| 1. |
- Al-Shourbaji, Ibrahim, et al.
(författare)
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Artificial Ecosystem-Based Optimization with Dwarf Mongoose Optimization for Feature Selection and Global Optimization Problems
- 2023
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Ingår i: International Journal of Computational Intelligence Systems. - : SPRINGERNATURE. - 1875-6891 .- 1875-6883. ; 16:1
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Tidskriftsartikel (refereegranskat)abstract
- Meta-Heuristic (MH) algorithms have recently proven successful in a broad range of applications because of their strong capabilities in picking the optimal features and removing redundant and irrelevant features. Artificial Ecosystem-based Optimization (AEO) shows extraordinary ability in the exploration stage and poor exploitation because of its stochastic nature. Dwarf Mongoose Optimization Algorithm (DMOA) is a recent MH algorithm showing a high exploitation capability. This paper proposes AEO-DMOA Feature Selection (FS) by integrating AEO and DMOA to develop an efficient FS algorithm with a better equilibrium between exploration and exploitation. The performance of the AEO-DMOA is investigated on seven datasets from different domains and a collection of twenty-eight global optimization functions, eighteen CEC2017, and ten CEC2019 benchmark functions. Comparative study and statistical analysis demonstrate that AEO-DMOA gives competitive results and is statistically significant compared to other popular MH approaches. The benchmark function results also indicate enhanced performance in high-dimensional search space.
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| 2. |
- Alhenawi, Esra'a, et al.
(författare)
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Solving Traveling Salesman Problem Using Parallel River Formation Dynamics Optimization Algorithm on Multi-core Architecture Using Apache Spark
- 2024
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Ingår i: International Journal of Computational Intelligence Systems. - : SPRINGERNATURE. - 1875-6891 .- 1875-6883. ; 17:1
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Tidskriftsartikel (refereegranskat)abstract
- According to Moore's law, computer processing hardware technology performance is doubled every year. To make effective use of this technological development, the algorithmic solutions have to be developed at the same speed. Consequently, it is necessary to design parallel algorithms to be implemented on parallel machines. This helps to exploit the multi-core environment by executing multiple instructions simultaneously on multiple processors. Traveling Salesman (TSP) is a challenging non-deterministic-hard optimization problem that has exponential running time using brute-force methods. TSP is concerned with finding the shortest path starting with a point and returning to that point after visiting the list of points, provided that these points are visited only once. Meta-heuristic optimization algorithms have been used to tackle TSP and find near-optimal solutions in a reasonable time. This paper proposes a parallel River Formation Dynamics Optimization Algorithm (RFD) to solve the TSP problem. The parallelization technique depends on dividing the population into different processors using the Map-Reduce framework in Apache Spark. The experiments are accomplished in three phases. The first phase compares the speedup, running time, and efficiency of RFD on 1 (sequential RFD), 4, 8, and 16 cores. The second phase compares the proposed parallel RFD with three parallel water-based algorithms, namely the Water Flow algorithm, Intelligent Water Drops, and the Water Cycle Algorithm. To achieve fairness, all algorithms are implemented using the same system specifications and the same values for shared parameters. The third phase compares the proposed parallel RFD with the reported results of metaheuristic algorithms that were used to solve TSP in the literature. The results demonstrate that the RFD algorithm has the best performance for the majority of problem instances, achieving the lowest running times across different core counts. Our findings highlight the importance of selecting the most suitable algorithm and core count based on the problem characteristics to achieve optimal performance in parallel optimization.
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| 3. |
- Aliahmadipour, Laya, et al.
(författare)
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A definition for hesitant fuzzy partitions
- 2016
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Ingår i: International Journal of Computational Intelligence Systems. - : Taylor & Francis Group. - 1875-6891 .- 1875-6883. ; 9:3, s. 497-505
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Tidskriftsartikel (refereegranskat)abstract
- In this paper, we define hesitant fuzzy partitions (H-fuzzy partitions) to consider the results of standard fuzzy clustering family (e.g. fuzzy c-means and intuitionistic fuzzy c-means). We define a method to construct H-fuzzy partitions from a set of fuzzy clusters obtained from several executions of fuzzy clustering algorithms with various initialization of their parameters. Our purpose is to consider some local optimal solutions to find a global optimal solution also letting the user to consider various reliable membership values and cluster centers to evaluate her/his problem using different cluster validity indices.
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| 4. |
- Danielson, Mats, et al.
(författare)
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Weighting Under Ambiguous Preferences and Imprecise Differences in a Cardinal Rank Ordering Process
- 2014
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Ingår i: International Journal of Computational Intelligence Systems. - : Springer Science and Business Media LLC. - 1875-6891 .- 1875-6883. ; 7:S1, s. 105-112
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Tidskriftsartikel (refereegranskat)abstract
- The limited amount of good tools for supporting elicitation of preference information in multi-criteria decision analysis (MCDA) causes practical problem. In our experiences, this can be remedied by allowing more relaxed input statements from decision-makers, causing the elicitation process to be less cognitively demanding. Furthermore, it should not be too time consuming and must be able to actually use of the information the decision-maker is able to supply. In this paper, we propose a useful weight elicitation method for MAVT/MAUT decision making, which builds on the ideas of rank-order methods, but increases the precision by adding numerically imprecise cardinal information as well.
