Search results

This paper aims to introduce a new target tracking method based on risk theory in a 2-D discrete environment. After that, the related sensor scheduling method is proposed. This can make up the blank of target tracking and sensor management in the 2-D discrete environment.

The definition of risk is proposed based on risk decision theory firstly. Then the target tracking model in a two-dimensional discrete environment is built. The motion state updating and estimation method of target’s motion state based on Bayes theory is given. Thirdly, the method of computing sensor emission interception risk is provided. Afterwards, the optimization rule of obtaining the minimum risk is followed to model the sensor scheduling objective function. The lion algorithm is adjusted and improved combined with Chaos theory to generate the optimal sensor management projects.

The risk-based sensor target tracking method and sensor management method are both effective in a 2-D discrete environment.

To the best of the authors’ knowledge, this paper is the first to study the target tracking method and sensor scheduling method in a 2-D environment. Furthermore, the lion algorithm is improved combined with Chaos theory to show a better optimization performance.

Generator maintenance scheduling (GMS) is an essential task for electric power utilities as the periodical maintenance activity enhances the lifetime and also ensures the reliable and continuous operation of generating units. Though numerous meta-heuristic algorithms have been reported for the GMS solution, enhancing the existing techniques or developing new optimization procedure is still an interesting research task. The meta-heuristic algorithms are population based and the selection of their algorithmic parameters influences the quality of the solution. This paper aims to propose statistical tests guided meta-heuristic algorithm for solving the GMS problems.

The intricacy characteristics of the GMS problem in power systems necessitate an efficient and robust optimization tool. Though several meta-heuristic algorithms have been applied to solve the chosen power system operational problem, tuning of their control parameters is a protracting process. To prevail over the previously mentioned drawback, the modern meta-heuristic algorithm, namely, ant lion optimizer (ALO), is chosen as the optimization tool for solving the GMS problem.

The meta-heuristic algorithms are population based and require proper selection of algorithmic parameters. In this work, the ANOVA (analysis of variance) tool is proposed for selecting the most feasible decisive parameters in algorithm domain, and the statistical tests-based validation of solution quality is described. The parametric and non-parametric statistical tests are also performed to validate the selection of ALO against the various competing algorithms. The numerical and statistical results confirm that ALO is a promising tool for solving the GMS problems.

As a first attempt, ALO is applied to solve the GMS problem. Moreover, the ANOVA-based parameter selection is proposed and the statistical tests such as Wilcoxon signed rank and one-way ANOVA are conducted to validate the applicability of the intended optimization tool. The contribution of the paper can be summarized in two folds: the ANOVA-based ALO for GMS applications and statistical tests-based performance evaluation of intended algorithm.

The ever-stringent environmental regulations force power producers to produce electricity at the cheapest price and with minimum pollutant emission levels. The electrical power generation from fossil fuel releases several contaminants into the air, and this becomes excrescent if the generating unit is fed by multiple fuel sources (MFSs). Inclusion of this issue in operational tasks is a welcome perspective. This paper aims to develop a multi-objective model comprising total fuel cost and pollutant emission.

The cost-effective and environmentally responsive power system operations in the presence of MFSs can be recognised as a multi-objective constrained optimisation problem with conflicting operational objectives. The complexity of the problem requires a suitable optimisation tool. Ant lion algorithm (ALA), the most recent nature-inspired algorithm, was used as the main optimisation tool because of its salient characteristics. The fuzzy decision-making mechanism has been integrated to determine the best compromised solution in the multi-objective framework.

This paper is the first to propose a more precise and practical operational model for studying a multi-fuel power dispatch scenario considering valve-point effects and CO₂ emission. The modern meta-heuristic algorithm ALA is applied for the first time to address the economic operation of thermal power systems with multiple fuel options.

Power companies aim to make profit by abiding by the norms of the regulatory board. To achieve economic benefits, the power system must be analysed using an accurate operational model. The proposed model integrates total fuel cost, valve-point loadings and CO₂ emission, which are prevailing power system operational objectives. The economic advantages of the operational model can be observed through economic deviation indices, and the performed analysis validates that the developed model corresponds to the actual power operation.

