In this paper, we propose chimp optimization algorithm (ChOA) for selection of feature to increase the classification accuracy of heart disease diagnosis. In this approach, noises contained in the cardiac image are re...
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In this paper, we propose chimp optimization algorithm (ChOA) for selection of feature to increase the classification accuracy of heart disease diagnosis. In this approach, noises contained in the cardiac image are removed using median filter initially. Then, GLCM features are extracted from the cardiac image. Among the extracted features, optimal features are chosen using ChOA algorithm. These selected features taken as input to the classifier. In this approach, support vector neural network (SVNN) is used as classifier. The classifier classifies the image into normal and abnormal. Simulation results depict that the ChOA-based SVNN performs superior than the conventional SVNN, ANN, KNN and SVM in terms of accuracy.
The chimp optimization algorithm (ChOA) is a recently introduced metaheuristic algorithm inspired by nature. This algorithm identified four types of chimpanzee groups: attacker, barrier, chaser, and driver, and propos...
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The chimp optimization algorithm (ChOA) is a recently introduced metaheuristic algorithm inspired by nature. This algorithm identified four types of chimpanzee groups: attacker, barrier, chaser, and driver, and proposed a suitable mathematical model for them, based on the various intelligence and sexual motivations of chimpanzees. However, this algorithm is not more successful in the convergence rate and escaping of the local optimum trap in solving high-dimensional problems. Although it and some of its variants use some strategies to overcome these problems, it is observed that it is not sufficient. Therefore, in this study, a newly expanded variant is described. In the algorithm, called Ex-ChOA, hybrid models are proposed for position updates of search agents, and a dynamic switching mechanism is provided for transition phases. This flexible structure solves the slow convergence problem of ChOA and improves its accuracy in multi-dimensional problems. Therefore, it tries to achieve success in solving global, complex, and constrained problems. The performance of the proposed algorithm was analyzed on a total of 34 benchmark functions and a total of 17 real-world optimizations, including classical, constrained, and modern engineering problems. According to the results obtained, the proposed algorithm performs better or equivalent performance than the compared algorithms.
Neural mass model (NMM) serves as an effective tool for understanding and exploring the complex dynamics of brain systems. Accurately estimating the model parameters of NMM is highly important for building brain model...
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Neural mass model (NMM) serves as an effective tool for understanding and exploring the complex dynamics of brain systems. Accurately estimating the model parameters of NMM is highly important for building brain models driven by observed electroencephalogram (EEG) data. However, existing methods for comparing model output with observed data primarily focus on one-dimensional linear comparisons, overlooking the highdimensional nonlinear dynamics and Riemannian geometry characteristics of EEG data. To address this issue, we propose a novel parameter estimation method for NMM based on the improved chimp optimization algorithm (ChOA) and Riemannian geometry. First, ChOA is improved by incorporating the Aquila optimizer (AOChOA) is used to improve the convergence efficiency and accuracy of the nonlinear optimization problem. Then, a novel loss function based on the Riemannian geometry of symmetric positive definite matrices (LRSPD) is constructed to capture the high-dimensional nonlinear dynamics of EEG signals. Finally, we validate the effectiveness of the proposed method by using the model output with fixed model parameters and real EEG signals as observed data, respectively. When using the model output with fixed model parameters, the loss function LRSPD yielded more accurate parameter estimation results compared to others, with the fitted model closely matching the dynamics of the observed data. When using real EEG data, the proposed method successfully recovered differences in EEG dynamics for subjects at different consciousness levels. Additionally, our study reveals the neural mechanisms of decreased consciousness level in patients with disorders of consciousness (DOC), characterized by increased inhibitory neural activity of the brain.
A Mobile Ad hoc Network (MANET) is a widely used and vibrant network, which is unevenly distributed in the environment. It is a set of self-organized independent mobile nodes interconnected without any centralized inf...
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A Mobile Ad hoc Network (MANET) is a widely used and vibrant network, which is unevenly distributed in the environment. It is a set of self-organized independent mobile nodes interconnected without any centralized infrastructure. However, this topology nature makes the network prompt to various network security attacks. To address this issue, this paper proposes a Coot chimp optimization algorithm- Deep Q-Network (CChOA-DQN) for detecting the black hole attacks in MANET. Here, the designed CChOA is used for the identification of the optimal route in the MANET for transmitting data, which takes into fitness parameters, such as energy, distance, neighbourhood quality, link quality, and trust. The features are extracted using the Fisher score and augmented using the over-sampling technique, which is further allowed for the detection process using DQN. Also, the weights of the DQN are enhanced using the CChOA algorithmic technique to enhance the detection performance. Additionally, the results gathered from the experiment revealed that CChOA attained high performance with a maximum of 0.983 Mbps throughput, 93.70 % Packet Delivery Ratio (PDR), and minimum end-end delay of 0.096Sec, Residual energy of 0.119 J, and Control overhead of 4473.11. Also, the CChOA-DQN technique achieved the minimum False Positive Rate (FPR) of 0.122, False Negative Rate (FNR) of 0.121, Computation time of 0.153 and Run time of 0.094.
