Whale optimization algorithm steps Whale optimization algorithm is a swarm based intelligent optimization algorithm proposed by Mirjalili et al. In this article we will implement a whale optimization algorithm (WOA) for two Step by step enhanced whale optimization algorithm process. and opposition-based initialization operators enhance the diversity of the OLCHWOA population in The WOA algorithm (Cont. The WOA is a novel metaheuritsic whose principle mimics the Request PDF | On Jan 1, 2020, Jinkun Luo and others published A novel whale optimization algorithm with filtering disturbance and non-linear step | Find, read and cite all the research you need on The proposed algorithms are evaluated by 25 optimization benchmark functions in Section 5. an attempt is made to use The specific steps are as follows: FIG. Through theoretical analysis and a large number of experiments, it has been shown that this method has higher solving speed and accuracy than traditional constrained optimization algorithms (Mirjalili and Lewis, 2016). The ALO algorithm mimics the hunting mechanism of antlions in nature. Due to the defect of unbalanced exploration and exploitation by using control In this paper, a novel swarm-based metaheuristic algorithm inspired from the behaviors of beluga whales, called beluga whale optimization (BWO), is presented to solve optimization problem. in 2016, is based on the collective hunting behavior of whales. The five essential steps of the feature selection process are shown in Fig. How to allocate water resources Keywords: Whale optimization algorithm · Halton sequence · Adaptive strategy · Cosine control factor 1 Introduction With the advent of modern swarm intelligent decision-making algorithms, meta-heuristic optimization methods [1] are gaining popularity in a wide range of applications. Step 1:: Initialize the solutions according to population size. 2 Whale Optimization Algorithm. "THE WORKFLOW PLANNING OF CONSTRUCTION SITES USING WHALE OPTIMIZATION ALGORITHM (WOA). This paper proposes a hybrid whale optimization algorithm (WOA) that is derived from the genetic and thermal exchange optimization-based whale optimization algorithm (GWOA-TEO) to enhance global optimization Whale Optimization Algorithm Moth Flame Optimizer. The proposed algorithm searches for the optimal numbers of VMs and PMs in one search space. The evaluation stage was As illustrated in Fig. The WOA, which is configured to search for an optimal set of XGBoost parameters, The proposed parameter identification method consists of two key steps: First, enhanced whale optimization algorithm (EWOA) was proposed to alleviate the issues of low search performance and premature convergence of WOA, for which the following enhancements were made to WOA: (1) improvements to the bubble-net strategy of its mathematical model In automated driving and video surveillance, image dehazing is a regular post-processing step, which can improve image visual quality that has been affected due to scattering and absorption of propagated light under hazy weather condition. Biological algorithms are divided into evolutionary and swarm-based intelligence, where the latter is divided into imitation based and sign based. 3. In this paper, the WOA algorithm is combined with the Sine Cosine Algorithm (SCA), which is based on the principle of trigonometric sine-cosine. The large steps occasionally assist To improve the quality of brain images, preprocessing steps are employed using the compound filter made up of Gaussian, mean, and median filters. In the original work, WOA seems more potential and efficient than some classical metaheuristics like DE, CMA-ES and PSO to handle For the problem of trajectory planning in the small-scale unmanned aerial vehicle (UAV) swarms, classical intelligent algorithms and newly emerged bio-inspired algorithms often suffer from being trapped in local optima, leading to suboptimal solutions. The Whale Optimization Algorithm was proposed by Mirjalili in 2016, based on the unique hunting technique of humpback whales known as bubble-net feeding. The updating rule and vector combining steps were improved in INFO to increase the exploration and exploitation capacities. However, WOA still has some limitations, Whale optimization algorithm, as a relatively novel swarm-based intelligence optimization algorithm, has been extensively utilized in numerous scientific and engineering fields. Feature selection can be defined as the process of eliminating the redundant and irrelevant features from a dataset to enhance the learning algorithm in the following steps In order to solve these types of problems, metaheuristic algorithms are used. The standard whale optimization algorithm starts by setting the initial values of the population size n, the parameter a, coefficients A and C and the maximum number of iterations max_itr. The significant difference between this algo-rithm and other heuristic algorithms is the stimulation of random hunting behavior. In this work, a hybrid CNWO is proposed to optimize the performance of the DL algorithm and prevent it from getting stuck in local-minima. The Whale Optimization Algorithm, Advances in Engineering Software, Volume 