Whale Optimization Algorithm

This paper presents an optimized K-nearest neighbors (KNNs) classification algorithm using the metaheuristic whale optimization to searches for sink node in wireless sensor networks. Sink node aggregate data from all sensor nodes and reducing the energy consumption network to prolong network lifetime. To reach aforementioned, a fitness function has formulated to choose the best location of sink node with high residual neighbor's sensor nodes energy to leads to maximizing the network lifetime. Eventually, the experimental results have been conducted whereas sensor nodes are propagated in a random location within the desired network area. The system has 11% improvement on the net-work's energy consumption that increases the lifetime of the network.

Hybrid metaheuristics are of the most interesting recent trends in optimization and memetic algorithms. In this paper, two hybridization models are used to design different feature selection techniques based on Whale Optimization Algorithm (WOA). In the first model, Simulated Annealing (SA) algorithm is embedded in WOA algorithm, while it is used to improve the best solution found after each iteration of WOA algorithm in the second model. The goal of using SA here is to enhance the exploitation by searching the most promising regions located by WOA algorithm. The performance of the proposed approaches is evaluated on 18 standard benchmark datasets from UCI repository and compared with three well-known wrapper feature selection methods in the literature. The experimental results confirm the efficiency of the proposed approaches in improving the classification accuracy compared to other wrapper-based algorithms , which insures the ability of WOA algorithm in searching the feature space and selecting the most informative attributes for classification tasks.

In this paper, Investigating SOAP and XML technologies in web service is studied. The reason for using XML technology to transmit data and also the need for application of existing communicative structure in SOAP technology in web pages with WSDL technology are investigated uniquely. And also the need for searchable address giving for web service which is available in UDDI technology and the advantages of using it are explained for programmers.

This paper work present one of the latest meta heuristic optimization approaches named whale optimization algorithm as a new algorithm developed to solve the economic dispatch problem. The execution of the utilized algorithm is analyzed using standard test system of IEEE 30 bus system. The proposed algorithm delivered optimum or near optimum solutions. Fuel cost and emission costs are considered together to get better result for economic dispatch. The analysis shows good convergence property for WOA and provides better results in comparison with PSO. The achieved results in this study using the above-mentioned algorithm have been compared with obtained results using other intelligent methods such as particle swarm Optimization. The overall performance of this algorithm collates with early proven optimization methodology, Particle Swarm Optimization (PSO). The minimum cost for the generation of units is obtained for the standard bus system.

This article estimates the unknown dc motor parameters by adapting the adaptive model with the reference model created by experimental data onto armature current and speed response from separately excited dc motor. The field flux dynamics, which is usually ignored, is included to model the dynamics of the motor. The block diagram including the flux dynamics and model parameters is considered as the adaptive model. The integral time square error between the instant experimental data and the corresponding adaptive model data is taken as cost function. The Whale optimization algorithm is used to minimize the cost function. Additionally, to improve the performances of optimization algorithm and for accurate result, the experimental data is divided into three intervals which form the three inequality constraints. A fixed penalty value is added to the cost function for violating these constraints. The effectiveness of estimation with two different methods is validated by convergence curve.

Utility grids face significant problems in the power sector in modern times because of the constant increase of loads and the lack of capital spending to install new transmission networks. As a consequence, the generating system is unable to fulfill the anticipated demand and finds it difficult to transport the generated power from central power generation to the distribution network. This can result in high power losses in the distribution system and also disrupt the system's overall performance. This problem can be addressed by the optimum positioning of Distributed Generation (DG) in power systems. From a technological and economic point of view, the DG has further advantages, such as minimizing system losses, improving the voltage profile and reducing line congestion. In this paper, the hybrid algorithm which includes Binary Particle Swarm Optimization (BPSO) and Whale Optimization Algorithm (WOA is used for proper positioning and sizing of DG in order to minimize actual and reactive power losses and increase the voltage profile of the distribution system, which in turn improves the system's voltage stability. This approach is simulated using Matlab software on the IEEE 33-bus systems, which demonstrates an increase in the voltage profile and reduces the power loss, when compared to other methods of optimization.

