Hybrid Whale Optimization Algorithm Research Articles

Parameter Identification of the RBF-ARX Model Based on the Hybrid Whale Optimization Algorithm

Parameter Identification of the RBF-ARX Model Based on the Hybrid Whale Optimization Algorithm

A hybrid whale optimization algorithm for distributed no-wait flow-shop scheduling problem with batch delivery

Enterprises have increasingly focused on integrated production and transportation problems, recognizing their potential to enhance cohesion across different decision-making levels. The whale optimization algorithm, with its advantages such as minimal parameter control, has garnered attention. In this study, a hybrid whale optimization algorithm (HWOA) is designed to settle the distributed no-wait flow-shop scheduling problem with batch delivery (DNWFSP-BD). Two objectives are considered concurrently, namely, the minimization of the makespan and total energy consumption. In the proposed algorithm, four vectors are proposed to represent a solution, encompassing job scheduling, factory assignment, batch delivery and speed levels. Subsequently, to generate high-quality candidate solutions, a heuristic leveraging the Largest Processing Time (LPT) rule and the NEH heuristic is introduced. Moreover, a novel path-relinking strategy is proposed for a more meticulous search of the optimal solution neighborhood. Furthermore, an insert-reversed block operator and variable neighborhood descent (VND) are introduced to prevent candidate solutions from converging to local optima. Finally, through comprehensive comparisons with efficient algorithms, the superior performance of the HWOA algorithm in solving the DNWFSP-BD is conclusively demonstrated.

Techno-economic investigation of grid integrated renewable energy resources with hydrogen storage systems

Fundamentally, a system is inherently connected to the presence of uncertainty, which consequently leads to the development of uncertain design and scheduling. In the contemporary power system, the prevailing conditions can primarily be attributed to the uncertain efficiency of several variables, such as pricing. Consequently, the utilization of uncertainty modeling turns into imperative. This study utilizes an optimization technique that employs a hybrid whale optimization algorithm and pattern search (HWOA-PS) to achieve the optimum efficiency in the smart parking of electric vehicles under uncertain conditions arising from fluctuations in the pricing of the main grid within the demand response program (DRP). The proposed method can effectively reduce daily costs by shifting the load between peak and light-load conditions. The suggested scheme has several features such as a non-dominated arrangement model, variable discovery, memory-based approach assortment and fuzzy theory to select the greatest Pareto. In addition to all the advantages mentioned above, the proposed algorithm has a high response speed in achieving the final and high possibility to reach the global point. There exist several important limitations for Hydrogen Storage Systems (HSS) that needs to be considered in modeling. The most important limitations include the restrictions of the electrolyzer and the boundaries of Fuel cell (FC) as well as a storage tank. The performance of the suggested algorithm is confirmed in a system with parking and several resources under uncertainty. The results confirm that the proposed method has a great ability to cope with uncertainty. Hence, there has been a reduction of as much as 41 % in the expense of SPL variation. Conversely, by taking into account the DPR, the mean expense of the SPL increases by 4.92 %, resulting in a corresponding decrease of 47.01 % in the variation of SPL costs. When evaluating the impact of DPR, it is seen that an increase in the mean expense of SPL leads to a decrease in its overall magnitude.

IWO-IGA—A Hybrid Whale Optimization Algorithm Featuring Improved Genetic Characteristics for Mapping Real-Time Applications onto 2D Network on Chip

Network on Chip (NoC) has emerged as a potential substitute for the communication model in modern computer systems with extensive integration. Among the numerous design challenges, application mapping on the NoC system poses one of the most complex and demanding optimization problems. In this research, we propose a hybrid improved whale optimization algorithm with enhanced genetic properties (IWOA-IGA) to optimally map real-time applications onto the 2D NoC Platform. The IWOA-IGA is a novel approach combining an improved whale optimization algorithm with the ability of a refined genetic algorithm to optimally map application tasks. A comprehensive comparison is performed between the proposed method and other state-of-the-art algorithms through rigorous analysis. The evaluation consists of real-time applications, benchmarks, and a collection of arbitrarily scaled and procedurally generated large-task graphs. The proposed IWOA-IGA indicates an average improvement in power reduction, improved energy consumption, and latency over state-of-the-art algorithms. Performance based on the Convergence Factor, which assesses the algorithm’s efficiency in achieving better convergence after running for a specific number of iterations over other efficiently developed techniques, is introduced in this research work. These results demonstrate the algorithm’s superior convergence performance when applied to real-world and synthetic task graphs. Our research findings spotlight the superior performance of hybrid improved whale optimization integrated with enhanced GA features, emphasizing its potential for application mapping in NoC-based systems.