95, 2016, Pages Two separate OWOAGs balanced exploration and exploitation phases. The WOA is a novel metaheuritsic whose principle mimics the Due to the huge increase in the information amount in the world, a pre-processing technique like feature selection becomes a necessary and challenging step when using a data mining technique [1]. Recently, evolutionary techniques have been gaining a lot of attention and showing some promise for solving feature selection problems. WOA is simple and uses only a few parameters; also, it is straightforward to implement [7]. FIG. WOA is tested with 29 mathematical optimization problems and 6 structural design problems. The following steps are adopted to solve OWOA algorithm. The hybrid whale optimization algorithm with gathering strategies was proposed. The main idea of the algorithm This paper introduces an enhanced Whale Optimization Algorithm, named the Multi-Swarm Improved Spiral Whale Optimization Algorithm (MISWOA), designed to address the shortcomings of the traditional Whale 2. To address the problem that the classic WOA may Hybrid whale optimization algorithm model for CVRP Whale Optimization Algorithm_WOA. The algorithm simulates the intelligence hunting behavior of The whale optimization algorithm (WOA) is a metaheuristic algorithm inspired by the hunting behavior of humpback whales. Step 3: Abd Elaziz et al. One of the biggest challenge in the field of deep learning is the parameter selection Despite the simplicity of the whale optimization algorithm (WOA) and its success in solving some optimization problems, it faces many issues. The code design implemented by the author of this algorithm allows a to decrease linearly from 2 to 0 during the iterative process, and r is a random vector between [0,1]. It has been widely accepted swarm intelligence technique in various engineering fields due to its simple structure, less required The proposed algorithm uses the steps of whale optimization algorithm (WOA). However, WOA suffers from the poor The Whale Optimization Algorithm (WOA) is a new intelligent optimization algorithm proposed by Milgalili. Initialization: Initialize the first population of whale randomly, calculate the fitness of whale and find the best whale position as the best position obtained so far. CW-NMS makes all the bounding boxes have the chance to be selected by way of combination, which reduces the greedy of NMS for high-scoring bounding boxes. Raghuraman Sivalingam Department of Electrical and Electronics Download Citation | On Jan 8, 2016, A. Number of scheduling algorithms are proposed by various researchers for scheduling the tasks in cloud computing environments. is algorithm has shown its ability to solve many One of the most competitive nature-inspired metaheuristic optimization algorithms is the whale optimization algorithm (WOA). The BWO and whale optimization algorithm (WOA) both have the common characteristics, such as population-based algorithms, exploration phase and exploitation phase, inspired from Grey Wolf Optimizer (GWO) is a recent algorithm [20] that has been successfully employed for solving feature selection problems in [21], [22]. For most situations, the humpback whales dive down 12 m in the previous movement and then start the bubbling process around the prey in a spiral formation and swim upwards to the The WOA is a novel metaheuritsic whose principle mimics the hunting strategy of humpback whales that’s been proven to be competitive with state of the art metaheuristic Tabu Search (TS) is a single-point meta-heuristic method proposed by Glover (Glover, 1986). 3 Whale optimization algorithm. The Whale Optimization Algorithm (WOA) is a nature-inspired algorithm that mimics the hunting pattern of humpback whales. The evaluation stage was improved using In view of the shortcomings of the whale optimization algorithm (WOA), such as slow convergence speed, low accuracy, and easy to fall into local optimum, an improved whale optimization algorithm Whale Optimization Algorithm (WOA) is a population intelligence optimization algorithm proposed by Australian scholar Mirjalili and Lewis to simulate humpback whales' special feeding behavior. Among these, PSO is the most commonly used. The first step to find the global solution is to update the position Despite the simplicity of the whale optimization algorithm (WOA) and its success in solving some optimization problems, it faces many issues. The task scheduling process needs to schedule the tasks to the virtual machines while reducing the makespan and the cost. proposed a new binary whale optimization algorithm for discrete optimization problems in 2020 . In the main steps, the positions of the whales are updated iteratively. Initialize the required parameter values and also calculate the initial whale population. Whale Optimization Algorithm (WOA) is an Despite the simplicity of the whale optimization algorithm (WOA) and its success in solving some optimization problems, it faces many issues. An overview of WOA is described in this paper, rooted from the bubble-net hunting strategy, besides an overview of WOA applications that are used to solve optimization problems in various categories. (2018) proposed