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Background: Studying movement patterns of individual animals over time can give insight into how they interact with the environment and optimize foraging strategies. Humpback whales (Megaptera novaeangliae) undertake long seasonal migrations between feeding areas in polar regions and breeding grounds in tropical areas. During the last decade, several individuals have had up to a 3-month stop-over period around specific fjord-areas in Northern Norway to feed on Norwegian spring-spawning (NSS-) herring (Culpea harengus L.). Their behavioral patterns during this period are not well understood, including why some whales seemingly leave the fjords and then later return within the same season. Methods: To investigate whale behavior during this seasonal stopover, we classified humpback whale tracks into five distinct movement modes; ranging, encamped, nomadic, roundtrip and semi-roundtrip. A behavioral change point analysis (BCPA) was used to select homogeneous segments based on persistence velocity. Then, net squared displacement (NSD) over time was modeled to differentiate movement modes. This study also manually identified longer roundtrips away from the fjords that lasted several days and examined movement modes within these. Results: Inside the fjord systems, encamped mode was most prevalent in December–January, suggesting the whales were mainly foraging on overwintering NSS-herring in this area. During the same winter seasons, half of the whales left the fjords and then returned. We hypothesize that these trips serve as 'searching trips' during which the whales seek better feeding opportunities outside the fjords. If better foraging conditions are not found, they return to the fjords to continue their feeding. The overall most common mode was ranging (54%), particularly seen during the start of their southwards migration and in areas outside the fjord systems, indicating that the whales mainly moved over larger distances in the offshore habitat. Conclusions: This study serves as a baseline for future studies investigating both the searching trip theory and humpback whale behavior in general, and confirms that this method could be useful to analyze local scale movement patterns of satellite tagged whales. [ABSTRACT FROM AUTHOR]

Owing to its low operating costs and high productivity, submerged arc welding (SAW) is a widely utilized machine in various sectors, particularly industry. However, achieving optimal bead width (BW) remains a challenge due to the process being intricate and non-linear. This research is devoted to proposing a novel nature-inspired meta-heuristic optimization algorithm based on the whale optimization algorithm (WOA), called the improved whale optimization algorithm (IWOA), an inertia weight is included, which imitates the social behavior of hunting humpback whales. This parameter controls the influence of the current best solution on the search process during the exploitation phase, allowing an exhaustive search near the optimal solution. Today, due to its competitiveness and efficiency, this algorithm has been widely used to solve complex optimization problems in various fields, including manufacturing and engineering. The proposed algorithm was used to adjust four parameters of the SAW process, including arc voltage (V), welding current (I), welding speed (S), and wire feed (F). The objective function is minimizing the bead width, which is an essential factor in order to maximize weld quality and neatness (consistency). The obtained results have revealed that the IWOA can effectively optimize the SAW process parameters. When compared to other optimization algorithms, which include particle swarm optimization (PSO) and WOA, the suggested method exceeds them all in terms of fast convergence time and solution quality. [ABSTRACT FROM AUTHOR]

Brain tumors, characterized by the uncontrolled growth of abnormal cells, pose a significant threat to human health. Early detection is crucial for successful treatment and improved patient outcomes. Magnetic Resonance Imaging (MRI) is the primary diagnostic tool for brain tumors, providing detailed visualizations of the brain's intricate structures. However, the complexity and variability of tumor shapes and locations often challenge physicians in achieving accurate tumor segmentation on MRI images. Precise tumor segmentation is essential for effective treatment planning and prognosis. To address this challenge, we propose a novel hybrid deep learning technique, Convolutional Neural Network and ResNeXt101 (ConvNet-ResNeXt101), for automated tumor segmentation and classification. Our approach commences with data acquisition from the BRATS 2020 dataset, a benchmark collection of MRI images with corresponding tumor segmentations. Next, we employ batch normalization to smooth and enhance the collected data, followed by feature extraction using the AlexNet model. This involves extracting features based on tumor shape, position, shape, and surface characteristics. To select the most informative features for effective segmentation, we utilize an advanced meta-heuristics algorithm called Advanced Whale Optimization (AWO). AWO mimics the hunting behavior of humpback whales to iteratively search for the optimal feature subset. With the selected features, we perform image segmentation using the ConvNet-ResNeXt101 model. This deep learning architecture combines the strengths of ConvNet and ResNeXt101, a type of ConvNet with aggregated residual connections. Finally, we apply the same ConvNet-ResNeXt101 model for tumor classification, categorizing the segmented tumor into distinct types. Our experiments demonstrate the superior performance of our proposed ConvNet-ResNeXt101 model compared to existing approaches, achieving an accuracy of 99.27% for the tumor core class with a minimum learning elapsed time of 0.53 s. [ABSTRACT FROM AUTHOR]

In this contribution L1-norm target function is minimized by Whale algorithm for the first time. It is a meta-heuristic optimization method which mimics the social behavior of humpback whales. The Whale algorithm is simple and flexible. It takes advantage of a derivation-free mechanism. L1-norm is an efficient tool for outlier detection, nevertheless, its implementation is complex since after formulation of L1-norm minimization for a certain problem, one must solve a linear programming problem by a cumbersome search method while here we only need to set the corresponding L1-norm cost function. During this contribution we also investigate other advantages of the proposed method over traditional methods numerically. Furthermore, the proposed algorithm still has better performance in convergence rate compared to some well-known metaheuristic methods. As the Whale algorithm cannot deal with rank deficient problems, it must be improved. Thus the second algorithm of this contribution is an improved Whale algorithm which is developed here. Three geodetic applications approve the robustness of the proposed approach. [ABSTRACT FROM AUTHOR]