A Sequential-Multi-Decision Scheme for WiFi Localization Using Vision-Based Refinement

Currently, most mobile devices have WIFI and camera modules to locate their position. However, there are two main challenges in large, highly similar indoor environments (localization accuracy and localization time). Aiming to balance these problems, we propose a sequential-multi-decision integrated system that combines WIFI and vision to acquire users' locations. This system has two phases: sequential fusion localization and adaptive multi-decision fusion localization. The former employs WIFI-based localization first, then image-based localization and fusion localization are used within the constraints of WIFI-based localization. In the WIFI-based localization phase, the gaussian process regression (GPR) model is used to construct a WIFI indoor map. Subsequently, we propose to apply the hybrid whale optimization algorithm (HWOA) to WIFI-based localization to improve its accuracy and stability. The latter uses an adaptive multi-decision fusion mechanism that integrates WIFI-based localization, image-based localization, and fusion localization to obtain the users' location finally. The experiments show the effectiveness of HWOA applied to WIFI-based localization. We also experimentally evaluate the proposed fusion algorithm with other state-of-the-art fusion algorithms (e.g., accuracy and time) in a real environment (an area larger than 10,000 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$m^{2}$</tex-math></inline-formula> ). The experimental results show that the proposed fusion system is competitive.

A Novel Hybrid Whale Optimization Algorithm Differential Evolution Algorithm-Based Maximum Power Point Tracking Employed Wind Energy Conversion Systems for Water Pumping Applications: Practical Realization

This article describes the implementation of a proposed hybrid whale optimization algorithm differential evolution (WOADE) as a maximum power point tracking (MPPT) for wind energy conversion systems (WECS) employed for water pumping applications. The proposed hybrid MPPT algorithm provides rapid and zero oscillation power tracking to achieve the global maximum power point (GMPP) within a limited number of iterations. In this work, the exploitation capability of the whale optimization algorithm (WOA) is integrated with the exploration behavior of differential evolution (DE) to improve the tracking ability over a steady and dynamic operating climate. The performance of a permanent magnet synchronous generator (PMSG) driven centrifugal pump for water pumping applications with wind energy conversion systems has also been investigated. A modified Ćuk converter provides a higher output voltage with an enhanced voltage conversion ratio and is employed for optimal wind power tracking. The practical realization is done to substantiate the productiveness of the introduced hybrid WOADE based MPPT algorithm for WECS applied for water pumping under varying and sudden changes in wind velocity.

Hybrid whale optimization algorithm for enhanced routing of limited capacity vehicles in supply chain management

The present study focuses on the problem of vehicle routing with limited capacity, with the objective of minimizing the transportation distance required to serve h clients with predetermined locations and needs. The aim is to create k trips that cover the shortest possible distance. To achieve this goal, a hybrid whale optimization algorithm (hGWOA) is proposed, which combines the whale optimization algorithm (WOA) with the grey wolf optimizer (GWO). The proposed hybrid model is comprised of two main steps. First step, the GWO’s hunting mechanism is integrated transitioning to the utilization phase of WOA, and a newly devised state is introduced that is linked to GWO. In the second step, a novel technique is incorporated into the exploration mission phase to enhance the resolve after per iteration. The algorithm’s performance is assessed and compared with other modern algorithms, including the GWO, WOA, ant lion optimizer (ALO), and dragonfly algorithm (DA) using 23 benchmark test functions and CEC2017 benchmark test function. The results indicate that the hybrid hGWOA method outperforms other algorithms in terms of delivery distance optimization for scenarios involving scale and complexity. These findings are corroborated through case studies related to cement delivery and a real-world scenario in Viet Nam.