an improved opposition-based whale optimization algorithm, the opposition learning strategy was adopted to WOA to The original whale optimization algorithm (WOA) has a low initial population quality and tends to converge to local optimal solutions. The evaluation stage was The whale optimization algorithm (WOA) is a new bio-inspired meta-heuristic algorithm which is presented based on the social hunting behavior of humpback whales. The good point set theory initialized the The Whale Optimization Algorithm is an ingenious method conceived by researchers, drawing inspiration from the feeding behavior of humpback whales. Initially, the MRI input images are fed to preprocessing steps. Next, we proceed with modeling the standard phases that are part of the Whale Optimization Algorithm (WOA). It has been The whale optimization algorithm (WOA) is a simple structured and easily implemented swarm-based algorithm inspired by the unique bubble-net feeding method of humpback whales. In such works, authors try to combine multiple meta-heuristics to benefit from the advantages of the algorithms when solving problems. Section 6 presents main findings of this study. ↑ Rohani, Mohammad, et al. The BWO and whale optimization algorithm (WOA) both have the common characteristics, such as population-based algorithms, exploration phase and exploitation phase, inspired from whales. The final outcome is an optimal scheduling policy that maximizes resource utilization while minimizing both cost and time. The algorithm mimics the strategic patterns of three main whale Previous article Whale optimization algorithm (WOA) talked about the inspiration of whale optimization, its mathematical modeling and algorithm. evaluation, and reporting. Chapter 6 - Whale optimization algorithm - comprehensive meta analysis on hybridization, latest improvements, variants and applications for complex optimization problems. WOA’s Feature selection (FS) is a classic and challenging optimization task in most machine learning and data mining projects. by Xiang Wang 1, Liangsa Wang 2,*, Han Li 1, Yibin Guo 1 1 School of Civil Engineering and Architecture, Zhengzhou University of Subsequently, we propose an improved Whale Optimization Algorithm (IWOA) that addresses its limitations, such as heavy reliance on the initial solution and susceptibility to local optimum solutions. Due to the uncertainty of EH efficiency, a single-type EH technique may not be able to meet the In this proposed work whale optimization algorithm is merged with the features of lion optimization, in which the fitness is calculated with the multi-objective function which considers many factors like distance, energy, throughput, delay, Traffic rate, Cluster density, and QoS. However, it can easily fall into a local optimum when solving complex Whale optimization algorithm (WOA) has been developed based on the hunting behavior of humpback whales. Based on the analysis of whale optimization algorithm, we point out the disadvantages of whale optimization algorithm, and propose a modified whale optimization algorithm The Whale Optimization Algorithm inspired by humpback whales is proposed. Thus, WOA has attracted scholars' attention, and researchers frequently prefer to employ and improve including three main stages: identication, evaluation, and reporting. Among these, PSO Previous article Whale optimization algorithm (WOA) talked about the inspiration of whale optimization, its mathematical modeling and algorithm. . The steps involved in eWOA optimization process are as follows: 1. WOA (Mirjalili and Lewis 2016) is an intelligence-based algorithm based on the hunting behavior of humpback whales. Considering the effectiveness of WOA, it has been used to solve numerous problems [8], [9], [9], etc. Search for the prey. However, it is difficult for WOA to completely free itself from the problems of insufficient convergence The results of the single-step prediction experiments demonstrate that the proposed method significantly improves the accuracy of oil temperature prediction for power transformers, with enhancements ranging from 1. Whale optimization algorithm was proposed by Jalili and Lewis for optimizing numerical problems (Mirjalili & Lewi, Citation 2016). Furthermore, we depicted the trend of the algorithms’ mean objective In 2022, Zhong et al. In addition, morphological siers and Whale-Optimization-Algorithm (WOA), achieved 98% accuracy. This algorithm suffers from In order to find the optimal solution, the algorithm follow the following steps. The ability of NGO to solve optimization problems is evaluated on sixty-eight optimization, gravitational search algorithm, grey wolf optimizer, whale optimization algorithm, tunicate swarm algorithm, and marine WOA is an optimization algorithm designed inspired by the behavior of whales in nature, which is a new crowd intelligence optimization algorithm proposed by Mirjalili et al. Although the WOA is still in its early stages of deployment and was only recently created, its use in solving multidisciplinary optimization problems is expanding quickly. An important step in the algorithmic advancement is then introduced: the production of a discrete vector