Humpback whales in Bermuda have been observed engaging in a unique behavior of expanding their oral pouches and rubbing them against the seafloor. This behavior is believed to be a method of skin maintenance in areas where barnacles and whale lice congregate. The whales were observed swimming together, often in pairs, and descending into sand holes to rub their expanded pouches against the bottom. This behavior is different from feeding and may serve as a way for the whales to exfoliate and clean hard-to-reach areas of their bodies. Further research is needed to fully understand this behavior. [Extracted from the article]

This article discusses the first documented observation of sexual behavior between two male humpback whales. The researchers observed one emaciated whale with a jaw injury being pursued and penetrated by another whale, who held it in place with its pectoral fins. While nonreproductive sexual behavior is common in many species, humpback whale sexual behavior remains largely undescribed. The significance and contexts of these behaviors in humpback whale social interactions require further study. [Extracted from the article]

This article discusses a study on humpback whale vocalizations in a mid-latitude, winter feeding ground in Virginia. The researchers were unable to determine the sex of the whale, but they found that it vocalized for a significant amount of time, producing 11 distinct call types. They also identified patterns and themes in the whale's vocalizations, suggesting the presence of song fragments. The researchers hypothesize that the tagged whale was a juvenile male practicing singing behavior. The article also highlights the threat of vessel strikes to humpback whales, particularly in the U.S. Atlantic Coast, where ship traffic is increased. The researchers suggest that the whale's passive, head-down orientation during vocalizations may increase its vulnerability to ship strikes. Overall, this study contributes to our understanding of humpback whale behavior and the risks they face in their feeding grounds. [Extracted from the article]

The article discusses the negative impacts of cetacean tourism, such as whale watching, on cetacean populations. It suggests that current management strategies are ineffective due to a lack of regulation, low compliance with guidelines, and a lack of enforcement. The article proposes incorporating concepts from successful business management to improve cetacean tourism management and achieve sustainability. It emphasizes the importance of considering the perspectives and safety of tourists in addition to the well-being of cetaceans. The provided text consists of a list of academic articles and studies related to the impact of tourism on marine mammals, specifically dolphins and whales. The articles cover various topics such as the effects of tourism on behavior, compliance with guidelines, and the sustainability of tourist interactions with cetaceans. The studies examine the behavioral responses of dolphins to vessel presence, the long-term effects of swim-with-dolphin tourism, and the implications of whale-watching activities on conservation. The articles provide valuable insights into the complex relationship between tourism and marine mammals, highlighting the need for responsible and sustainable practices in the industry. The given list of sources includes research articles on the topic of whale watching and its impacts. The articles cover topics such as the negative impacts of whale watching, recent advances in research, compliance with regulations, and the behavior of dolphins and whales in the presence of whale watching vessels. The research examines the effects of whale watching on cetaceans, such as changes in behavior and increased stress levels. It also explores the effectiveness of regulations and codes of conduct in [Extracted from the article]

The article is a review of a book titled "Sex in Cetaceans: Morphology, Behavior, and the Evolution of Sexual Strategies." The book consists of 24 chapters written by different authors, providing an overview of the reproductive strategies and behaviors of cetaceans. It includes new research on the reproduction of specific species of toothed and baleen whales. The chapters cover topics such as genetic tools for parentage analysis, sexual anatomy, reproductive strategies, nonconceptive sexual behavior, infanticide, and drone perspectives on mating behavior. The book also discusses the impact of human activities on cetacean reproductive health. Overall, the book is recommended for those interested in learning more about reproduction and mating systems in aquatic-living mammals. [Extracted from the article]

Studying sound production at different developmental stages can provide insight into the processes involved in vocal ontogeny. Humpback whales (Megaptera novaeangliae) are a known vocal learning species, but their vocal development is poorly understood. While studies of humpback whale calves in the early stages of their lives on the breeding grounds and migration routes exist, little is known about the behavior of these immature, dependent animals by the time they reach the feeding grounds. In this study, we used data from groups of North Atlantic humpback whales in the Gulf of Maine in which all members were simultaneously carrying acoustic recording tags attached with suction cups. This allowed for assignment of likely caller identity using the relative received levels of calls across tags. We analyzed data from 3 calves and 13 adults. There were high levels of call rate variation among these individuals and the results represent preliminary descriptions of calf behavior. Our analysis suggests that, in contrast to the breeding grounds or on migration, calves are no longer acoustically cryptic by the time they reach their feeding ground. Calves and adults both produce calls in bouts, but there may be some differences in bout parameters like inter-call intervals and bout durations. Calves were able to produce most of the adult vocal repertoire but used different call types in different proportions. Finally, we found evidence of immature call types in calves, akin to protosyllables used in babbling in other mammals, including humans. Overall, the sound production of humpback whale calves on the feeding grounds appears to be already similar to that of adults, but with differences in line with ontogenetic changes observed in other vocal learning species. [ABSTRACT FROM AUTHOR]