The realistic operational model is proposed by considering total fuel and pollutant emission, and the ALA is applied for the first time to address the proposed multi-objective problem. To validate the effectiveness of ALA, it is implemented in standard test systems with varying generating units (10-100) and the IEEE 30 bus system, and various kinds of power system operations are performed. Moreover, the comparison and performance analysis confirm that the current proposal is found enhanced in terms of solution quality.

In computer application scenario, data mining task is rarely utilized in power system, as an enhanced part, this work presented data mining task in power systems, to overcome frequency deviation issues. Load frequency control (LFC) is a primary challenging problem in an interconnected multi-area power system.

This paper adopts lion algorithm (LA) for the LFC of two area multi-source interconnected power systems. The LA calculates the optimal gains of the fractional order PI (FOPI) controller and hence the proposed LA-based FOPI controller (LFOPI) is developed.

For the performance analysis, the proposed algorithm compared with various algorithm is given as, 80.6% lesser than the FOPI algorithm, 2.5% lesser than the GWO algorithm, 2.5% lesser than the HSA algorithm, 4.7% lesser than the BBO algorithm, 1.6% lesser than PSO algorithm and 80.6% lesser than the GA algorithm.

The LFOPI controller is the proposed controlling method, which is nothing but the FOPI controller that gets the optimal gain using the LA. This method produces better performance in terms of converging behavior, optimization of controller gain, transient profile and steady-state response.

In today’s digital world, real-time health monitoring is becoming a most important challenge in the field of medical research. Body signals such as electrocardiogram (ECG), electromyogram and electroencephalogram (EEG) are produced in human body. This continuous monitoring generates huge count of data and thus an efficient method is required to shrink the size of the obtained large data. Compressed sensing (CS) is one of the techniques used to compress the data size. This technique is most used in certain applications, where the size of data is huge or the data acquisition process is too expensive to gather data from vast count of samples at Nyquist rate. This paper aims to propose Lion Mutated Crow search Algorithm (LM-CSA), to improve the performance of the LMCSA model.

A new CS algorithm is exploited in this paper, where the compression process undergoes three stages: designing of stable measurement matrix, signal compression and signal reconstruction. Here, the compression process falls under certain working principle, and is as follows: signal transformation, computation of Θ and normalization. As the main contribution, the theta value evaluation is proceeded by a new “Enhanced bi-orthogonal wavelet filter.” The enhancement is given under the scaling coefficients, where they are optimally tuned for processing the compression. However, the way of tuning seems to be the great crisis, and hence this work seeks the strategy of meta-heuristic algorithms. Moreover, a new hybrid algorithm is introduced that solves the above mentioned optimization inconsistency. The proposed algorithm is named as “Lion Mutated Crow search Algorithm (LM-CSA),” which is the hybridization of crow search algorithm (CSA) and lion algorithm (LA) to enhance the performance of the LM-CSA model.

Finally, the proposed LM-CSA model is compared over the traditional models in terms of certain error measures such as mean error percentage (MEP), symmetric mean absolute percentage error (SMAPE), mean absolute scaled error, mean absolute error (MAE), root mean square error, L1-norm and L2-normand infinity-norm. For ECG analysis, under bior 3.1, LM-CSA is 56.6, 62.5 and 81.5% better than bi-orthogonal wavelet in terms of MEP, SMAPE and MAE, respectively. Under bior 3.7 for ECG analysis, LM-CSA is 0.15% better than genetic algorithm (GA), 0.10% superior to particle search optimization (PSO), 0.22% superior to firefly (FF), 0.22% superior to CSA and 0.14% superior to LA, respectively, in terms of L1-norm. Further, for EEG analysis, LM-CSA is 86.9 and 91.2% better than the traditional bi-orthogonal wavelet under bior 3.1. Under bior 3.3, LM-CSA is 91.7 and 73.12% better than the bi-orthogonal wavelet in terms of MAE and MEP, respectively. Under bior 3.5 for EEG, L1-norm of LM-CSA is 0.64% superior to GA, 0.43% superior to PSO, 0.62% superior to FF, 0.84% superior to CSA and 0.60% better than LA, respectively.

This paper presents a novel CS framework using LM-CSA algorithm for EEG and ECG signal compression. To the best of the authors’ knowledge, this is the first work to use LM-CSA with enhanced bi-orthogonal wavelet filter for enhancing the CS capability as well reducing the errors.