A Hybrid Whale Optimization Algorithm for Quality of Service-Aware Manufacturing Cloud Service Composition

Cloud Manufacturing (CMfg) has attracted lots of attention from scholars and practitioners. The purpose of quality of service (QoS)-aware manufacturing cloud service composition (MCSC), as one of the key issues in CMfg, is to combine different available manufacturing cloud services (MCSs) to generate an optimized MCSC that can meet the diverse requirements of customers. However, many available MCSs, deployed in the CMfg platform, have the same function but different QoS attributes. It is a great challenge to achieve optimal MCSC with a high QoS. In order to obtain better optimization results efficiently for the QoS-MCSC problems, a whale optimization algorithm (WOA) with adaptive weight, Lévy flight, and adaptive crossover strategies (ASWOA) is proposed. In the proposed ASWOA, adaptive crossover inspired by the genetic algorithm is developed to balance exploration and exploitation. The Lévy flight is designed to expand the search space of the WOA and accelerate the convergence of the WOA with adaptive crossover. The adaptive weight is developed to extend the search scale of the exploitation. Simulation and comparison experiments are conducted on various benchmark functions and different scale QoS-MCSC problems. The QoS attributes of the problems are randomly and symmetrically generated. The experimental results demonstrate that the proposed ASWOA outperforms other compared cutting-edge algorithms.

Short-Term Photovoltaic Power Generation Prediction Model Based on Improved Data Decomposition and Time Convolution Network

In response to the volatility of photovoltaic power generation, this paper proposes a short-term photovoltaic power generation prediction model (HWOA-MVMD-TPA-TCN) based on a Hybrid Whale Optimization Algorithm (HWOA), multivariate variational mode decomposition (MVMD), temporal pattern attention mechanism (TPA), and temporal convolutional network (TCN). In order to improve the accuracy of photovoltaic power generation forecasting, HWOA-MVMD is used for data decomposition, the Minimum Mode Overlap Component (MMOC) is used as the objective function, the photovoltaic power generation sequence is decomposed into finite Intrinsic Mode Functions (IMFs) according to the optimal solution, and the training set is formed with key meteorological variable data such as total radiation (unit: W/m2), ambient temperature, and humidity. Then, the TPA-TCN model is used to train the sub-sequences, the final predicted values are obtained after superimposing the reconstruction of the prediction results, and finally the prediction error of the photovoltaic power generation data is studied. The proposed method is applied to real photovoltaic power generation data from a commercial center in Tianjin and is compared with HWOA-MVMD-BiLSTM, GWO-MVMD-TPA-TCN, and TPA-TCN prediction models. The simulation results demonstrate that the MAE value of the forecast method proposed in this paper is 1.95 MW and the RMSE value is 2.55 MW, which can be reduced by up to 33.74% and 38.85%, respectively. The HWOA-MVMD-TPA-TCN-based short-term photovoltaic power generation prediction model presented in this paper achieves higher prediction accuracy and superior performance, serving as a valuable reference for related research.

An improved hybrid whale optimization algorithm for global optimization and engineering design problems

The whale optimization algorithm (WOA) is a widely used metaheuristic optimization approach with applications in various scientific and industrial domains. However, WOA has a limitation of relying solely on the best solution to guide the population in subsequent iterations, overlooking the valuable information embedded in other candidate solutions. To address this limitation, we propose a novel and improved variant called Pbest-guided differential WOA (PDWOA). PDWOA combines the strengths of WOA, particle swarm optimizer (PSO), and differential evolution (DE) algorithms to overcome these shortcomings. In this study, we conduct a comprehensive evaluation of the proposed PDWOA algorithm on both benchmark and real-world optimization problems. The benchmark tests comprise 30-dimensional functions from CEC 2014 Test Functions, while the real-world problems include pressure vessel optimal design, tension/compression spring optimal design, and welded beam optimal design. We present the simulation results, including the outcomes of non-parametric statistical tests including the Wilcoxon signed-rank test and the Friedman test, which validate the performance improvements achieved by PDWOA over other algorithms. The results of our evaluation demonstrate the superiority of PDWOA compared to recent methods, including the original WOA. These findings provide valuable insights into the effectiveness of the proposed hybrid WOA algorithm. Furthermore, we offer recommendations for future research to further enhance its performance and open new avenues for exploration in the field of optimization algorithms. The MATLAB Codes of FISA are publicly available at https://github.com/ebrahimakbary/PDWOA.