This article discusses the use of acoustic tags and bottom-moored recorders to study the impulsive sounds produced by humpback whales during surface active behavior. The study combines data from passive acoustic monitoring (PAM) and instrumented suction cup tags to gain insights into humpback whale behavior and communication. The impulsive sounds were found to be associated with surface-active behaviors such as tail slaps and pectoral fin slaps. The presence of these impulsive sounds in PAM data can provide valuable information about the occurrence and activity of humpback whales in a monitored area. [Extracted from the article]

This document provides a compilation of scientific articles that explore various aspects of killer whale behavior, ecology, and interactions with other marine species. The articles discuss the use of drones to study killer whales, their feeding habits and prey selection, their distribution and abundance, and their role in marine ecosystems. The document emphasizes the importance of using drone footage and citizen science to gain insights into killer whale behavior and conservation efforts. The articles cover a range of regions around the world, providing valuable information on killer whales in different contexts. [Extracted from the article]

Parameter identification for complex systems of nonlinear nature is challenging due to the complicated process structure and large number of parameters with different identifiability. The scope of this work is to develop a model-based parameter identification method for a nonlinear industrial winding system. 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, (2) amendments to the humpback whales' movements in the direction of the best whales, and (3) imitation of the schooling behavior of humpback whales when chasing prey. Second, EWOA was acted as a training method for artificial neural networks (ANNs)-type multilayer perceptron (MLP), a method referred to as EWOA-MLP. In this, EWOA was applied to train ANNs in order to mitigate the main difficulties of ANNs due to their nonlinear nature and unknown optimal set of control parameters (i.e., weights and biases). The performance of the proposed EWOA-MLP was assessed in modeling the subsystems of the winding process under study and in solving fifteen classification datasets. The effectiveness of EWOA-MLP in both modeling and classification studies was judged by several pertinent assessment metrics. Results of comparison of the proposed EWOA-MLP with other promising methods firmly confirm the promising performance of EWOA-MLP for both local optima avoidance and convergence rate, proving its value and superiority. Moreover, EWOA-MLP was able to outperform other algorithms in modeling the winding process and solving many classification problems. [ABSTRACT FROM AUTHOR]

Despite its biological importance, the nursing behavior in baleen whales has been poorly documented mainly because of the challenges in tracking whales in their natural environment. We studied the suckling behavior (nursing from the calf's perspective) in < 3-month-old humpback whale calves off Sainte Marie Island, Madagascar, South Western Indian Ocean (breeding/calving area). We investigated the temporal pattern of the suckling behavior, its spatial and behavioral context, and the ontogeny of these characteristics. We exploited data from both camera-equipped and non-camera-equipped animal-borne multi-sensor tags (Acousonde and CATS cam) collected from 2013 to 2022 (37 calves). Our dataset constitutes the largest sample assembled so far to investigate the suckling behavior in free-ranging whales. We found that the suckling event duration does not vary much with the calves' relative age. The estimated proportion of time spent suckling ranged between 0.8 and 1.6%. The suckling events mainly occurred during the descent and bottom phases of dives and rarely during the ascent phase of dives or surface activities. Strong evidence of humpback whale suckling at night is presented for the first time. We found that suckling events were often performed in bouts of 2–6 events. Suckling bouts mostly occurred less than 10 min apart, and suckling sessions were separated by about 2 h of non-suckling periods. In other words, humpback whale calves suckled several times throughout the day in short sessions similar to terrestrial "followers." Our results add to the evidence that humpback whales, and potentially most baleen whales, are behaviorally and functionally similar to terrestrial "followers" regarding maternal strategy. Significance statement: Young mammals rely on the milk provided by their mother to survive during their early life stage. The suckling behavior, i.e., the procurement of milk from the mother, has been widely studied for terrestrial mammals but not for fully aquatic mammals like the baleen whales due to the challenges in tracking them. By placing sensors and cameras on calves, we described the suckling pattern in humpback whales, the most common baleen whale species. We found that the temporal pattern of suckling in humpback whales is similar to those of terrestrial precocial mammals experiencing comparable environments (open habitat with predators): the young suckle frequently but in short sessions. Our results offer a new insight supporting the hypothesis that the rearing strategy used by baleen whales is an aquatic version of the "following" behavior found in these precocial terrestrial species. [ABSTRACT FROM AUTHOR]

Recently, the reduction of electricity costs in residential areas has become one of the major research fields. A comprehensive power management system is required to lower the cost of both power generation and consumption. Moreover, the world's energy consumption is continuously increasing. This rise is the consequence of perpetual birth rates and the expansion of factories, which have both significantly increased carbon dioxide emissions and global warming. In order to address these difficulties, hybrid renewable energy systems have evolved in an important way since the development of renewable energy sources. Two meta-heuristic approaches are applied in this paper: the first one is the particle swarm optimization (PSO), which is inspired by the social behavior of bird swarms, and the second one is the whale optimization algorithm (WOA) which is inspired by humpback whale hunting behavior, to tackle the main issue in this work which is decreasing the overall electricity cost of a residential home. The residential home considered in this work consists of two renewable energy sources: a solar panel and the wind turbine, and a power storage system based on battery. This residential home is connected to the main grid through a bidirectional inverter. Furthermore, a comparative optimization study was suggested where we propose two different residential load demands. The results showed the best decrease in the total electricity cost and the best optimal solution by employing the whale optimization approach in both proposed cases. [ABSTRACT FROM AUTHOR]