In the past few decades, the wireless sensor network (WSN) has become the more vital one with the involvement of the conventional WSNs and wireless multimedia sensor networks (WMSNs). The network that is composed of low-power, small-size, low-cost sensors is said to be WSN. Here, the communication information is handled using the multiple hop and offers only a simple sensing data, such as humidity, temperature and so on, whereas WMSNs are referred as the distributed sensing networks that are composed of video cameras, which contain the sector sense area. These WMSNs can send, receive and process the video information data, which is more intensive and complicated by wrapping with wireless transceiver. The WSNs and the WMSNs are varied in terms of their characteristic of turnablity and directivity.

The main intention of this paper is to maximize the lifetime of network with reduced energy consumption by using an advanced optimization algorithm. The optimal transmission radius is achieved by optimizing the system parameter to transmit the sensor information to the consequent sensor nodes, which are contained within the range. For this optimal selection, this paper proposes a new modified lion algorithm (LA), the so-called cub pool-linked lion algorithm (CLA). The next contribution is on the optimal selection of cluster head (CH) by the proposed algorithm. Finally, the performance of proposed model is validated and compared over the other traditional methods in terms of network energy, convergence rate and alive nodes.

The proposed model's cost function relies in the range of 74–78. From the result, it is clear that at sixth iteration, the proposed model’s performance attains less cost function, that is, 11.14, 9.78, 7.26, 4.49 and 4.13% better than Genetic Algorithm (GA), Dragonfly Algorithm (DA), Particle Swarm Optimization (PSO), Glowworm Swarm Optimization (GSO) and Firefly (FF), correspondingly. The performance of the proposed model at eighth iteration is 14.15, 7.96, 4.36, 7.73, 7.38 and 3.39% superior to GA, DA, PSO, GSO, FF and LA, correspondingly with less convergence rate.

This paper presents a new optimization technique for increasing the network lifetime with reduced energy consumption. This is the first work that utilizes CLA for optimization problems.

Finding the optimal maintenance schedules is the primitive aim of preventive maintenance scheduling (PMS) problem dealing with the objectives of reliability, risk and cost. Most of the earlier works in the literature have focused on PMS with the objectives of leveling reserves/risk/cost independently. Nevertheless, very few publications in the current literature tackle the multi-objective PMS model with simultaneous optimization of reliability, and economic perspectives. Since, the PMS problem is highly nonlinear and complex in nature, an appropriate optimization technique is necessary to solve the problem in hand. The paper aims to discuss these issues.

The complexity of the PMS problem in power systems necessitates a simple and robust optimization tool. This paper employs the modern meta-heuristic algorithm, namely, Ant Lion Optimizer (ALO) to obtain the optimal maintenance schedules for the PMS problem. In order to extract best compromise solution in the multi-objective solution space (reliability, risk and cost), a fuzzy decision-making mechanism is incorporated with ALO (FDMALO) for solving PMS.

As a first attempt, the best feasible maintenance schedules are obtained for PMS problem using FDMALO in the multi-objective solution space. The statistical measures are computed for the test systems which are compared with various meta-heuristic algorithms. The applicability of the algorithm for PMS problem is validated through statistical t-test. The statistical comparison and the t-test results reveal the superiority of ALO in achieving improved solution quality. The numerical and statistical results are encouraging and indicate the viability of the proposed ALO technique.

As a maiden attempt, FDMALO is used to solve the multi-objective PMS problem. This paper fills the gap in the literature by solving the PMS problem in the multi-objective framework, with the improved quality of the statistical indices.

Now days, various techniques are used for controlling the plants. New ideas are evolving day by day to get better-quality control for various industrial processes to produce high-quality products. Currently, the focus of this research is being emphasized on application of nature-inspired algorithms in control systems. The purpose of this paper is to apply a nature-inspired algorithm called Ant Lion Optimizer (ALO) for the design of proportional-integrator-derivative (PID) controller for an automatic voltage regulator (AVR) system.

For the design of the PID controller, the ALO algorithm is considered as a designing tool for obtaining the optimal values of the controller parameter. All the simulations are carried out in Simulink/MATLAB environment. A comparative study is carried out with some modern nature-inspired algorithm to describe the advantages of this tuning method.

The proposed method has superiority value in transient and frequency domain analysis than the other published heuristic optimization algorithms. The presented approach has almost no variation in transient response when varying time constants of the system parameter, such as exciter, generator, amplifier and sensor from −50 per cent to +50 per cent. In addition, the close loop system is robust against any disturbances such as input–output disturbances and parametric uncertainty, as the sensitivity values are nearly equal to one.