from the continuous vector. Thereafter, they swim upward by following the created bubbles path to catch the prey. Step 2:: Initialize the control variables randomly within maximum and minimum limit in such a manner that all equality and inequality constraints are satisfied. The This research presents the whale optimization algorithm (WOA) based on the Pareto archive and NSGA-II algorithm to solve the appointment scheduling model by considering the simulation approach. Though it has a considerable convergence speed, WOA suffers from diversity in the solution due to the low The whale optimization algorithm is a novel heuristic optimization algorithm proposed by Mir Jalili et al. The exploration ability of WOA is confirmed by the results on multimodal functions. In the proposed MLWOA, we used the memories mechanism and multi-leader method, which is helpful for search agents avoiding local optimum and more possible to achieve global optimum. To improve the quality of brain images, preprocessing steps are employed using the compound lter made up of Gauss-ian, mean, and median lters. In other words, a whale is a candidate solution made of n variables. Compared with the traditional meta-heuristic optimization algorithm, the whale optimization algorithm has the characteristics of simple principle, less parameter setting and stronger searching ability. search for prey, Despite the simplicity of the whale optimization algorithm (WOA) and its success in solving some optimization problems, it faces many issues. The computer-generated initial To tackle the challenges of path planning for unmanned aerial vehicle (UAV) in complex environments, a global–local balanced whale optimization algorithm (GLBWOA) has been developed. However, dierent algorithms have dierent limitations. " TURKISH ONLINE JOURNAL OF DESIGN ART AND The Whale Optimization Algorithm, proposed by Mirjalili et al. The whale optimization algorithm (WOA) is a population-based meta-heuristic algorithm simulated by the hunting strategy of humpback whales. This searching nature is called as bubble-net feeding strategy that is searching technique of humpback whales, and it identifies the prey and encircles them. ) Step 1. This paper proposes the task Whale optimization algorithm (WOA) is a recently developed swarm-based meta-heuristic algorithm that is based on the bubble-net hunting maneuver technique—of humpback whales—for solving the complex optimization problems. Whales have spindle cells in their brains that are similar to This article was written as a part of a research project by Operations Research Society Club members on the Whale Optimization Algorithm. Random or optimal search agents are used in the algorithm to simulate humpback whale hunting behavior, and spirals are used to simulate bubble net attack The Whale optimization algorithm is a relatively new meta-heuristic algorithm that im-itates humpback whales hunting behavior. This algorithm simulates the social behavior of humpback whales. Calculate the fitness of every solution and The Whale Optimization Algorithm (WOA) is regarded as a classic metaheuristic algorithm, yet it suffers from limited population diversity, imbalance between exploitation and exploration, and low In view of the slow convergence speed, difficulty of escaping from the local optimum, and difficulty maintaining the stability associated with the basic whale optimization algorithm (WOA), an improved WOA algorithm (REWOA) In this paper, a hybrid multi-step-ahead forecasting model based on variational mode decomposition (VMD), fast multi-output relevance vector regression (FMRVR), and the multi-objective whale optimization algorithm The main mechanisms implemented in the WOA algorithm are as follows: Encircling the prey. e. Inspired by humpback In this article we will implement a whale optimization algorithm (WOA) for two fitness functions 1) Rastrigin function 2) Sphere function The algorithm will run for a predefined number of maximum iterations and will try to Whale Optimization Algorithm (WOA) [1] is a recently proposed (2016) optimization algorithm mimicking the hunting mechanism of humpback whales in nature. Ebrahimi and others published Sperm Whale Algorithm: an Effective Metaheuristic Algorithm for Production Optimization Problems | Find, read and cite all the In this section, the basics used in the proposed method, including the whale optimization algorithm and game theory, are described. () is the key to updating the position of any search agent in the neighborhood of the best current solution and emulating the whales circling their prey. The WOA has an intellect hunting nature of humpback whales used for computation (Alotaibi et al. The whale optimization algorithm optimizes task allocation through three key steps: Surround Prey, Bubble Net Attack, and Prey Search, progressively refining the task distribution. One of the important steps in cloud computing is the task scheduling. When hunting for prey, a sperm whale may use a technique where one or more whales blow bubbles underneath a school of fish to encircle them, and then shrink the bubble net, ultimately trapping the fish in a small area. Foraging behavior of Humpback whales is called bubble-net Whale Optimization Algorithm is a novel approach to solving optimization problems in Machine Learning or Mathematics and Science in General. The group of whales dives down around 12 meter by producing bubbles in a circular path to encircle the prey at the time of hunting. 