Insights into the use of an unmanned aerial vehicle (UAV) to investigate the behavior of humpback whales (Megaptera novaeangliae) in Vava'u, Kingdom of Tonga. For whales on or just below the surface, the red/green and red/blue depth-invariant band pairs were the most effective band pair combination for enhancing contrast between the whale and surrounding waters, both visually (Figure 3A and C) and resulting in the highest anomaly values (Table 1). The UAV was launched to an initial altitude of 55 m to provide sufficient sensor field of view for whale identification and lowered to >=25 m once whale(s) were visible on the controller screen. [Extracted from the article]

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Biggs killer whale (Orcinus orca) predation on subadult humpback whales (Megaptera novaeangliae) in lower Cook Inlet and Kodiak, Alaska. Associations with multiple male groups increase the energy expenditure of humpback whale (Megaptera novaeangliae) female and calf pairs on the breeding grounds. Escort service: Sex and relatedness of humpback whales accompanying mother-calf pairs off Western Australia Humpback whale "super-groups" - A novel low-latitude feeding behaviour of Southern Hemisphere humpback whales (Megaptera novaeangliae) in the Benguela Upwelling System. [Extracted from the article]

Whale optimization algorithm (WOA) has been developed based on the hunting behavior of humpback whales. Though it has a considerable convergence speed, WOA suffers from diversity in the solution due to the low exploration of search space. As a result, it tends to trap in local optima and suffer from low solution accuracy. This study proposes a novel improved WOA method (ImWOA) with increased diversity in the solution to avoid the aforesaid gaps. The random solution selection process in the search prey phase is altered to increase exploration. The whale's cooperative hunting strategy is also incorporated in the algorithm's exploitation phase to balance the exploration and exploitation phase of WOA. Also, the total iterations are divided into two halves explicitly for exploration and exploitation purposes. The modifications facilitate WOA to jump out of local optima, increase solution accuracy, and increase convergence speed. The experiments were carried out evaluating IEEE CEC 2017 functions in dimensions 10, 30, 50, and 100. The performances were compared with basic algorithms as well as recent WOA variants. Three engineering design problems have also been solved to check its problem-solving ability and compared with a wide range of algorithms. Moreover, the image segmentation problem with multiple thresholding approaches has been solved by using the proposed ImWOA. Comparing results with state-of-the-art algorithms and modified WOAs, statistical analysis, diversity analysis, and convergence analysis validate that ImWOA is superior or competitive. [ABSTRACT FROM AUTHOR]

Cables are mainly composed of wires and are used to transmit signals or electrical energy. It can be seen everywhere in real life. However, due to the changeable environment in which it is located, it often leads to the problem of insulation aging. Therefore, it is particularly important to study the law of cable insulation aging and to evaluate its life. Humpback whale hunting behavior is simulated by WOA, a swarming intelligence algorithm. It can intelligently identify the relationship between various data. SVR is a linear regression model with a special computational loss. The aim of this paper is to investigate a WOA-SVR model to evaluate the aging law and life of cable insulation. This paper analyzes a variety of detection models. Finally, the partial discharge detection model and the depolarization detection model are selected for comparative testing with the WOA-SVR model studied in this paper. The test method is to count the aging of cable insulation under different temperature, humidity, and electric field strength environments. The test results show that the evaluation accuracy of the WOA-SVR model in this paper is 92%, 97%, and 98%, respectively, under different temperatures, humidity, and electric field strengths. The average accuracy of its evaluation is higher than the other two models. Therefore, the WOA-SVR model is more accurate and reliable for the cable insulation aging law and life evaluation. [ABSTRACT FROM AUTHOR]

Cables are mainly composed of wires and are used to transmit signals or electrical energy. It can be seen everywhere in real life. However, due to the changeable environment in which it is located, it often leads to the problem of insulation aging. Therefore, it is particularly important to study the law of cable insulation aging and to evaluate its life. Humpback whale hunting behavior is simulated by WOA, a swarming intelligence algorithm. It can intelligently identify the relationship between various data. SVR is a linear regression model with a special computational loss. The aim of this paper is to investigate a WOA-SVR model to evaluate the aging law and life of cable insulation. This paper analyzes a variety of detection models. Finally, the partial discharge detection model and the depolarization detection model are selected for comparative testing with the WOA-SVR model studied in this paper. The test method is to count the aging of cable insulation under different temperature, humidity, and electric field strength environments. The test results show that the evaluation accuracy of the WOA-SVR model in this paper is 92%, 97%, and 98%, respectively, under different temperatures, humidity, and electric field strengths. The average accuracy of its evaluation is higher than the other two models. Therefore, the WOA-SVR model is more accurate and reliable for the cable insulation aging law and life evaluation. [ABSTRACT FROM AUTHOR]

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

The second half of the book consists of a mix of reviews of various species and groups of closely related species, as well as a more in-depth analysis of humpback whale vocal behavior. Although this chapter is about the anatomical basis of behavior rather than behavior itself, Reidenberg shows that understanding anatomy also helps explain behavioral differences, not just in vocal behavior, but also in swimming speed and foraging. I Ethology and Behavioral Ecology of Mysticetes i is one of the last of the ambitious and successful series of books I Ethology and Behavioral Ecology of Marine Mammals i , edited by Bernd Würsig. [Extracted from the article]