Reliability optimization in IRS-assisted UAV networks

Unmanned aerial vehicles (UAVs) and intelligent reflecting surfaces (IRSs) are regarded as critical technologies which need to be developed for the sixth-generation mobile network (6G). In this paper, we study the reliability of the IRS-assisted Unmanned aerial vehicles (UAV) communication system with limited UAV energy in complex urban scenarios. We present the problem by joint optimization of the IRS's scheduling, the UAV's trajectory, the IRS's phase shift, and the UAV's transmit power for maximizing the reliability of the transmission link under the constraints of UAV energy and channel error rate. The concavity of the problem is difficult to judge, and the problem is hard to find a substitution function to convert it into a tractable form. Therefore, we design a chaotic adaptative hybrid whale optimization algorithm (CAHWOA) to address the problem. CAHWOA is implemented by using alternately the chaotic adaptation whale optimization algorithm (CAWOA) and the variational perturbation binary whale optimization algorithm (VPBWOA). CAWOA and VPBWOA are used for solving the continuous variable subproblem and the binary variable subproblem, respectively. The numerical results show that the joint optimization of IRS and UAV improves the transmission link reliability by nearly 30% compared with the two baseline schemes. CAHWOA can improve the convergence rate by about 18% compared with the baseline algorithms. Meanwhile, it can enhance the optimization-seeking accuracy by about 0.04.

Hybrid whale optimization algorithm with simulated annealing for load frequency controller design of hybrid power system

Hybrid whale optimization algorithm with simulated annealing for load frequency controller design of hybrid power system

Hybrid whale optimization algorithm‐scaled cascade fractional order hybrid controller applied to renewable based Electric Vehicle generating systems

AbstractIn the upcoming epochs, conventional energy may deplete soon. Thus, the use of conventional energy in the power industries need to be supplemented by non‐conventional energy resources. This would result in loss of synchronisms in the power grids owing to the fact that solar and wind alternate their attributes expeditiously with change in atmospheric phenomenon. To ameliorate frequency deviation within a specific range automatic generation control (AGC) implements forced allowance on system operation. A three area thermal with photovoltaic (PV), electric vehicle (EV), wind system is considered under deregulated environment to develop and to judge the efficacy of newly developed cascade fractional order hybrid controller combination of (FOTID &amp; 3DOF‐PID). Comparing the aforementioned controller to other controllers such as the three degree of freedom proportional‐integral‐derivative (3DOF‐PID), the fractional order tilt‐integral‐derivative (FOTID) and the proportional‐integral‐derivative (PID) justifies the system's effectiveness. This assessment has been accomplished by a trendy optimization technique such as hybrid whale optimization algorithm (HWOA). However, the main intent of this write‐up is to fabricate a cascade fractional order (CC‐FO) hybrid controller that would act as the new control mechanism for the proposed system under deregulated scenario. It has been found that the suggested CC‐FO hybrid controller stabilizes the system (i.e., Under step load disruptions, frequency deviation and tie‐line power become zero) in the shortest amount of time possible. Additionally, it is seen that the recommended controller can control a wide range of nominal loading circumstances and system characteristics, demonstrating its robustness.

A Systematic Review of the Whale Optimization Algorithm: Theoretical Foundation, Improvements, and Hybridizations.

