Proposed Whale Optimization Algorithm Research Articles

Non-speech emotion recognition based on back propagation feed forward networks

Non-speech emotion recognition involves identifying emotions conveyed through non-verbal vocalizations such as laughter, crying, and other sound signals, which play a crucial role in emotional expression and transmission. This paper employs a nine-category discrete emotion model encompassing happy, sad, angry, peaceful, fearful, loving, hateful, brave, and neutral. A proprietary non-speech dataset comprising 2337 instances was utilized, with 384-dimensional feature vectors extracted. The traditional Backpropagation Neural Network (BPNN) algorithm achieved a recognition rate of 87.7% on the non-speech dataset. In contrast, the proposed Whale Optimization Algorithm - Backpropagation Neural Network (WOA-BPNN) algorithm, applied to a self-made non-speech dataset, demonstrated a remarkable accuracy of 98.6%. Notably, even without facial emotional cues, non-speech sounds effectively convey dynamic information, and the proposed algorithm excels in their recognition. The study underscores the importance of non-speech emotional signals in communication, especially with the continuous advancement of artificial intelligence technology. The abstract thus encapsulates the paper’s focus on leveraging AI algorithms for high-precision non-speech emotion recognition.

Whale optimization algorithm and internet of things for horizontal axis solar tracker-basedload optimization

<p>Renewable solar energy is the future of all other resources because of its reliability and availability all over the earth. Optimization of the energy consumption and utilization of internet of things (IoT) devices deployed in such systems poses significant challenges. Axis tracker panel is the scope for the next decade toincrease the performance of the existing panels. This research focuses on the development of intelligent energy optimization algorithms for IoT devices. The integration of renewable energy sources and IoT devices in solar-microgrid energy systems offers promising solutions for sustainable and efficient energy management. The proposed whale optimization algorithm (WOA) takes into account dynamic factors, including varying energy availability and fluctuating demand patterns, to optimize the overall performance. Leveraging real-time data from IoT sensors and smart meters, the algorithms balance energy generation and consumption, prioritize critical loads, and incorporate energy forecasting techniques to handle fluctuations in renewable energy production. Moreover, they integrate demand response mechanisms and dynamic pricing models to encourage flexible energy consumption patterns and minimize operational costs. The results of this study demonstrate the significant potential of the WOA algorithm in enhancing the sustainability of microgrid energy systems, paving the way for a greener and more reliable energy future.</p>

Multi-Objective Evolutionary Algorithm With Machine Learning and Local Search for an Energy-Efficient Disassembly Line Balancing Problem in Remanufacturing

Abstract Product disassembly is a vital element of recycling and remanufacturing processes. The disassembly line balancing problem (DLBP), i.e., how to assign a set of tasks to a disassembly workstation, is crucial for a product disassembly process. Based on the importance of energy efficiency in product disassembly and the trend toward green remanufacturing, this study proposes an optimization model for a multi-objective disassembly line balancing problem that aims to minimize the idle rate, smoothness, cost, and energy consumption during the disassembly operation. Due to the complex nature of the optimization problem, a discrete whale optimization algorithm is proposed in this study, which is developed as an extension of the whale optimization algorithm. To enable the algorithm to solve discrete optimization problems, we propose coding and decoding methods that combine the features of DLBP. First of all, the initial disassembly solution is obtained by using K-means clustering to speed up the exchange of individual information. After that, new methods for updating disassembly sequences are developed, in which a local search strategy is introduced to increase the accuracy of the algorithm. Finally, the algorithm is used to solve the disassembly problem of a worm reducer and the first 12 feasible task allocation options in the Pareto frontier are shown. A comparison with typically existing algorithms confirms the high performance of the proposed whale optimization algorithm, which has a good balance of solution quality and efficiency.