The whale optimization algorithm (WOA) is inspired by humpback whale social behavior and WOA is a popular swarm intelligence algorithm. Yet, the WOA does have certain flaws, such as a restricted global search capability and an inconsistent convergence speed, and when dealing with complex optimization problems, WOA is easy to fall into local optimum. To address the WOA's shortcomings, a modified whale optimization algorithm with convergence and exploitability enhancement called MWOA-CEE is proposed. Three operators, opposition-based learning, exponentially decreasing function, and elite-guided Cauchy mutation, are integrated into the WOA to enhance the exploration performance and avoid local optimum. First, the opposition-based learning used the opposition-based concept to generate the opposite position of the candidate solution, which can enhance the convergence ability and quality of the solution. Then, the exponentially decreasing function balances the exploitation and exploration phases and boosts the exploitation capability. In the end, the elite-guided Cauchy mutation generated a wide disturbance near the current candidate solution, which improves solutions diversity and increases the algorithm's global search capability. MWOA-CEE is validated on 19 optimization benchmark functions and 14 CEC2014 benchmark functions and used to identify IIR systems. It is compared to seven of the commonly used algorithms. The results prove that MWOA-CEE outperforms the other algorithms in terms of final solution quality and convergence rate. [ABSTRACT FROM AUTHOR]

Female humpback whale (Megaptera novaeangliae) reproductive class and male-female interactions during the breeding season [Doctoral dissertation]. However, note that the dive behavior of PE2 and SE4 is still correlated through 13:20 even though the animal was not visually sighted again, and that both PE2 and SE4 surfaced at 13:02 at the same time as the female. Humpback whale competitive group behavior on breeding grounds was first described in the early 1980s (Baker & Herman, 1984; Mobley & Herman, 1985; Tyack & Whitehead, 1982), and has been found to be consistent across breeding grounds and ocean basins. Quantifying the behavior of humpback whales (Megaptera novaeangliae) and potential responses to sonar. [Extracted from the article]

Summary: Swarm intelligence meta‐heuristic optimization algorithms for optimizing engineering applications have become increasingly popular. The whale optimization algorithm (WOA) is a recent and effective swarm intelligence optimization algorithm that mimics humpback whales' behaviors when optimizing a problem. Applying the algorithm to achieve optimal solutions has shown good results compared to most meta‐heuristic optimization algorithms. However, complex applications might require the processing of large‐scale computations, which results in down‐scaling computational throughput of WOA. Apache Spark, a well‐known parallel data processing framework, is the most recent distributed computing framework and has been proven to be the most efficient. In this article, we propose a WOA implementation on top of Apache Spark, represented as SBWOA, to enhance its computational performance while providing higher scalability of the algorithm for handling more complex problems. Compared with the recently reported MapReduce WOA (MR‐WOA), and serial implementation of WOA, our approach achieves significant enhancements with respect to computational performance for the highest population size with the maximum number of iterations. SBWOA successfully handles higher‐complexity problems which require complex computations. [ABSTRACT FROM AUTHOR]

Getting maternal milk through nursing is vital for all newborn mammals. Despite its importance, nursing has been poorly documented in humpback whales (Megaptera novaeangliae). Nursing is difficult to observe underwater without disturbing the whales and is usually impossible to observe from a ship. We attempted to observe nursing from the calf's perspective by placing CATS cam tags on three humpback whale calves in the Sainte Marie channel, Madagascar, Indian Ocean, during the breeding seasons. CATS cam tags are animal-borne multi-sensor tags equipped with a video camera, a hydrophone, and several auxiliary sensors (including a 3-axis accelerometer, a 3-axis magnetometer, and a depth sensor). The use of multi-sensor tags minimized potential disturbance from human presence. A total of 10.52 h of video recordings were collected with the corresponding auxiliary data. Video recordings were manually analyzed and correlated with the auxiliary data, allowing us to extract different kinematic features including the depth rate, speed, Fluke Stroke Rate (FSR), Overall Body Dynamic Acceleration (ODBA), pitch, roll, and roll rate. We found that suckling events lasted 18.8 ± 8.8 s on average (N =34) and were performed mostly during dives. Suckling events represented 1.7% of the total observation time. During suckling, the calves were visually estimated to be at a 30-45° pitch angle relative to the midline of their mother's body and were always observed rolling either to the right or to the left. In our auxiliary dataset, we confirmed that suckling behavior was primarily characterized by a high average absolute roll and additionally we also found that it was likely characterized by a high average FSR and a low average speed. Kinematic features were used for supervised machine learning in order to subsequently detect suckling behavior automatically. Our study is a proof of method on which future investigations can build upon. It opens new opportunities for further investigation of suckling behavior in humpback whales and the baleen whale species. [ABSTRACT FROM AUTHOR]

The whale optimization algorithm (WOA) is a popular swarm intelligence algorithm which simulates the hunting behavior of humpback whales. WOA has the deficiency of easily falling into the local optimal solutions. In order to overcome the weakness of the WOA, a modified variant of WOA called OCDWOA is proposed. There are four main operators introduced into the OCDWOA to enhance the search performance of WOA. The operators include opposition-based learning method, nonlinear parameter design, density peak clustering strategy, and differential evolution. The proposed algorithm is tested on 19 optimization benchmark functions and a seismic inversion problem. OCDWOA is compared with the classical WOA and three typical variants of WOA. The results demonstrate that OCDWOA outperforms the compared algorithms in terms of obtaining the global optimal solution. [ABSTRACT FROM AUTHOR]