Fuzzy Logic System (FLS) is an efficient method to solve engineering problems. However, the training of a Fuzzy Logic System is a time-consuming task. Optimization Algorithm can be used to optimize the rule base of any FLS. Out of Type-1 FLS and Type-2 FLS, the type-2 found to be more effective to deal with noisy data. Due to their computational requirements Interval Type-2 (IT2) has been preferred over General Type-2. Whale Optimization Algorithm (WOA)has been introduced recently. The algorithm has been tested on different engineering problems and is found to be more effective. General Purpose Computing using graphics Processing Unit (GPGPU) is a new way to solve compute intensive problems on Graphics Processing Unit (GPU). CUDA-C is a parallel language that can be used to execute parallel code NVIDIA GPU. This paper integrates IT2 FLS, WOA and Processing Power of GPU. A toolbox is proposed that can be used to optimize the rule base in parallel. The toolbox provides both the implementations, i.e. serial and Parallel. FLS WOA Toolbox is design in such a way that user can pass parameter dynamically according to their need without interfacing with the code.

As the world is progressing forward, the load demand in the power system has been continuously increasing day by day. This situation has forced the power system to operate under stress condition due to its limitation. Therefore, due to the stressed condition, the transmission losses faced higher increment with a lower minimum voltage. Theoretically, the installation of the Flexible AC Transmission System (FACTS) device can solve the problem experienced by the power system. This paper presents the Whale Optimization Algorithm for loss minimization using FACTS devices in the transmission system. Thyristor controlled series compensator (TCSC) is chosen for this study. In this study, WOA is developed to identify the optimal sizing of FACTS device for loss minimization in the power system. IEEE 30-bus RTS was used as the test system to validate the effectiveness of the proposed algorithm.

Ontologies are used to model knowledge in several domains of interest, such as the biomedical domain. Conceptualization is the basic task for ontology building. Concepts are identified, and then they are linked through their semantic relationships. Recently, ontologies have constituted a crucial part of modern semantic webs because they can convert a web of documents into a web of things. Although ontology learning generally occupies a large space in computer science, Arabic ontology learning, in particular, is underdeveloped due to the Arabic language's nature as well as the profundity required in this domain. The previously published research on Arabic ontology learning from text falls into three categories: developing manually hand-crafted rules, using ordinary supervised/unsupervised machine learning algorithms, or a hybrid of these two approaches. The model proposed in this work contributes to Arabic ontology learning in two ways. First, a text mining algorithm is proposed for extracting concepts and their semantic relations from text documents. The algorithm calculates the concept frequency weights using the term frequency weights. Then, it calculates the weights of concept similarity using the information of the ontology structure, involving (1) the concept's path distance, (2) the concept's distribution layer, and (3) the mutual parent concept's distribution layer. Then, feature mapping is performed by assigning the concepts' similarities to the concept features. Second, a hybrid genetic-whale optimization algorithm was proposed to optimize ontology learning from Arabic text. The operator of the G-WOA is a hybrid operator integrating GA's mutation, crossover, and selection processes with the WOA's processes (encircling prey, attacking of bubble-net, and searching for prey) to fulfill the balance between both exploitation and exploration, and to find the solutions that exhibit the highest fitness. For evaluating the performance of the ontology learning approach, extensive comparisons are conducted using different Arabic corpora and bio-inspired optimization algorithms. Furthermore, two publicly available non-Arabic corpora are used to compare the efficiency of the proposed approach with those of other languages. The results reveal that the proposed genetic-whale optimization algorithm outperforms the other compared algorithms across all the Arabic corpora in terms of precision, recall, and F-score measures. Moreover, the proposed approach outperforms the state-of-the-art methods of ontology learning from Arabic and non-Arabic texts in terms of these three measures.

