración de recursos distribuidos ha impuesto desafíos en el análisis y operación de sistemas de potencia, principalmente debido a incertidumbres en los recursos primarios. En la última década, la implementación de sistemas de almacenamiento por baterías en redes eléctricas ha captado el interés en la investigación, ya que los resultados han demostrado efectos positivos cuando se despliegan óptimamente. En este trabajo se presenta una revisión de la optimización de sistemas de almacenamiento por baterías en sistemas de potencia. Pare ello se procedió, primero, a mostrar el contexto en el cual se implementan los sistemas de baterías, su marco de operación, las tecnologías y las bases de optimización. Luego, fueron detallados la formulación y el marco de optimización de algunos de los problemas de optimización encontrados en literatura reciente. Posteriormente se presentó una revisión de las técnicas de optimización implementadas o propuestas recientemente y una explicación básica de las técnicas más recurrentes. Finalmente, se discutieron los resultados de la revisión. Se obtuvo como resultados que los problemas de optimización con sistemas de almacenamiento por baterías son un tema de tendencia para la investigación, en el que se han propuesto diversas formulaciones para el análisis en estado estacionario y transitorio, en problemas multiperiodo que incluyen la estocasticidad y formulaciones multiobjetivo. Adicionalmente, se encontró que el uso de técnicas metaheurísticas es dominante en el análisis de problemas complejos, multivariados y multiobjetivo, mientras que la implementación de relajaciones, simplificaciones, linealizaciones y la adaptación mono-objetivo ha permitido el uso de técnicas más eficientes y exactas. La hibridación de técnicas metaheurísticas ha sido un tema relevante para la investigación que ha mostrado mejorías en los resultados en términos de eficiencia y calidad de las soluciones