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| 5. |
- Ekinci, Serdar, et al.
(författare)
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Revolutionizing Vehicle Cruise Control: An Elite Opposition-Based Pattern Search Mechanism Augmented INFO Algorithm for Enhanced Controller Design
- 2023
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Ingår i: International Journal of Computational Intelligence Systems. - : SPRINGERNATURE. - 1875-6891 .- 1875-6883. ; 16:1
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Tidskriftsartikel (refereegranskat)abstract
- This paper presents a groundbreaking approach to enhance the performance of a vehicle cruise control system-a crucial aspect of road safety. The work offers two key contributions. Firstly, a state-of-the-art metaheuristic algorithm is proposed by augmenting the performance of the weighted mean of vectors (INFO) algorithm using pattern search and elite opposition-based learning mechanisms. The resulting boosted INFO (b-INFO) algorithm surpasses the original INFO, marine predators, and gravitational search algorithms in terms of performance on benchmark functions, including unimodal, multimodal, and fixed-dimensional multimodal functions. Secondly, a novel proportional, fractional order integral, derivative plus double derivative with filter ((PIDNDN2)-D-?-N-2) controller is proposed as a more efficient control structure for vehicle cruise control systems. An objective function is utilized to determine the optimal values for the controller parameters, and the proposed methods performance is compared against a range of recent approaches. Results demonstrate that the b-INFO algorithm-based (PIDNDN2)-D-?-N-2 controller is the most efficient and superior method for controlling a vehicle cruise control system. Moreo-ver, this work represents the first report of a (PIDNDN2)-D-?-N-2 controllers implementation for vehicle cruise control systems, underscoring the novelty and significance of this research. The proposed methods exceptional ability is further confirmed by comparisons with the genetic algorithm, ant lion optimizer, atom search optimizer, arithmetic optimization algorithm, slime mold algorithm, L & eacute;vy flight distribution algorithm, manta ray foraging optimization, and hunger games search-based proportional-integral-derivative (PID), along with Harris hawks optimization-based PID and fractional order PID control-lers. This work marks a remarkable milestone toward safer and more efficient vehicle cruise control systems.
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| 6. |
- Giri, Chandadevi, et al.
(författare)
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Exploitation of Social Network Data for Forecasting Garment Sales
- 2019
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Ingår i: International Journal of Computational Intelligence Systems. - : Springer Science and Business Media LLC. - 1875-6891 .- 1875-6883. ; 12:2, s. 1423-1435
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Tidskriftsartikel (refereegranskat)abstract
- Growing use of social media such as Twitter, Instagram, Facebook, etc., by consumers leads to the vast repository of consumer generated data. Collecting and exploiting these data has been a great challenge for clothing industry. This paper aims to study the impact of Twitter on garment sales. In this direction, we have collected tweets and sales data for one of the popular apparel brands for 6 months from April 2018 – September 2018. Lexicon Approach was used to classify Tweets by sentence using Naïve Bayes model applying enhanced version of Lexicon dictionary. Sentiments were extracted from consumer tweets, which was used to map the uncertainty in forecasting model. The results from this study indicate that there is a correlation between the apparel sales and consumer tweets for an apparel brand. “Social Media Based Forecasting (SMBF)” is designed which is a fuzzy time series forecasting model to forecast sales using historical sales data and social media data. SMBF was evaluated and its performance was compared with Exponential Forecasting (EF) model. SMBF model outperforms the EF model. The result from this study demonstrated that social media data helps to improve the forecasting of garment sales and this model could be easily integrated to any time series forecasting model.
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| 7. |
- Javeed, Ashir, 1989-, et al.
(författare)
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Predictive Power of XGBoost_BiLSTM Model : A Machine-Learning Approach for Accurate Sleep Apnea Detection Using Electronic Health Data
- 2023
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Ingår i: International Journal of Computational Intelligence Systems. - : Springer Nature. - 1875-6891 .- 1875-6883. ; 16:1
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Tidskriftsartikel (refereegranskat)abstract
- Sleep apnea is a common disorder that can cause pauses in breathing and can last from a few seconds to several minutes, as well as shallow breathing or complete cessation of breathing. Obstructive sleep apnea is strongly associated with the risk of developing several heart diseases, including coronary heart disease, heart attack, heart failure, and stroke. In addition, obstructive sleep apnea increases the risk of developing irregular heartbeats (arrhythmias), which can lead to low blood pressure. To prevent these conditions, this study presents a novel machine-learning (ML) model for predicting sleep apnea based on electronic health data that provides accurate predictions and helps in identifying the risk factors that contribute to the development of sleep apnea. The dataset used in the study includes 75 features and 10,765 samples from the Swedish National Study on Aging and Care (SNAC). The proposed model is based on two modules: the XGBoost module assesses the most important features from feature space, while the Bidirectional Long Short-Term Memory Networks (BiLSTM) module classifies the probability of sleep apnea. Using a cross-validation scheme, the proposed XGBoost_BiLSTM algorithm achieves an accuracy of 97% while using only the six most significant features from the dataset. The model’s performance is also compared with conventional long-short-term memory networks (LSTM) and other state-of-the-art ML models. The results of the study suggest that the proposed model improved the diagnosis and treatment of sleep apnea by identifying the risk factors.