Despite the simplicity of the whale optimization algorithm (WOA) and its success in solving some optimization problems, it faces many issues. Thus, WOA has attracted scholars' attention, and researchers frequently prefer to employ and improve it to address real-world application optimization problems. As a result,many WOA variations have been developed, usually using two main approaches improvement and hybridization. However, no comprehensive study critically reviews and analyzes WOA and its variantsto find effective techniques and algorithmsand develop more successful variants. Therefore, in this paper, first, the WOA is critically analyzed, then the last 5 years' developments of WOA are systematically reviewed. To do this, a new adapted PRISMA methodology is introduced to select eligible papers, including three main stages: identification, evaluation, and reporting. The evaluation stage was improved using three screening steps and strict inclusion criteria to select a reasonable number of eligible papers. Ultimately, 59 improved WOA and 57 hybrid WOA variants published by reputable publishers, including Springer, Elsevier, and IEEE, were selected as eligible papers. Effective techniques for improving and successful algorithms for hybridizing eligible WOA variants are described. The eligible WOA are reviewed in continuous, binary, single-objective, and multi/many-objective categories. The distribution of eligible WOA variants regarding their publisher, journal, application, and authors' country was visualized. It is also concluded that most papers in this area lack a comprehensive comparison with previous WOA variants and are usually compared only with other algorithms. Finally, some future directions are suggested.

MCSC-Net: COVID-19 detection using deep-Q-neural network classification with RFNN-based hybrid whale optimization.

COVID-19 is the most dangerous virus, and its accurate diagnosis saves lives and slows its spread. However, COVID-19 diagnosis takes time and requires trained professionals. Therefore, developing a deep learning (DL) model on low-radiated imaging modalities like chest X-rays (CXRs) is needed. The existing DL models failed to diagnose COVID-19 and other lung diseases accurately. This study implements a multi-class CXR segmentation and classification network (MCSC-Net) to detect COVID-19 using CXR images. Initially, a hybrid median bilateral filter (HMBF) is applied to CXR images to reduce image noise and enhance the COVID-19 infected regions. Then, a skip connection-based residual network-50 (SC-ResNet50) is used to segment (localize) COVID-19 regions. The features from CXRs are further extracted using a robust feature neural network (RFNN). Since the initial features contain joint COVID-19, normal, pneumonia bacterial, and viral properties, the conventional methods fail to separate the class of each disease-based feature. To extract the distinct features of each class, RFNN includes a disease-specific feature separate attention mechanism (DSFSAM). Furthermore, the hunting nature of the Hybrid whale optimization algorithm (HWOA) is used to select the best features in each class. Finally, the deep-Q-neural network (DQNN) classifies CXRs into multiple disease classes. The proposed MCSC-Net shows the enhanced accuracy of 99.09% for 2-class, 99.16% for 3-class, and 99.25% for 4-class classification of CXR images compared to other state-of-art approaches. The proposed MCSC-Net enables to conduct multi-class segmentation and classification tasks applying to CXR images with high accuracy. Thus, together with gold-standard clinical and laboratory tests, this new method is promising to be used in future clinical practice to evaluate patients.

A Novel Brownian Motion-based Hybrid Whale Optimization Algorithm

&lt;p&gt;The Whale Optimization Algorithm (WOA) has the characteristics of simple implementation and few adjustment parameters, which is remarkable in the optimization algorithm. However, there are shortcomings such as premature convergence, slow convergence in the later period, and low search accuracy. For these shortcomings, a novel Brownian motion-based hybrid whale optimization algorithm (HWOA) is proposed. The search strategy in the Harris hawk optimization algorithm (HHO) is adopted to improve the global search ability of the algorithm, and a soft besiege with progressive rapid dives is introduced to solve the problems of premature convergence and slow convergence. Besides, the Brownian motion model is used to replace WOA. The random parameters in the distance formula are calculated to better simulate the prey&amp;rsquo;s escape during the predation process, and help to jump out of the local optimum. The simulation of 23 benchmark functions shows that compared with the classic and HWOA and metaheuristic, the convergence accuracy and speed have been improved, and the local optimum can be effectively jumped out. At the same time, 10 CEC06-2019 test functions are used to test and analyze it. Compared with WOA, HWOA has better search results, which verifies the superiority of the improved algorithm.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;

Application of improved hybrid whale optimization algorithm to optimization problems

Application of improved hybrid whale optimization algorithm to optimization problems