The search for food stimulated by hunger is a common phenomenon in the animal world. Mimicking the concept, recently, an optimization algorithm Hunger Games Search (HGS) has been proposed for global optimization. On the other side, the Whale Optimization Algorithm (WOA) is a commonly utilized nature‐ inspired algorithm portrayed by a straightforward construction with easy parameters imitating the hunting behavior of humpback whales. However, due to minimum exploration of the search space, WOA has a high chance of trapping into local solutions, and more exploitation leads it towards premature convergence. The concept of hunger from HGS is merged with the food searching techniques of the whale to lessen the inherent drawbacks of WOA. Two weights of HGS are adaptively designed for every whale using the respective hunger level for balancing search strategies. Performance verification of the proposed hunger search‐based whale optimization algorithm (HSWOA) is done by comparing it with 10 state‐of‐the‐art algorithms, including three very recently developed algorithms on 30 classical benchmark functions. Comparison with some basic algorithms, recently modified algorithms, and WOA variants is performed using IEEE CEC 2019 function set. Statistical performance of the proposed algorithm is verified with Friedman's test, boxplot analysis, and Nemenyi multiple comparison test. The operating speed of the algorithm is determined and tested with complexity analysis and convergence analysis. Finally, seven real‐world engineering problems are solved and compared with a list of metaheuristic algorithms. Numerical and statistical performance comparison with state‐of‐the‐art algorithms confirms the efficacy of the newly designed algorithm. [ABSTRACT FROM AUTHOR]

This study builds a predictive model capable of estimating the critical temperature of a superconductor from experimentally determined physico-chemical properties of the material (input variables): features extracted from the thermal conductivity, atomic radius, valence, electron affinity and atomic mass. This original model is built using a novel hybrid algorithm relied on the multivariate adaptive regression splines (MARS) technique in combination with a nature-inspired meta-heuristic optimization algorithm termed the whale optimization algorithm (WOA) that mimics the social behavior of humpback whales. Additionally, the Ridge, Lasso and Elastic-net regression models were fitted to the same experimental data for comparison purposes. The results of the current investigation indicate that the critical temperature of a superconductor can be successfully predicted using this proposed hybrid WOA/MARS-based model. Furthermore, the results obtained with the Ridge, Lasso and Elastic-net regression models are clearly worse than those obtained with the WOA/MARS-based model. [ABSTRACT FROM AUTHOR]

Background: Despite exhibiting one of the longest migrations in the world, half of the humpback whale migratory cycle has remained unexamined. Until now, no study has provided a continuous description of humpback whale migratory behavior from a feeding ground to a calving ground. We present new information on satellite-derived offshore migratory movements of 16 Breeding Stock G humpback whales from Antarctic feeding grounds to South American calving grounds. Satellite locations were used to demonstrate migratory corridors, while the impact of departure date on migration speed was assessed using a linear regression. A Bayesian hierarchical state–space animal movement model (HSSM) was utilized to investigate the presence of Area Restricted Search (ARS) en route. Results: 35,642 Argos locations from 16 tagged whales from 2012 to 2017 were collected. The 16 whales were tracked for a mean of 38.5 days of migration (range 10–151 days). The length of individually derived tracks ranged from 645 to 6381 km. Humpbacks were widely dispersed geographically during the initial and middle stages of their migration, but convened in two convergence regions near the southernmost point of Chile as well as Peru's Illescas Peninsula. The state–space model showed almost no instances of ARS along the migratory route. The linear regression assessing whether departure date affected migration speed showed suggestive but inconclusive support for a positive trend between the two variables. Results suggestive of stratification by sex and reproductive status were found for departure date and route choice. Conclusions: This multi-year study sets a baseline against which the effects of climate change on humpback whales can be studied across years and conditions and provides an excellent starting point for the investigation into humpback whale migration. [ABSTRACT FROM AUTHOR]

Male humpback whales (Megaptera novaeangliae) sing in mating aggregations in the form of song displays, but much less is known about how both sexes use sound on their feeding grounds. Here, we test different hypotheses about the function of vocalizations in 14 foraging humpback whales tagged with sound and movement recording Dtags in Greenland. We show that this population of foraging humpback whales have an overall low call rate of 11.9 calls h−1 (inter-quartile range = 12.1) with no support for the hypotheses that they employ sound in the localization or manipulation of prey nor in the coordination of lunge feeding. The calls had a mean received level of 135 ± 5dB re 1 μPa, which is some 30 dB lower than maximum levels of song recorded on similar deployed tags, suggesting a much smaller active space of these vocalizations. This reduced active space might, in concert with low call rates, serve to mitigate eavesdropping by predatory killer whales or conspecifics competing for the same prey resources. We conclude that feeding humpback whales in Greenland produce low level, infrequent calls suggesting that calling is not a prerequisite for successful feeding, but likely serves to mediate within group social interactions. [ABSTRACT FROM AUTHOR]