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| 8. |
- Karvonen, Niklas, 1979-, et al.
(författare)
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Classifier Optimized for Resource-constrained Pervasive Systems and Energy-efficiency
- 2017
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Ingår i: International Journal of Computational Intelligence Systems. - : Atlantis Press. - 1875-6891 .- 1875-6883. ; 10:1, s. 1272-1279
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Tidskriftsartikel (refereegranskat)abstract
- Computational intelligence is often used in smart environment applications in order to determine a user’scontext. Many computational intelligence algorithms are complex and resource-consuming which can beproblematic for implementation devices such as FPGA:s, ASIC:s and low-level microcontrollers. Thesetypes of devices are, however, highly useful in pervasive and mobile computing due to their small size,energy-efficiency and ability to provide fast real-time responses. In this paper, we propose a classi-fier, CORPSE, specifically targeted for implementation in FPGA:s, ASIC:s or low-level microcontrollers.CORPSE has a small memory footprint, is computationally inexpensive, and is suitable for parallel processing.The classifier was evaluated on eight different datasets of various types. Our results show thatCORPSE, despite its simplistic design, has comparable performance to some common machine learningalgorithms. This makes the classifier a viable choice for use in pervasive systems that have limitedresources, requires energy-efficiency, or have the need for fast real-time responses.
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| 9. |
- Leon, Miguel, et al.
(författare)
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A Novel Memetic Framework for Enhancing Differential Evolution Algorithms via Combination With Alopex Local Search
- 2019
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Ingår i: International Journal of Computational Intelligence Systems. - : ATLANTIS PRESS. - 1875-6891 .- 1875-6883. ; 12:2, s. 795-808
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Tidskriftsartikel (refereegranskat)abstract
- Differential evolution (DE) represents a class of population-based optimization techniques that uses differences of vectors to search for optimal solutions in the search space. However, promising solutions/ regions are not adequately exploited by a traditional DE algorithm. Memetic computing has been popular in recent years to enhance the exploitation of global algorithms via incorporation of local search. This paper proposes a new memetic framework to enhance DE algorithms using Alopex Local Search (MFDEALS). The novelty of the proposed MFDEALS framework lies in that the behavior of exploitation (by Alopex local search) can be controlled based on the DE global exploration status (population diversity and search stage). Additionally, an adaptive parameter inside the Alopex local search enables smooth transition of its behavior from exploratory to exploitative during the search process. A study of the important components of MFDEALS shows that there is a synergy between them. MFDEALS has been integrated with both the canonical DE method and the adaptive DE algorithm L-SHADE, leading to the MDEALS and ML-SHADEALS algorithms, respectively. Both algorithms were tested on the benchmark functions from the IEEE CEC'2014 Conference. The experiment results show that Memetic Differential Evolution with Alopex Local Search (MDEALS) not only improves the original DE algorithm but also outperforms other memetic DE algorithms by obtaining better quality solutions. Further, the comparison between ML-SHADEALS and L-SHADE demonstrates that applying the MFDEALS framework with Alopex local search can significantly enhance the performance of L-SHADE.
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| 10. |
- Lun, Zhao, et al.
(författare)
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Skip-YOLO : Domestic Garbage Detection Using Deep Learning Method in Complex Multi-scenes
- 2023
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Ingår i: International Journal of Computational Intelligence Systems. - : Springer Science+Business Media B.V.. - 1875-6891 .- 1875-6883. ; 16:1
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Tidskriftsartikel (refereegranskat)abstract
- It is of great significance to identify all types of domestic garbage quickly and intelligently to improve people's quality of life. Based on the visual analysis of feature map changes in different neural networks, a Skip-YOLO model is proposed for real-life garbage detection, targeting the problem of recognizing garbage with similar features. First, the receptive field of the model is enlarged through the large-size convolution kernel which enhanced the shallow information of images. Second, the high-dimensional features of the garbage maps are extracted by dense convolutional blocks. The sensitivity of similar features in the same type of garbage increases by strengthening the sharing of shallow low semantics and deep high semantics information. Finally, multiscale high-dimensional feature maps are integrated and routed to the YOLO layer for predicting garbage type and location. The overall detection accuracy is increased by 22.5% and the average recall rate is increased by 18.6% comparing the experimental results with the YOLOv3 analysis. In qualitative comparison, it successfully detects domestic garbage in complex multi-scenes. In addition, this approach alleviates the overfitting problem of deep residual blocks. The application case of waste sorting production line is used to further highlight the model generalization performance of the method. © 2023, Springer Nature B.V.
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