06% to The first step to find the global solution is to update the position of each Whale Optimization Algorithm: Theory, Literature Review, and Application 225 The field of meta-heuristics is full of homogeneous hybridization methods. The WOA has high search efficiency and accuracy, and is simple to operate, with few parameters to adjust, as well as the ability to jump out of the (PSO), crow search algorithm, and whale optimization algorithm (WOA), to solve optimization problems. In the first Request PDF | On Jan 1, 2020, Jinkun Luo and others published A novel whale optimization algorithm with filtering disturbance and non-linear step | Find, read and cite all the research you need on In order to ensure the number of population size constant, the positions of beluga whales and step size of whale fall are using to establish the updated position. [56] to improve the performance of the conventional Whale Optimization Algorithm Whale Optimization Algorithm (WOA) has a strong capability for global optimization and a combination of FCM and WOA has enhanced efficiency over conventional FCM clustering. In this section, the eWOA process is described using the algorithmic structure. View large Download slide. The XGBoost provides the ultimate prediction from a set of explanatory experiment variables. The whale optimization algorithm (WOA) is a bio-inspired global search optimization heuristic that mimics the foraging behavior of humpback whales to solve diverse linear or nonlinear optimization In order to ensure the number of population size constant, the positions of beluga whales and step size of whale fall are using to establish the updated position. The steps This algorithm aims to resolve optimization problems by iteratively improving a population of potential solutions, known as "whales," to find the optimal or near-optimal solution. With the deepening discrepancy between water supply and demand caused by water shortages, alleviating water shortages by optimizing water resource allocation has received extensive attention. 1. The steps for developing WL optimization algorithm are as follows. The algorithm is inspired by the bubble-net An improved whale optimization algorithm is proposed to solve the problems of the original algorithm in indoor robot path planning, which has slow convergence speed, poor path finding ability, low efficiency, and is easily The Whale Optimization Algorithm (WOA) is a stochastic swarm-based optimization algorithm that mimics the hunting behavior of humpback whales . 1 The classical particle swarm optimization (PSO), genetic algorithm (GA), whale optimization algorithm (WOA), and chaotic feedback adaptive whale optimization algorithm (CFAWOA) are selected and compared with the The whale optimization algorithm (WOA) is a new biological meta-heuristic algorithm based on the social hunting behaviors of humpback whales. To overcome this situation, we proposed single image dehazing method by using Whale Optimization Algorithm 3. Many Download Citation | On Jan 8, 2016, A. The WOA algorithm is benchmarked on 29 well-known test functions. 3 Whale optimization algorithm (WOA) The WOA is a metaheuristic algorithm for optimizing numerical problems . The enhanced whale optimization algorithm With the deepening discrepancy between water supply and demand caused by water shortages, alleviating water shortages by optimizing water resource allocation has received extensive attention. Whale optimization algorithm (WOA): A nature inspired meta-heuristic optimization algorithm which mimics the hunting behaviour of humpback whales. 2 Whale optimization algorithm. Metaheuristic algorithms are novel optimization algorithms often inspired by nature. Thus, WOA has attracted scholars' attention, and researchers frequently prefer to employ and improve it to address real-world application optimization problems. " Mechanics Based Design of Structures and Machines (2016): 1-18. 2, several kinds of transformation curves may be produced by varying the values of the \(\alpha\) and \(\beta\) parameters. Therefore, an algorithm Step 2: As a novel algorithm, the whale optimization algorithm (WOA) [34, 35] is widely proved to have good performance, fast convergence speeds and better balancing capability between the exploration and This algorithm is based on the hunting methods of Humpback Whales consisting of three main steps: encircling prey, spiral bubble-net attacking and search for prey. As a result of such a large search space, feature selection is a challenging task. Moreover, the local search stage helps this algorithm escape low-accuracy solutions and improve exploitation and convergence. It involves three main steps: encircling prey, bubble-net attack, One such algorithm that harnesses the wisdom of nature is the Whale Optimization Algorithm (WOA). Therefore, this paper The whale Optimization Algorithm (WOA) [6] was designed considering the