Reviewer reliability and XGboost whale optimized sentiment analysis for online product recommendation

Nowadays, people always use online promotions to know about best shops to buy the best products. This shopping experience and shopper’s opinion about the shop can be observed by the customer-experience shared on social media. A new customer when searching a shop needs information about manufacturing date (MRD) and manufacturing price (MRP), offers, quality, and suggestions which are only provided by the previous customer experience. Several approaches were used previously for predicting the product details, but no one approach provides accurate information. To overcome these issues, Reviewer Reliability and XGboost whale Optimized Sentiment Analysis for Online Product Recommendation is proposed in this manuscript.Initially, Amazon Product recommendation datathe data are preprocessed and given to XGboost Classifier that classifies the product recommendation result as, good, bad and average. Generally the XGboost Classifier does not reveal any adoption of optimization techniques for computing the optimal parameters for assuring accurate classification of product recommendation. Therefore in this work, proposed Whale optimization algorithm utilized to optimize the weight parameters of the XGboost. Then the proposed model is implemented in MATLAB. The proposed method attains 18.31%, 12.81%, 45.75%, 26.97% and 25.55% lower Mean Absolute error, 18.31%, 12.81%, 27.97%, 25.97%, and 25.55% higher Mean absolute percentage error and 15.31%, 10.33%, 25.86%, 22.86% and 15.22% lower Mean Square Error than the existing methods.

Application of an improved whale optimization algorithm in time-optimal trajectory planning for manipulators.

To address the issues of unstable, non-uniform and inefficient motion trajectories in traditional manipulator systems, this paper proposes an improved whale optimization algorithm for time-optimal trajectory planning. First, an inertia weight factor is introduced into the surrounding prey and bubble-net attack formulas of the whale optimization algorithm. The value is controlled using reinforcement learning techniques to enhance the global search capability of the algorithm. Additionally, the variable neighborhood search algorithm is incorporated to improve the local optimization capability. The proposed whale optimization algorithm is compared with several commonly used optimization algorithms, demonstrating its superior performance. Finally, the proposed whale optimization algorithm is employed for trajectory planning and is shown to be able to produce smooth and continuous manipulation trajectories and achieve higher work efficiency.

Intrusion detection in the IoT data streams using concept drift localization

<abstract><p>With the widespread application of smart devices, the security of internet of things (IoT) systems faces entirely new challenges. The IoT data stream operates in a non-stationary, dynamic environment, making it prone to concept drift. This paper focused on addressing the issue of concept drift in data streams, with a key emphasis on introducing an innovative drift detection method-ensemble multiple non-parametric concept localization detectors, abbreviated as EMNCD. EMNCD employs an ensemble of non-parametric statistical methods, including the Kolmogorov-Smirnov, Wilcoxon rank sum and Mann-Kendall tests. By comparing sample distributions within a sliding window, EMNCD accurately detects concept drift, achieving precise localization of drift points, and enhancing overall detection reliability. Experimental results demonstrated the superior performance of EMNCD compared to classical methods on artificial datasets. Simultaneously, to enhance the robustness of data stream processing, we presented an online anomaly detection method based on the isolation forest (iForest). Additionally, we proposedwhale optimization algorithm (WOA)-extreme gradient boosting (XGBoost), a drift adaptation model employing XGBoost as a base classifier. This model dynamically updates using drift points detected by EMNCD and fine-tunes parameters through the WOA. Real-world applications on the edge-industrial IoTset (IIoTset) intrusion dataset explore the impact of concept drift on intrusion detection, where IIoT is a subclass of IoT. In summary, this paper focused on EMNCD, introducing innovative approaches for drift detection, anomaly detection, and drift adaptation. The research provided practical and viable solutions to address concept drift in data streams, enhancing security in IoT systems.</p></abstract>

Multi-Objective Optimal Economic Dispatch of a Fuel Cell and Combined Heat and Power Based Renewable Integrated Grid Tied Micro-grid Using Whale Optimization Algorithm

Micro-grids are practical solution for combining distributed energy resources and combined heat and power units in order to satisfy the system power and heat demands. Nowadays, in order to integrate both renewable and non-renewable energy resources like photovoltaic, wind turbine, combined heat and power systems and fuel-cell unit; micro-grid seems to be a good idea. The aim of this paper is to obtain optimal scheduling of proposed generating units and to reduce the total operational cost and net emission of the system through economic/environmental power dispatch, while considering the impact of grid tied and autonomous mode of operation and satisfying the operational constraints. In this paper, a novel whale optimization algorithm is employed to solve this multi-objective problem. The obtained optimal results through this proposed whale optimization algorithm represents the efficiency, feasibility and capability of handling non-linear optimization problems in an efficient way compared to other optimization techniques. The proposed system is studied in a 24-h time horizon. The results obtained from this proposed technique are compared with other techniques which are recently employed.