The machine learning process in high-dimensional datasets is far more complicated than in low-dimensional datasets. In high-dimensional datasets, Feature Selection (FS) is necessary to decrease the complexity of learn...
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The machine learning process in high-dimensional datasets is far more complicated than in low-dimensional datasets. In high-dimensional datasets, Feature Selection (FS) is necessary to decrease the complexity of learning. However, FS in high-dimensional datasets is a complex process that requires the combination of several search techniques. The chimp optimization algorithm, known as ChOA, is a new meta-heuristic method inspired by the chimps' individual intellect and sexual incentive in cooperative hunting. It is basically employed in solving complex continuous optimization problems, while its binary version is frequently utilized in solving difficult binary optimization problems. Both versions of ChOA are subject to premature convergence and are incapable of effectively solving high-dimensional optimization problems. This paper proposes the Binary Improved ChOA algorithm (BICHOA) for solving the bi-objective, high-dimensional FS problems (i.e., high-dimensional FS problems that aim to maximize the classifier's accuracy and minimize the number of selected features from a dataset). BICHOA improves the performance of ChOA using four new exploration and exploitation techniques. First, it employs the opposition-based learning approach to initially create a population of diverse binary feasible solutions. Second, it incorporates the L & eacute;vy mutation function in the main probabilistic update function of ChOA to boost its searching and exploring capabilities. Third, it uses an iterative exploration technique based on an exploratory local search method called the beta-hill climbing algorithm. Finally, it employs a new binary time-varying transfer function to calculate binary feasible solutions from the continuous feasible solutions generated by the update equations of the ChOA and beta-hill climbing algorithms. BICHOA's performance was assessed and compared against six machine learning classifiers, five integer programming methods, and nine efficient popular optimiza
Aiming at the shortcomings of chimp optimization algorithm (ChOA), which is easy to fall into local optimal value and imbalance between global exploration ability and local exploitation ability. To improve ChOA from t...
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Aiming at the shortcomings of chimp optimization algorithm (ChOA), which is easy to fall into local optimal value and imbalance between global exploration ability and local exploitation ability. To improve ChOA from the perspective of multi-strategy mixing, MSchimp was proposed, and the algorithm was applied to global optimization and minimum spanning tree problems. The main research work of this paper is as follows: (1) In the initialization stage of ChOA, an opposition-based learning strategy was introduced to improve the population diversity;Sine Cosine algorithm (SCA) was introduced in the exploitation process to improve the convergence speed and accuracy of the algorithm in the later stage, so as to balance the exploration and exploitation capabilities of the algorithm. (2) The improved algorithm was compared with different types of meta-heuristic algorithms in 20 benchmark functions and CEC 2019 test sets, and was used to solve the minimum spanning tree. The experimental results show that the improved ChOA has significantly improved the ability to find the optimal value, which verifies the effectiveness and feasibility of MSchimp. Compared with other algorithms, the algorithm proposed in this paper has strong competitiveness.
To enhance the diversity and distribution uniformity of initial population,as well as to avoid local extrema in the chimp optimization algorithm(CHOA),this paper improves the CHOA based on chaos initialization and Cau...
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To enhance the diversity and distribution uniformity of initial population,as well as to avoid local extrema in the chimp optimization algorithm(CHOA),this paper improves the CHOA based on chaos initialization and Cauchy ***,Sin chaos is introduced to improve the random population initialization scheme of the CHOA,which not only guarantees the diversity of the population,but also enhances the distribution uniformity of the initial ***,Cauchy mutation is added to optimize the global search ability of the CHOA in the process of position(threshold)updating to avoid the CHOA falling into local ***,an improved CHOA was formed through the combination of chaos initialization and Cauchy mutation(CICMCHOA),then taking fuzzy Kapur as the objective function,this paper applied CICMCHOA to natural and medical image segmentation,and compared it with four algorithms,including the improved Satin Bowerbird optimizer(ISBO),Cuckoo Search(ICS),*** experimental results deriving from visual and specific indicators demonstrate that CICMCHOA delivers superior segmentation effects in image segmentation.
Earthquakes are complex phenomena that generate various forms of clusters that are highly correlated in the space and time domains. To study the dynamic properties of seismicity and estimate risk, the earthquake catal...