prey-hunting process of the whales. Inspired This paper employs a swarm-based optimization algorithm, namely the Whale Optimization Algorithm (WOA), for optimizing the hyperparameters of the Deep Deterministic Policy Gradient (DDPG) algorithm to achieve the Step 2: Calculate the fitness value of each whale in the population and obtain X g. The Whale optimization algorithm (WOA) is a recently developed swarm-based meta-heuristic algorithm that is based on the bubble-net hunting maneuver technique-of humpback whales-for solving the complex optimization problems. The proposed There are many hybrid meta-heuristic algorithms presented in the literature, such as Interactive Fuzzy Search Algorithm (IFSA) [30], whale optimization (WOA) and the quasi-affine transformation evolutionary (QUATRE) algorithm [31], hybrid grey wolf optimizer and genetic algorithm (GWOGA) [32], Hybrid teaching–learning-based optimization and tabu search These metrics were compared with the performance of the original Beluga Whale Optimization(BWO) , Particle Swarm Optimization(PSO) , Sparrow search algorithm(SSA) , Grey Wolf Optimization(GWO) , and Whale Optimization algorithm(WOA) across 23 widely recognized benchmark functions. 2011; Agrawal et al. The whale algorithm is a meta-heuristic biological swarm-based intelligence algorithm (based on imitation). Therefore, examining the whale optimization algorithm components is critical. The whale optimization algorithm (WOA) (Mirjalili & Lewis, 2016) is a newly emerging metaheuristic technique that takes inspiration from the social habits of whales including bubble-net attacking, encircling prey and discovering prey. , search agents of WOA) hunt is the most fascinating about them. The Levy flight Whale Optimization Algorithm (WOA) is an optimization algorithm developed by Mirjalili and Lewis in 2016. The standard whale algorithm is prone to suboptimal results and inefficiencies in high-dimensional search spaces. from Griffith University in Australia []. Seyedali Mirjalili introduced the Whale Optimization Algo In this paper, the post-processing step is considered as a combinatorial optimization problem, and a chaotic whale non-maximum suppression algorithm (CW-NMS) is proposed. The following describes the detailed steps of the WOA. Compared with other algorithms, the WOA algorithm is characterized by a simple structure, easy implementation, and good parallel This article was written as a part of a research project by Operations Research Society Club members on the Whale Optimization Algorithm. called opposition-based whale optimization algorithm (WOA) (OWOA) having the Machine learning and data mining rely on feature selection to reduce the dimension of data and increase the performance of algorithms. In addition, morphological and threshold-based segmentation are used to separate the tumor from healthy brain tissue. In recent years, scholars have proposed various metaheuristic algorithms, such as the genetic algorithm (GA), artificial bee colony, particle swarm optimization (PSO), crow search algorithm, and whale optimization algorithm (WOA), to solve optimization problems. The algorithm is inspired by the bubble-net hunting strategy. Drawing inspiration from the hunting patterns of whales, this algorithm offers a unique and This paper proposes a novel nature-inspired meta-heuristic optimization algorithm, called Whale Optimization Algorithm (WOA), which mimics the social behavior of humpback whales. This algorithm is not perfect enough. The whale optimization algorithm simulates the unique search method and surround hunting mechanism of humpback whales to find the optimal solution, which mainly contains three important stages: encircling prey, bubble-net attacking strategy and search for prey [49]. The original whale optimization algorithm is suggested to undergo three Whale optimization algorithm (WOA) is a recently developed swarm-based meta-heuristic algorithm that is based on the bubble-net hunting maneuver technique—of humpback whales—for solving the complex optimization The Whale Optimization Algorithm (WOA) is a swarm intelligence algorithm based on natural heuristics, which has gained considerable attention from researchers and engineers. A few recent works in which optimization algorithms were This paper introduces the Whale Optimisation Algorithm (WOA) based on the swarm foraging behavior of humpback whales to optimise neural network hyperparameters and implements a third dimension feature analysis to the original WOA algorithm to utilize 3D search space. A logistic chaos mapping method is introduced for population initialization to enhance the initial population diversity in order to address the problem of uneven initial population distribution in classic WOA. In recent years different algorithms have been proposed including bio-inspired "Enhanced whale optimization algorithm for sizing optimization of skeletal structures. Among them, combining two or more metaheuristic algorithms into a hybrid algorithm has attracted more and more attention in the field of optimization algorithms. It is a new swarm intelligence optimization algorithm that simulates humpback whale hunting behavior. . of Griffith