Enhanced whale optimization algorithm for sizing of hybrid wind/photovoltaic/diesel with battery storage in Algeria desert

This paper focuses on the optimization and operation of the renewable energy power sources for electrification of isolated rural city in Algeria desert. For this purpose, a system composed by photovoltaic (PV), wind turbine (WT), diesel generator (DG), and battery bank (BB) as well as for storing the energy in the electrical form to meet the load. In the present paper we are interested in evolutionary algorithms for solving optimization problem of hybrid renewable energy system. A new meta-heuristic algorithm namely whale optimization algorithm (WOA) is used to solve optimization problem of cost of energy (COE) and total net present cost (TNPC) including reliability evaluation by using basic probabilistic concept in order to find Loss of Power Supply Probability (LPSP). The WOA mimics the social behavior of humpback whales. This algorithm is inspired by the bubble-net hunting strategy. Three recent algorithms, particle swarm optimization (PSO), grey wolf optimizer (GWO), and modified grey wolf optimizer (M-GWO) are also implemented in this work. For examining the accuracy, stability, and robustness of proposed optimization technique two case studies have been tested. The results of simulations and comparison with other methods exhibit high accuracy and validity of the proposed whale optimization algorithm to solve optimization problem of hybrid renewable energy system.

Analysis and design of PEM fuel cell/photovoltaic system to supply a traffic/light signals in Ouargla city based on field experience

The idea of powering street traffic lights using stand-alone renewable systems has attracted much attention, in recent years. Classical configuration of photovoltaic (PV) cells connected to a battery bank is the most commonly employed approach in this regard. However, the low sunlight intensity in a lot of places around the globe, as well as the short-stage batteries autonomy, make this kind of systems technically less adequate. In this paper, and in order to improve the efficacy of the PV-based classical traffic lights, a hybrid system composed of a PV panel, battery, and a small PEM fuel cell is proposed. The goal is to develop a more reliable, efficient, and clean power supply that able to meet the load demands of the traffic lights during all the year. Ouargla region in Algeria (Latitude of 31°95′ N, Longitude of 5°24′E, and Altitude of 0.141 km above Mean Sea Level) is taken as a case study to be investigated. The influence of stack temperature on PEMFC I–V characteristics are also given and analyzed, furthermore, the proposed Whale Optimization Algorithm WOA is used to solve the parameters identification problems of the PEMFC electrochemical model. Comprehensive experiment results and analyses indicate that the proposed algorithm has good performance considering a two temperature samples (26 °C and 39 °C), in such a way, the lowest Mean Absolute Error reaches 0.0589 V and 0.1044 V respectively. Finally, an optimal computer program to design the system components is established and the results show that the optimum PV area, hydrogen generator number, and battery capacity for powering regular light signals are obtained to be 0.72 m2, one electrolyser of 60 l/h, and one battery of 80 Ah, respectively.

An Evolutionary-Based Sentiment Analysis Approach for Enhancing Government Decisions during COVID-19 Pandemic: The Case of Jordan

The world has witnessed recently a global outbreak of coronavirus disease (COVID-19). This pandemic has affected many countries and has resulted in worldwide health concerns, thus governments are attempting to reduce its spread and impact on different aspects of life such as health, economics, education, and politics by making emergent decisions and policies (e.g., lockdown and social distancing). These new regulations influenced people’s daily life and cast significant burdens, concerns, and disparities on various population groups. Taking the wrong actions and enforcing bad decisions by some countries result in increasing the contagion rate and more catastrophic results. People start to post their opinions and feelings about their government’s decisions on different social media networks, and the data received through these platforms present a very useful source of information that affects how governments perceive and cope with the current the pandemic. Jordan was one of the top affected countries. In this paper, we proposed a decision support system based on the sentiment analysis mechanism by combining support vector machines with a whale optimization algorithm for automatically tuning the hyperparameters and performing feature weighting. The work is based on a hybrid evolutionary approach that aims to perform sentiment analysis combined with a decision support system to study people’s posts on Facebook to investigate their attitudes and feelings toward the government’s decisions during the pandemic. The government regulations were divided into two periods: the first and latter regulations. Studying public sentiments during these periods allows decision-makers in the government to sense people’s feelings, alert them in case of possible threats, and help in making proactive actions if needed to better handle the current pandemic situation. Five different versions were generated for each of the two collected datasets. The results demonstrate the superiority of the proposed Whale Optimization Algorithm & Support Vector Machines (WOA-SVM) against other metaheuristic algorithms and standard classification models as WOA-SVM has achieved 78.78% in terms of accuracy and 84.64% in term of f-measure, while other standard classification models such as NB, k-NN, J84, and SVM achieved an accuracy of 69.25%, 69.78%, 70.17%, and 69.29%, respectively, with 64.15%, 62.90%, 60.51%, and 59.09% F-measure. Moreover, when comparing our proposed WOA-SVM approach with other metaheuristic algorithms, which are GA-SVM, PSO-SVM, and MVO-SVM, WOA-SVM proved to outperform the other approaches with results of 78.78% in terms of accuracy and 84.64% in terms of F-measure. Further, we investigate and analyze the most relevant features and their effect to improve the decision support system of government decisions.