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Earthquakes are complex phenomena that generate various forms of clusters that are highly correlated in the space and time domains. To study the dynamic properties of seismicity and estimate risk, the earthquake catalog needs to be separated into clustered and background events. Here, seismicity de-clustering is formulated as a binary many-objective optimization problem. A swarm-based many-objective chimp optimization algorithm (MaOChOA) is proposed to segregate the earthquake catalog into aftershocks and background events. The reference point-based leader selection strategy is adopted to update the solution. The proposed MaOChOA is evaluated on many objective benchmark test functions. The results are compared with existing many-objective techniques using performance metrics such as generational distance, inverse generational distance, spacing metric, hyper-volume distance, and runtime. Further, de-clustering the earthquake catalog is performed using a binary version of many-objective chimp optimization algorithms (BMaChOA), where the sigmoid function is used in the position update mechanism. The BMaChOA is applied to 32-year historical earthquake catalogs of the Japan, California, Indonesia, and Himalayan regions. The potential of the proposed algorithm is reported by comparing it to five benchmark de-clustering techniques. The results are validated using the epicenter plot, inter-event time Vs. inter-event distance plot, cumulative plot, lambda-plot, and statistical parameters such as the m-Morisita index, coefficient of variance, average nearest neighbor, and nearest neighbor distance.
The importance of efficient path planning (PP) cannot be overstated in the domain of robots, as it involves the utilization of intelligent algorithms to determine the optimal trajectory for robot to navigate between t...
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The importance of efficient path planning (PP) cannot be overstated in the domain of robots, as it involves the utilization of intelligent algorithms to determine the optimal trajectory for robot to navigate between two given points. The main target of PP is to determine potential trajectories for robot operating in a complex environment containing various obstacles. The implementation of these movements should facilitate robot in traversing a path without encountering any collisions, starting from its initial location and reaching the intended destination. In order to address the challenges associated with robot PP, this study applies the chimp optimization algorithm (CHOA) as a local searching (LS) technique and the evolutionary programming algorithm (EPA) to enhance the potential route discovered via a collection of LSs. In order to address CHOA's tendency to converge to local minima, a new updating technique called twin-reinforced (TR) is developed. In order to assess the effectiveness of the TRCHOA, we conducted a comparative analysis with other widely used meta-heuristic algorithms that are typically employed for solving robot PP problems. Additionally, we included the conventional probabilistic roadmap method (PRM) in our evaluation. We evaluated the planning performances of these algorithms on a standardized set of benchmark problems. Our findings indicate that the TRCHOA outperforms the other algorithms in terms of its planning performance. The evaluation of planning effectiveness encompasses several key criteria, namely path length, consistency of scheduled paths, time complexity, and rate of success. The experiments conducted in this study provide evidence of the statistically significant value of the enhancements obtained through the implementation of the proposed method. The findings derived from the TRCHOA provide compelling evidence of its capacity to accurately determine the most optimal route within the specified test map.
Accurately predicting accounting profit (PAP) plays a vital role in financial analysis and decision-making for businesses. The analysis of a business's financial achievements offers significant insights and aids i...
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Accurately predicting accounting profit (PAP) plays a vital role in financial analysis and decision-making for businesses. The analysis of a business's financial achievements offers significant insights and aids in the formulation of strategic plans. This research paper focuses on improving the chimp optimization algorithm (CHOA) to evolve deep long short-term memory (LSTM) models specifically for financial accounting profit prediction. The proposed hybrid approach combines CHOA's global search capabilities with deep LSTMs' sequential modeling abilities, considering both the global and temporal aspects of financial data to enhance prediction accuracy. To overcome CHOA's tendency to get stuck in local minima, a novel updating technique called adaptive pair reinforced (APR) is introduced, resulting in APRCHOA. In addition to well-known conventional prediction models, this study develops five deep LSTM-based models, namely conventional deep LSTM, CHOA (deep LSTM-CHOA), adaptive reinforcement-based genetic algorithm (deep LSTM-ARGA), marine predator algorithm (deep LSTM-MPA), and adaptive reinforced whale optimizationalgorithm (deep LSTM-ARWOA). To comprehensively evaluate their effectiveness, the developed deep LSTM-APRCHOA models are assessed using statistical error metrics, namely root mean square error (RMSE), bias, and Nash-Sutcliffe efficiency (NSEF). In the validation set, at a lead time of 1 h, the NSEF values for LSTM, LSTM-MPA, LSTM-CHOA, LSTM-ARGA, LSTM-ARWOA, and deep LSTM-APRCHOA were 0.9100, 0.9312, 0.9350, 0.9650, 0.9722, and 0.9801, respectively. The results indicate that among these models, deep LSTM-APRCHOA demonstrates the highest accuracy for financial profit prediction.
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