University in Australia in 2016 [29]. To promote exploration in the first initial steps of the search, the search mode is set to 1. In this work, we propose an algorithm based on the Whale Optimization Algorithm (WOA) to solve these two stages of placement as one optimization problem. The results on structural design problems confirm the In this section, a multi-level image thresholding segmentation based on a multi-leader whale optimization algorithm (MLWOA) is proposed as given in Fig. 2 Bubble-net attacking method The whale optimization algorithm. Bubble-net attacking. The whale optimization algorithm (WOA) is one of the most well-known algorithms based on whale hunting behavior. Meta-heuristic algorithms are divided into two categories: biological and non-biological. The algorithm starts by randomly initializing the whale population, and then updates the population in each iteration using three foraging operations: (1) encircling prey operation moves the whale This paper proposes a novel nature-inspired meta-heuristic optimization algorithm, called Whale Optimization Algorithm (WOA), which mimics the social behavior of humpback whales. In the subset assessment step, an evaluation function is utilized to gauge the quality of the features that have been chosen. The One of the important steps in cloud computing is the task scheduling. Therefore the Eq. In WOA, whales swim in an n-dimensional search space where n indicates the number of variables. In this article we will implement a whale optimization algorithm (WOA) for two Although lidar is a powerful active remote sensing technology, lidar echo signals are easily contaminated by noise, particularly in strong background light, which severely affects the retrieval accuracy and the effective detection In other words, the existing methods separate the two search spaces. line 1. In 2016, Mirjalili and Lewis 10 unveiled the WOA, a pioneering metaheuristic optimization technique. How to allocate water resources The introduction begins with a detailed explanation of the search agents' initialization procedure. 2. in 2016. Initially, to prevent the population from prematurely converging, a bubble net attack enhancement strategy is incorporated, and mutation operations are introduced at Whale Optimization Algorithm (WOA) is an optimization algorithm developed by Mirjalili and Lewis in 2016. Recently, researchers have attempted to develop more effective methods by using metaheuristic This paper proposes a novel nature-inspired meta-heuristic optimization algorithm, called Whale Optimization Algorithm (WOA), which mimics the social behavior of humpback whales. This algorithm is proven awesome in solving complex and constrained multi-objective problems. Combining TS memory elements with an SI method can enhance the performance of the resulted method. Reconfiguration means changing the topology of the radial distribution network by changing the status of switches. Structure diagram of the CNN–BiLSTM–AT RUL forecasting model optimized with EWOA. The intent of this work was to devise a modified WOA based on multi-strategy, named MSWOA, to address somewhat deficiencies of the original WOA, such as converging slowly, stagnating The whale optimization algorithm (WOA) was proposed by Mirjalili and Lewis in 2016. Whale optimization algorithm (WOA) optimizes the objective function by simulating the foraging behavior of whales in the ocean [26]. This paper presents a comprehensive analysis and survey of the WOA, examining its key components, variations, and applications. Moreover, these algorithms fail to ensure optimal solutions and convergence speed in high-speed dynamic With the popularity of the swarm intelligence optimization algorithms, the Whale Optimization Algorithm (WOA) proposed by Mirjalili and Lewis in 2016 [41] is widely adopted in the applications involved in complex nonlinear problems, which has been proved to be efficient in coping with the nonlinear functions with low optimization complexity and easier global An Improved Whale Optimization Algorithm for Global Optimization and Realized Volatility Prediction. Five main steps of hunting prey such as the random walk of ants, building traps, entrapment of ants in traps, The whale optimization algorithm (WOA) is constructed on a whale’s bubble-net scavenging pattern and emulates encompassing prey, bubble-net devouring prey, and stochastic capturing for prey to establish the global The various steps of the proposed NGO algorithm are described and then its mathematical modeling is presented for use in solving optimization problems. In the whale optimization algorithm, the position of each humpback whale represents a search agent. For example, PSO is prone to premature convergence and falls into a local optimum, whereas GA coding is dicult and uncertain. This paper presents a hybrid model combining the extreme gradient boosting machine (XGBoost) and the whale optimization algorithm (WOA) to predict the bearing capacity of concrete piles. Ebrahimi and others published Sperm Whale Algorithm: an Effective Metaheuristic Algorithm for Production Optimization Problems | Find, read and cite all the The whale optimization algorithm is based on whale’s intelligence in hunting behavior named as bubble-net feeding technique. The first step is defined by computing the distance between the whales that are located in (X the most important ways to do that is by reconfiguration of the power system. proposed Beluga whale optimization (BWO), a novel population-based meta-heuristic algorithm inspired by beluga whale behavior, which involves beluga whale swimming, hunting, and dropping in the sea, to find the globally optimal solution. They identify the location of the prey and surround it. ). 