Software defect prediction based on enhanced metaheuristic feature selection optimization and a hybrid deep neural network

Software defect prediction aims to identify the potential defects of new software modules in advance by constructing an effective prediction model. However, the model performance is susceptible to irrelevant and redundant features. In addition, previous studies mainly use traditional data mining or machine learning techniques for defect prediction, the prediction performance is not superior enough. For the first issue, motivated by the idea of search based software engineering, we leverage the recently proposed whale optimization algorithm (WOA) and another complementary simulated annealing (SA) to construct an enhanced metaheuristic search based feature selection algorithm named EMWS, which can effectively select fewer but closely related representative features. For the second issue, we employ a hybrid deep neural network — convolutional neural network (CNN) and kernel extreme learning machine (KELM) to construct a unified defect prediction predictor called WSHCKE, which can further integrate the selected features into the abstract deep semantic features by CNN and boost the prediction performance by taking full advantage of the strong classification capacity of KELM. We conduct extensive experiments for feature selection or extraction and defect prediction across 20 widely-studied software projects on four evaluation indicators. Experimental results demonstrate the superiority of EMWS and WSHCKE.

Group-based whale optimization algorithm

Meta-heuristic algorithms are divided into two categories: biological and non-biological. Biological algorithms are divided into evolutionary and swarm-based intelligence, where the latter is divided into imitation based and sign based. The whale algorithm is a meta-heuristic biological swarm-based intelligence algorithm (based on imitation). This algorithm suffers from the early convergence problem which means the population convergences early to an unfavorable optimum point. Usually, the early convergence occurs because of the weakness in exploration capability (global search). In this study, an optimized version of the whale algorithm is proposed that introduces a new idea in grouping of whales (called GWOA) to overcome the early convergence problem. The proposed whale optimization algorithm is compared with the standard whale algorithm (WOA), CWOA improved whale algorithm, particle swarm optimization, and BAT algorithms applying CEC2017 functions. The results of the experiments show that the proposed method applying Friedman’s test on 30 standard benchmark functions has a better performance than the other baseline algorithms.

A novel dynamic framework to detect DDoS in SDN using metaheuristic clustering

AbstractSecurity is a crucial factor in the continuously evolving programmable networks. With the emergence of programmable networking terminals, the need to protect the networks has become mandatory. Software‐defined networks (SDNs) provide programmable switches, thereby isolating the data plane from the control plane. Many security algorithms have been proposed to protect the network; however, they have failed to protect SDNs from attacks such as distributed denial of service (DDoS), jamming, and man‐in‐the‐middle attacks. In this article, we only address the DDoS attack that prevails in SDNs. Isolation of the control plane from the data plane increases the probability of an attack on the data plane. Therefore, a framework that can handle the dynamic traffic and can protect the network from DDoS attacks is required. Our proposed whale optimization algorithm–based clustering for DDoS detection (WOA‐DD) avoids the DDoS attacks using a metaheuristic approach by clustering the attack requests. We evaluated this algorithm for robustness in comparison with several existing solutions and found it to be safe under several conditions. The proposed attack request clustering is explored to check its feasibility with various machine learning approaches and found to be stable with the prevailing mechanisms. Analysis of the algorithm under varied conditions reveals that WOA‐DD is robust, stable, and efficient against DDoS attacks.