The proposed method presents the design and performance analysis of proportional integral derivate (PID) controller for an AVR system using the recently proposed ALO.

The humans are gifted with the potential of recognizing others by their uniqueness, in addition with more other demographic characteristics such as ethnicity (or race), gender and age, respectively. Over the decades, a vast count of researchers had undergone in the field of psychological, biological and cognitive sciences to explore how the human brain characterizes, perceives and memorizes faces. Moreover, certain computational advancements have been developed to accomplish several insights into this issue.

This paper intends to propose a new race detection model using face shape features. The proposed model includes two key phases, namely. (a) feature extraction (b) detection. The feature extraction is the initial stage, where the face color and shape based features get mined. Specifically, maximally stable extremal regions (MSER) and speeded-up robust transform (SURF) are extracted under shape features and dense color feature are extracted as color feature. Since, the extracted features are huge in dimensions; they are alleviated under principle component analysis (PCA) approach, which is the strongest model for solving “curse of dimensionality”. Then, the dimensional reduced features are subjected to deep belief neural network (DBN), where the race gets detected. Further, to make the proposed framework more effective with respect to prediction, the weight of DBN is fine tuned with a new hybrid algorithm referred as lion mutated and updated dragon algorithm (LMUDA), which is the conceptual hybridization of lion algorithm (LA) and dragonfly algorithm (DA).

The performance of proposed work is compared over other state-of-the-art models in terms of accuracy and error performance. Moreover, LMUDA attains high accuracy at 100th iteration with 90% of training, which is 11.1, 8.8, 5.5 and 3.3% better than the performance when learning percentage (LP) = 50%, 60%, 70%, and 80%, respectively. More particularly, the performance of proposed DBN + LMUDA is 22.2, 12.5 and 33.3% better than the traditional classifiers DCNN, DBN and LDA, respectively.

This paper achieves the objective detecting the human races from the faces. Particularly, MSER feature and SURF features are extracted under shape features and dense color feature are extracted as color feature. As a novelty, to make the race detection more accurate, the weight of DBN is fine tuned with a new hybrid algorithm referred as LMUDA, which is the conceptual hybridization of LA and DA, respectively.

Multiple input multiple-output (MIMO) has emerged as one among the many noteworthy technologies in recent wireless applications because of its powerful ability to improve bandwidth efficiency and performance, i.e. through developing its unique spatial multiplexing capability and spatial diversity gain. For carrying out an enhanced communication in next-generation networks, the MIMO and orthogonal frequency division multiple systems were combined that facilitate the spatial multiplexing on resource blocks (RBs) based on time-frequency. This paper aims to propose a novel approach for maximizing the throughput of cell-edge users and cell-center users.

In this work, the specified multi-objective function is defined as the single objective function, which is solved by the introduction of a new improved algorithm as well. This optimization problem can be resolved by the fine-tuning of certain parameters such as assigned power for RB, cell-center user, cell-edge user and RB allocation. The fine-tuning of parameters is attained by a new improved Lion algorithm (LA), termed as Lion with new cub generation (LA-NCG) model. Finally, the betterment of the presented approach is validated over the existing models in terms of signal to interference plus noise ratio, throughput and so on.

On examining the outputs, the adopted LA-NCG model for 4BS was 66.67%, 66.67% and 20% superior to existing joint processing coordinated multiple point-based dual decomposition method (JC-DDM), fractional programming (FP) and LA models. In addition, the throughput of conventional JC-DDM, FP and LA models lie at a range of 10, 45 and 35, respectively, at the 100th iteration. However, the presented LA-NCG scheme accomplishes a higher throughput of 58. Similarly, the throughput of the adopted scheme observed for 8BS was 59.68%, 44.19% and 9.68% superior to existing JC-DDM, FP and LA models. Thus, the enhancement of the adopted LA-NCG model has been validated effectively from the attained outcomes.

This paper adopts the latest optimization algorithm called LA-NCG to establish a novel approach for maximizing the throughput of cell-edge users and cell-center users. This is the first that work uses LA-NCG-based optimization that assists in fine-tuning certain parameters such as assigned power for RB, cell-center user, cell-edge user and RB allocation.