This paper presents a novel classification WOA-LLRBFNN (Whale optimization algorithm- Local Linear Radial Basis Function neural network) Model for classification of seizure EEG signal. The Bonn dataset has been applied for classification and detection of the seizure. The wavelet transform has been used for detection of seizure from the dataset. There are three wavelets such as Daubechies, coiflet and symlet are considered for detection and comparison results are presented. It is found that coiflet transform has shown good detection results in comparison to the other wavelets. Further, the proposed WOA based LLRBFNN accuracy results are compared with LLRBFNN, LLWNN (Local linear wavelet transform), RBFNN model to show the robustness of the model. It is found that the WOA-LLRBFNN model achieved an accuracy of 99.12% which is higher than the other models

Autism spectrum disorder (ASD) is a neurological illness, which affects around 1% of the global population. A commonly clinical approach to diagnose ASD is the standardized ASD test that results in more diagnostic time period with raised medical expenses. An automatic ASD diagnosis models enable to identity and classify the ASD at the earlier rate. The rise of machine learning (ML) models has begun to show promising results on the diagnosis of neuropsychiatric illness. Keeping this in mind, this paper presents a new Improved Whale Optimization Algorithm (IWOA) based Feature Selection (FS) with Fuzzy Rule Base Classifier (FRBC) model, called IWOA-FRBC for ASD diagnosis. The presented IWOA-FRBC model employs IWOA based FS process to proficiently pick up the required set of features from the medical data. In addition, particle swarm optimization (PSO) algorithm is integrated to the classical WOA to attain better exploration capability. Besides, FRBC model is applied to determine the different class labels of ASD from the feature reduced subset. The effectiveness of the IWOA-FRBC model takes place by the use of 3 benchmark ASD dataset namely children, adolescent, and adult. The attained simulation results portrayed the goodness of the IWOA-FRBC model by attaining the maximum accuracy of 93.49%, 95.45%, and 94.23% on ASD-Children, ASD-Adolescent, and ASD-Adult dataset respectively

Utility grids face significant problems in the power sector in modern times because of the constant increase of loads and the lack of capital spending to install new transmission networks. As a consequence, the generating system is unable to fulfill the anticipated demand and finds it dif icult to transport the generated power from central power generation to the distribution network. This can result in high power losses in the distribution system and also disrupt the system's overall performance. This problem can be addressed by the optimum positioning of Distributed Generation (DG) in power systems. From a technological and economic point of view, the DG has further advantages, such as minimizing system losses, improving the voltage profile and reducing line congestion. In this paper, the hybrid algorithm which includes Binary Particle Swarm Optimization (BPSO) and Whale Optimization Algorithm (WOA is used for proper positioning and sizing of DG in order to minimize actual and reactive power losses and increase the voltage profile of the distribution system, which in turn improves the system's voltage stability. This approach is simulated using Matlab software on the IEEE 33-bussystems, which demonstrates an increase in the voltage profile and reduces the power loss, when compared to other methods of optimization

Utility grids face significant problems in the power sector in modern times because of the constant increase of loads and the lack of capital spending to install new transmission networks. As a consequence, the generating system is unable to fulfill the anticipated demand and finds it difficult to transport the generated power from central power generation to the distribution network. This can result in high power losses in the distribution system and also disrupt the system's overall performance. This problem can be addressed by the optimum positioning of Distributed Generation (DG) in power systems. From a technological and economic point of view, the DG has further advantages, such as minimizing system losses, improving the voltage profile and reducing line congestion. In this paper, the hybrid algorithm which includes Binary Particle Swarm Optimization (BPSO) and Whale Optimization Algorithm (WOA is used for proper positioning and sizing of DG in order to minimize actual and reactive power losses and increase the voltage profile of the distribution system, which in turn improves the system's voltage stability. This approach is simulated using Matlab software on the IEEE 33-bus systems, which demonstrates an increase in the voltage profile and reduces the power loss, when compared to other methods of optimization.

Cognitive Radio Wireless Sensor Networks (CR-WSN) works on nodes that are dependent on batteries. A critical problem with CR-WSN is a lack of energy, especially in situations such as warfare where rapid and aggressive action is needed. The battery level of nodes degrades CR-WSN performance. Researchers face significant difficulty developing a routing protocol for CR-WSN, and that obstacle is posed by energy consumption to deliver a packet. A substantial number of nodes reside in CR-WSN. Every node in CR-WSN is constrained by battery. To minimize network cost, it should be feasible to have the routing protocol used for CR-WSN to be energy efficient. This paper proposes an optimization-based routing protocol, namely Whale Optimization Routing Protocol (WORP), for identifying the best route in CR-WSN to minimize the delay and lead to network efficiency. WORP draws inspiration from the behaviors of whales as they forage, similar to their hunting activity. By prioritizing residual energy and the total energy of the nodes in the route, WORP encourages energyaware route selection. WORP is examined via simulation with NS2 against current routing protocols. Benchmark performance metrics are used to assess the effectiveness of WORP. Results make an indication that WORP has superior performance than current routing protocols in CR-WSN.