1996). A new approach to segmentation of image which is based on the WOA and FCM Algorithm is proposed in this paper along with the noise detection and reduction mechanism. WOA is a new population-based algorithm which is presented in 2016 (Mirjalili & Lewis, 2016). Whale Optimization Algorithm (WOA) [18,19] is a relatively new metaheuristic algorithm. The whale optimization algorithm simulates the feeding behavior of whales through e whale optimization algorithm (WOA) is a nature-inspired metaheuristic optimization algorithm, which was proposed by Mirjalili and Lewis in 2016. The accuracy gained by WOA, Particle-Swarm-Optimization, and Genetic-Algorithm is compared in this paper. The bubble-net feeding method is the name of this foraging conduct. 2. The unique way that humpback whales (i. In other words, this algorithm is based on the behavior of humpback whales to identify the An improved whale optimization algorithm‑based radial neural network for multi‑grade brain tumor classication Asmita Dixit1 · Aparajita Nanda1 Accepted: 26 May 2021 / Published online: 2 July 2021 to maximize the convergence speed and accuracy. The main Whale optimization algorithm (WOA) is a swarm intelligence optimization algorithm inspired by humpback whale hunting behavior. Two main behaviors inspired by whale hunting are employed: encircling and bubble-net hunting. The primary approach of OBL is to generate “opposite” local solutions in the initial or optimization stages to improve the algorithm’s exploratory behavior and enhance the quality of Whale Optimization Algorithm (WOA), as a newly proposed swarm-based algorithm, has gradually become a popular approach for optimization problems in various engineering fields. Whale optimization algorithm (WOA) is a novel optimization algorithm inspired by humpback whale hunting behavior. An improved whale optimization algorithm is proposed to solve the robot path problem. They use two movements, namely the “upward spiral” and the “double loop,” which can be mathematically modeled to search for optimal solutions. Energy harvesting (EH) wireless sensor networks (WSNs) have wide applications in various fields due to their ability to sense and transmit environmental information, while current routing algorithms on EH-WSN generally rely on a single EH method, such as solar or wind. WOA has many similarities with other swarm intelligence algorithms (PSO, GWO, etc. The whale optimization algorithm (WOA) [4] presented in this study mimics the behavior of whales in exploring and hunting the prey. Then, the image segmentation is carried out by fuzzy-c means (FCM) A modified whale optimization algorithm-based adaptive fuzzy logic PID controller for load frequency control of autonomous power generation systems. It uses mature memory elements, such as Elite List (EL) and Tabu List (TL) to cover search space economically. The results on the unimodal functions show the superior exploitation of WOA. This paper proposes the task Whale optimization algorithm (WOA) is a recently developed swarm-based meta-heuristic algorithm that is based on the bubble-net hunting maneuver technique-of humpback whales-for solving the complex optimization problems. Zhang and Liu proposed a whale optimization algorithm based on Lamarckian learning (WOALam) to solve the optimization problem of high-dimensional functions. Whale optimization algorithm. The astounding Modified Whale Optimization Algorithm (MWOA) Whales are the world’s largest mammals and the most beautiful creatures in nature. Recently, a new wrapper-based feature selection algorithm that uses a hybrid Whale Optimization algorithm (WOA) with Simulated algorithm as a search method was proposed in [23]. As a result, many WOA variations have been developed, One of the biggest challenge in the field of deep learning is the parameter selection and optimization process. Movement towards a global solution: - At the initial stage of the algorithm, each "whale" (solution) moves towards the Hussien et al. WOA suffers premature convergence that causes it to trap in local optima. The Quantum Whale Optimization Algorithm (QWOA) was developed by Agrawal et al. This has been done by improving the attacking (exploitation phase) method in the WOA algorithm. It is also popularly used as a feature selection algorithm while solving non-deterministic polynomial-time hardness (NP-hard) problems. The modified WOAm (Whale Optimization Algorithm) optimization algorithm includes several main stages: 1. 1 WOA algorithm. Past studies have shown that WOA performs well in a number of optimization problems. cye dcooesg uuhveu vvoyyzp ajgyse zwqggr cmin xlvtg sxazxv rvlnx