Conspicuous signals, such as acoustic sexual signals, can be "risky" in that they may be overheard by competitors. This means the signaller must balance the benefits of signaling to the intended receiver with the costs of providing information to these competitors. Depending on the signaller context, this balance may change. Male humpback whales produce long, high amplitude songs. This conspicuous sexual signal is presumably used for advertisement when alone and possibly aimed at females when escorting. Regardless of which animal is the intended receiver, as it is conspicuous, it also available to multiple eavesdropping competitors. Here, we show a singing behavior in humpback whales that comprises of a series of "decisions" which depend on the singer's context (advertising alone versus escorting a female) and the eavesdropping risk. In lone males not with a female, eavesdropping risk was not a significant factor in determining the "decision" to start singing and for how long. Escorting singers, however, were presumably at risk of losing a female to a competitor and were less likely to invest effort into singing as eavesdropping risk increased. Results suggest signaling males seem to be capable of assessing eavesdropping risk, and modifying their signaling behavior, according to the trade-off between the costs and benefits of signaling within each context. Given male humpback whales are using long-range communication signals available to a network of competitors, these trade-off "decisions" are likely to be complex. Significance statement: Humpback whale males use song as a long-range sexual signal. However, for any long-range conspicuous sexual signal, there is a direct trade-off between the advantages of using a conspicuous signal, with the cost of signaling to eavesdroppers such as rival males. Here, we showed that the "decisions" made by humpback whale singers depended on a combination of social factors including a social "trigger," eavesdropping risk, and whether the singer was escorting a female. Results suggest male humpback whales are selecting the most appropriate singing strategy based on their assessment of a constantly changing, complex social environment. [ABSTRACT FROM AUTHOR]

Male humpback whales produce complex sounds known as songs during their breeding season. Previous studies have shown diel patterns of song in their breeding areas, but there had been no similar studies in the breeding area around Okinawa, Japan. To study diel patterns of song and the behavior of humpback whales in Okinawa, we conducted 24 hr recording with a fixed hydrophone in 2007, and vessel‐based sighting surveys during 2014–2017. Song was monitored for 15 days, with peaks at sunrise and around 2200. Singing activity declined significantly between sunrise and sunset, then increased until 2200. Activity levels at night were higher and more stable than during the day. During 278 days of sighting surveys, 2,551 whales in 1,382 groups were observed. 79 individuals were confirmed as singers, all of which were lone whales. In six cases, singing individuals stopped singing before joining a group or began singing after leaving a group. Previous studies have shown that group size of humpback whales increases through the day. Considering the results from our study and the former studies, the decrease in singing activity as the day progresses may be a result of aggregation increasing, thus reducing the number of lone singers during the day. [ABSTRACT FROM AUTHOR]

Report on a workshop on problems related to humpback whales (Megaptera novaeangliae) in Hawaii (pp. 29 - 44). (Spectrographs, all Hann window, 50% overlap, FFT/DFT Example 1: Offshore 2,048, Hawaii 8,192; Example 2: Offshore 1,042, Hawaii 8,192; Example 3: Offshore 512, Hawaii 4,096). gl This encounter in deep ocean basin adds to evidence of a tropical offshore distribution of North Pacific humpback whales at the same time as they are inshore in known traditional winter assemblies. North Pacific humpback whales migrate between high latitude feeding grounds around the Pacific Rim and winter breeding assemblies in tropical and subtropical waters (Nishiwaki, 1966; Rice, 1978). [Extracted from the article]

This paper presents a whale optimization algorithm (WOA) based on forward looking sonar to achieve two‐dimensional optimal path planning for an autonomous underwater vehicle. The purpose of path planning is not only to effectively avoid threat regions and safely reach the intended target with minimum fuel cost but also to obtain an optimal or near‐optimal path in a complex ocean battlefield environment. The WOA, based on the bubble‐net attacking behavior of humpback whales, mimics encircling the prey, attacks with a bubble‐net method, and search for prey to effectively determine the global optimal solution in the search space. The WOA not only has fast convergence speed and high calculation accuracy but can also effectively balance exploration and exploitation to avoid falling into a local optimum and obtain the global optimal solution. Five sets of experiments are applied to verify the superiority and stability of the WOA. Compared with other algorithms, such as artificial bee colony, bat algorithm, cuckoo search, flower pollination algorithm, moth‐flame optimization algorithm, particle swarm optimization, and water wave optimization, the WOA exhibits better optimization performance and stronger robustness. The experimental results reveal that the WOA can find the shortest path compared with all the other algorithms, and it is an effective and feasible method for solving the path planning problem. [ABSTRACT FROM AUTHOR]

Although humpback whale vocalizations can occur above the 1 kHz Nyquist frequency, this sampling rate is sufficient for inferring long-term patterns of hourly presence due to the amount of low-frequency energy in humpback song and calls (Murray et al., 2014). Humpback whale songs were detected March through May (2008) and December through February (2008-2009), a period which extends into the traditional winter breeding season of North Atlantic humpback whales. Repertoire and classification of non-song calls in Southeast Alaskan humpback whales (Megaptera novaeangliae). Humpback whales ( I Megaptera novaeangliae) i are well-known for their long migrations, strong maternal site-fidelity, and their striking vocalization patterns called "songs" (Katona & Beard, 1990; Payne & McVay, 1971). [Extracted from the article]