This book exemplifies how algorithms are developed by mimicking nature. Classical techniques for solving day-to-day problems is time-consuming and cannot address complex problems. Metaheuristic algorithms are nature-inspired optimization techniques for solving real-life complex problems. This book emphasizes the social behaviour of insects, animals and other natural entities, in terms of converging power and benefits. Major nature-inspired algorithms discussed in this book include the bee colony algorithm, ant colony algorithm, grey wolf optimization algorithm, whale optimization algorithm, firefly algorithm, bat algorithm, ant lion optimization algorithm, grasshopper optimization algorithm, butterfly optimization algorithm and others. The algorithms have been arranged in chapters to help readers gain better insight into nature-inspired systems and swarm intelligence. All the MATLAB codes have been provided in the appendices of the book to enable readers practice how to solve examples included in all sections. This book is for experts in Engineering and Applied Sciences, Natural and Formal Sciences, Economics, Humanities and Social Sciences.

Clock synchronization is an important component in many distributed applications of wireless sensor networks (WSNs). The deprived method of clock offset and skew estimation causes inaccuracy, synchronization delay, and communication overhead in the protocols. Hence, this paper exploits two techniques of variation truncated mean (VTM) and whale optimization (WO) to enhance the synchronization metrics. Sensor nodes are grouped into several non-overlapped clusters. The cluster head collects the member nodes' local time and computes the synchronization time using the truncated mean method. Nodes with a high variation in the timings compared to a preset value are truncated. The head node broadcasts the in which the whale optimization is aiming at each node to reduce the synchronization error. The intra and inter-cluster synchronizations are accomplished through the multihop message exchange approach. The theoretical analysis is validated, and the simulation outcomes show that the performance metrics in the proposed work are better than the conventional methods by achieving minimum error value.

The current economics of the fish protein industry demand rapid, accurate and expressive prediction algorithms at every step of protein production especially with the challenge of global climate change. This help to predict and analyze functional and nutritional quality then consequently control food allergies in hyper allergic patients. As, it is quite expensive and time-consuming to know these concentrations by the lab experimental tests, especially to conduct large-scale projects. Therefore, this paper introduced a new intelligent algorithm using adaptive neuro-fuzzy inference system based on whale optimization algorithm. This algorithm is used to predict the concentration levels of bioactive amino acids in fish protein hydrolysates at different times during the year. The whale optimization algorithm is used to determine the optimal parameters in adaptive neuro-fuzzy inference system. The results of proposed algorithm are compared with others and it is indicated the higher perfor...

Transmission congestion is mainly caused by agreed transactions between generating companies and distributing companies in deregulated power system networks across the world. Secured and safe operation of power system networks in the deregulated scenario can be ensured by managing the congestion in the lines. FACTS devices are used for changing the power flows so that all the lines are carrying power below their respective limit. In this work, series connected Thyristor Controlled Switched Capacitor(TCSC) devices are installed in suitable lines for relieving congestion in the over loaded lines of the system. The size and location of TCSC devices greatly affects their efficiency in congestion management problem. To maximize the benefits of TCSC in congestion management problem, the site and size of the TCSC devices are optimized by using an optimization technique. The recent bio inspired Whale Optimization Algorithm (WOA) is employed in this proposed work for the optimization of the objective value through identifying the position and size of FACTS devices. This algorithm involves less number of parameters to be tuned for attaining global best results and can be easily coded in Matlab platform. The proposed WOA based method for optimal sizing and sizing of TCSC is implemented on the IEEE 30 bus test system. The results obtained are compared against the results reported by Particle Swarm Optimization algorithm (PSO), Firefly algorithm(FFA)and are found to be outperforming.