Stacking sequence optimization of arbitrary quadrilateral laminated plates for maximum fundamental frequency by hybrid whale optimization algorithm

A Hybrid Whale Optimization Algorithm (HWOA) is proposed to optimize the stacking sequence of arbitrary quadrilateral composite plates. By introducing an adversarial learning strategy, a broader search is performed on the initial domain to generate a better initial population. The mutation operator and opposition-based learning (OBL) strategy of the differential evolution (DE) algorithm are combined to maintain the diversity of the evolutionary process. In addition, the nonlinear convergence factor is introduced to improve the global exploration ability of HWOA when dealing with discrete problems. The stacking sequences of quadrilateral composite plates with different boundary conditions and geometries are optimized to maximize the fundamental frequency. The optimized results of HWOA are compared with those of whale optimization algorithm (WOA), Aquila optimizer (AO), artificial bee colony (ABC) algorithm, DE algorithm and layerwise optimization approach (LOA) algorithm. The results show that HWOA with multiple strategy combinations has good global search capability and robustness, and thus HWOA is a good candidate optimization algorithm for composite plate optimization.

Energy management and operational planning of renewable energy resources-based microgrid with energy saving

• Proposing an environmental/economic model for optimal energy management. • Considering renewable energy resources in the presence of Photovoltaic and battery. • Presenting Hybrid Whale and Pattern Search Algorithm for optimizing Micro-grid. • Reporting superior solutions in Short-Term Scheduling and Micro-grid Energy Management. Energy resource scheduling is one of the major problems in power systems. This issue has become even more significant as renewable sources with intermittent power output are becoming more and more prevalent. Due to their low environmental impact and low operating costs, renewable energy sources (RESs) have attracted interest. The excess power generated by such generation methods may be a negative that affects power systems, hence issues relating to such systems should be properly handled. The optimal operation of a microgrid (MG) with several distributed generation (DG) units and uncertain behavior of RESs is suggested in this research using a stochastic optimization approach. So, for an MG fitted with a solar photovoltaic (PV) unit, this research proposes a day-ahead scheduling paradigm. In this regard, the effects of various climatic circumstances on the power production of the PV unit and the optimal scheduling of the MG have been examined in this research. In order to achieve this, statistics on solar irradiance were extracted from four distinct days from each of the four seasons. The single-objective optimization framework used to design the scheduling problem states that the objective function should be to minimize the total operating cost across the scheduling period. The aforementioned day-ahead scheduling problem can be solved by the "hybrid whale optimization algorithm and pattern search (HWOA-PS)” optimization algorithm, while both renewable and nonrenewable generating units, as well as an energy storage system are present. In order to confirm the higher performance of the recommended approach, a thorough comparison between the Hybrid WOA-PS algorithm and a few well-known optimization algorithms has also been conducted.

A hybrid whale optimization algorithm based on equilibrium concept

This research paper proposes a hybrid Whale Optimization Algorithm (WOA) variant based on Equilibrium Optimizer (EO), named Equilibrium Whale Optimization Algorithm (EWOA). The major finding lies in an efficient hybridization of bio-inspired (WOA) and physics-based (EO) metaheuristic algorithms. Upon mathematical modelling, EWOA proposes a main architecture that combines WOA's encircling and net-bubble attacking mechanisms via EO's weight balance strategy. The proposed algorithm was tested on 23 classical, 28 constrained CEC 2017, 30 unconstrained CEC 2017, 10 CEC 2019, and 10 CEC 2020 benchmark problems, in comparison with six recently proposed state-of-the-art algorithms (including WOA and EO). EWOA outperforms other algorithms with the best statistical mean performance on 46 out of 101 functions and the most promising clustering data in the graph, respectively. The fact that EWOA could achieve best statistical SD performance on 2 of the total 5 benchmark sets proves that EWOA is competitively robust. EWOA can converge to the optimum before 50% iterations of most benchmark functions, achieving the fastest convergence rate compared to other algorithms. The major contribution thereby lies in the successful development of this hybrid algorithm, which yields better optimization efficiency than the original and other state-of-the-art algorithms in terms of statistics, convergence and clustering.