Dynamic scheduling applying new population grouping of whales meta-heuristic in cloud computing

Scheduling in cloud computing is the assignment of tasks to resources with maximum performance, which is a multi-purpose problem. The scheduling is of NP-Hard issues that is the reason why meta-heuristic algorithms are used in scheduling problems. The meta-heuristic scheduling algorithms are divided into two categories of biological and non-biological. Swarm-based meta-heuristics are of biological algorithms that are based on imitation, or based on sign. The whale optimization algorithm is a meta-heuristic biological swarm-based intelligence algorithm based on imitation. This algorithm suffers from the early convergence problem which means the population convergences early to an unfavorable optimum point. Usually, the early convergence occurs because of the weakness in exploration capability (global search). In this study, an optimized version of the Whale optimization algorithm is introduced that presents a new idea in grouping whales called GWOA. It is firstly proposed to overcome the early convergence problem and then make a balance between the local and the global search in finding the optimal solution. The proposed method divides the sorted population into δ groups and a member of each group is randomly selected which is used in encircling prey section of the whale optimization algorithm. Then, the average best fitness was enhanced to improve both exploitation and exploration as well as premature convergence. In the next step, GWOA is used in a cloud computing scheduler at high workload to reduce the average execution time, response time, and increase the throughput in the cloud computing environment. The proposed whale optimization algorithm is compared with the standard whale optimization algorithm (WOA), improved whale optimization algorithm (CWOA), particle swarm optimization (PSO), and bat algorithms applying CEC2017 functions to compare the average parameter of the best fit, and then they are implemented as a cloud computing scheduler. The results of the experiments show that the proposed method has a better performance in comparison with competent meta-heuristic algorithms and scheduling algorithms.

Modeling and optimal design of LLC resonant converter using whale optimization algorithm

Resonant converter (RC) was brought under research in the 80’s widely, which can attain very small switching loss, therefore, facilitating resonant topologies to function at the high switching frequency. It is well addressed in the review that the optimal parameterization of the resonant converter is a crucial task. While the literature has come out with different methodologies, they are highly conceptual and so the uncertainty due to high theoretical impact persists. This paper intends to develop a Parameter Optimization (PO) algorithm for designing and developing of LLC-RC. The proposed algorithm overwhelms the limitation by introducing a nonconceptual model based on the simulated outcome. Specifically, the resonant current under start-up conditions is acquired from the literary outcome, and the intelligent model is constructed. Based on the proposed model, a renowned search algorithm called as Whale Optimization Algorithm (WOA) is exploited to optimize the time constant of the resonant converter, which is a critical design parameter. The objective model is derived as a function of start-up time and so the start-up time can be minimized. Moreover, the response speed of the output voltage is also increased. The proposed Whale Optimization Algorithm based Parameter Optimization (WOAPO) is compared with the conventional techniques such as IAPO, Ant Bee Colony-PO (ABC-PO), Particle Swarm Optimization- PO (PSOPO), FireFly PO (FFPO) and Grey Wolf Optimization (GWOPO). The obtained result verifies the performance of the proposed method in modeling LLC-RC system.

A parallel numerical method for solving optimal control problems based on whale optimization algorithm

Development of a numerical algorithm for solving optimal control problems is reported in this article. The method is a combination of multi-staging of the original problem to a finite dimensional optimization problem and the recently proposed Whale Optimization Algorithm (WOA). The method is proposed to reduce the required number of iterations. The parallel implementation is also proposed and discussed. Numerical examples are given to check the validity and accuracy of the proposed method. Results show that method converges to the exact solution with accuracy comparable to other numerical methods. Note that the source codes are publicly available at http://www.alimirjalili.com/SourceCodes/WOA4OCP.zip.

Optimizing connection weights in neural networks using the whale optimization algorithm

The learning process of artificial neural networks is considered as one of the most difficult challenges in machine learning and has attracted many researchers recently. The main difficulty of training a neural network is the nonlinear nature and the unknown best set of main controlling parameters (weights and biases). The main disadvantages of the conventional training algorithms are local optima stagnation and slow convergence speed. This makes stochastic optimization algorithm reliable alternative to alleviate these drawbacks. This work proposes a new training algorithm based on the recently proposed whale optimization algorithm (WOA). It has been proved that this algorithm is able to solve a wide range of optimization problems and outperform the current algorithms. This motivated our attempts to benchmark its performance in training feedforward neural networks. For the first time in the literature, a set of 20 datasets with different levels of difficulty are chosen to test the proposed WOA-based trainer. The results are verified by comparisons with back-propagation algorithm and six evolutionary techniques. The qualitative and quantitative results prove that the proposed trainer is able to outperform the current algorithms on the majority of datasets in terms of both local optima avoidance and convergence speed.