The accuracy and stability of relative pose estimation of an autonomous underwater vehicle (AUV) and a target depend on whether the characteristics of the underwater image can be accurately and quickly extracted. In this paper, a whale optimization algorithm (WOA) based on lateral inhibition (LI) is proposed to solve the image matching and vision-guided AUV docking problem. The proposed method is named the LI-WOA. The WOA is motivated by the behavior of humpback whales, and it mainly imitates encircling prey, bubble-net attacking and searching for prey to obtain the globally optimal solution in the search space. The WOA not only balances exploration and exploitation but also has a faster convergence speed, higher calculation accuracy and stronger robustness than other approaches. The lateral inhibition mechanism can effectively perform image enhancement and image edge extraction to improve the accuracy and stability of image matching. The LI-WOA combines the optimization efficiency of the WOA and the matching accuracy of the LI mechanism to improve convergence accuracy and the correct matching rate. To verify its effectiveness and feasibility, the WOA is compared with other algorithms by maximizing the similarity between the original image and the template image. The experimental results show that the LI-WOA has a better average value, a higher correct rate, less execution time and stronger robustness than other algorithms. The LI-WOA is an effective and stable method for solving the image matching and vision-guided AUV docking problem. [ABSTRACT FROM AUTHOR]

Cloud computing environment supply the computing resources based on the demand of cloud user requirements. It builds the resource allocation model through distributed computing and virtualization to emphasize the scalability of cloud services. However, to manage the demand of user creates a complex issue in the on-demand resource allocation framework. Therefore, an effective optimization algorithm named Sunflower Whale Optimization Algorithm (SFWOA) is proposed to solve the issues in the resource allocation model. The concept of virtualization helps to execute the tasks based on the availability of resources and reduces the response time. The tasks are allocated to the virtual machine in a distributed manner to balance the workload in cloud. The proposed SFWOA uses the hunting strategy and the foraging behavior of humpback whale along with the peculiar behavior of sunflower to achieve the effective resource allocation. The performance enhancement of the proposed SFWOA is revealed through the performance measures such that the proposed method attained a maximum resource utilization of 0.942 using 20 virtual machines, maximum memory utilization of 0.215, and maximum CPU utilization of 0.269 using 15 virtual machines, and minimum skewness of 0.001 using 25 virtual machines. [ABSTRACT FROM AUTHOR]

Identifying stress and its level has always been a challenging area for researchers. A lot of work is going on around the world on the same. An attempt has been made by the authors in this paper as they present a methodology for detecting stress in EEG signals. Electroencephalogram (EEG) is commonly used to acquire brain signal activity. Though there exist other techniques to extract the same like Functional magnetic resonance imaging (fMRI), positron emission tomography (PET) we have used EEG as it is economical. We have used an open-source dataset for EEG data. Various images are used as the target stressor for collecting EEG signals. After feature selection and extraction, a support vector machine (SVM) with a whale optimization algorithm (WOA) in its kernel function for classification is used. WOA is a bio-inspired meta-heuristic algorithm, based on the hunting behavior of humpback whales. Using this method, we had obtained 91% accuracy for detecting the stress. The paper also compared the previous work done in detecting stress with the work proposed in this paper. [ABSTRACT FROM AUTHOR]

Summary: The whale optimization algorithm (WOA) is a new biological meta‐heuristic algorithm based on the social hunting behaviors of humpback whales. However, it can easily fall into a local optimum when solving complex problems and exhibits slow convergence speed and poor exploration. This study proposed three improved versions of the WOA based on the concepts of chaos initialization, nonlinear convergence factor, and chaotic inertial weight to enhance its exploration abilities. These properties were employed to improve the population diversity and maintain the balance between exploration and exploitation. The performance of the best version was compared with those of moth‐flame optimization, firefly algorithm, particle swarm optimization, gray wolf optimizer, flower pollination algorithm, original WOA, and two recently proposed hybrid WOA through 19 benchmark functions. Experimental results indicated that the proposed algorithms exhibit better performance in terms of complexity and convergence speed. [ABSTRACT FROM AUTHOR]

The Whale Optimization Algorithm (WOA) is a recent meta-heuristic that can be explored in global optimization problems. This paper proposes a new parameter adjustment mechanism that influences the probability of the food recognition process in the whale algorithm. The adjustment is performed using a fuzzy inference system that uses the current iteration number as input information. Our simulation results are compared with other meta-heuristics such as the conventional version of WOA, Particle Swarm Optimization (PSO) and Differential Evolution (DE). All algorithms are used to optimize ten test functions (Sphere, Schwefel 2.22, Quartic, Rosenbrock, Ackley, Rastrigin, Penalty 1, Schwefel 2.21, Six hump camel back and Shekel 1) in order to obtain their respective optimal values for be used as criteria for analysis and comparison. The results of the simulations show that the proposed fuzzy inference system improves the convergence of WOA and also is competitive in relation to the other algorithms, i.e., classical WOA, PSO and DE. [ABSTRACT FROM AUTHOR]

The Whale Optimization Algorithm (WOA), which mimics the social behavior of humpback whales, is a recently developed meta-heuristic algorithm. However, WOA exists the defect of easily falling into local optimum. This paper proposes a new WOA based on a self-adapting parameter adjustment and a mix mutation strategy (abbreviated as SMWOA). The self-adapting parameter adjustment strategy based on a normal distribution is adopted to make the algorithm jump out of local optima and enhance the global exploration capability. Meanwhile, in order to obtain a better tradeoff between the exploration and exploitation capabilities of the WOA, a novel mix mutation strategy is embedded in the proposed algorithm. The performance of SMWOA is tested on 23 benchmark functions and test suites composed of four engineering design problems. Experimental results and statistical analyses indicate that the proposed algorithm is very competitive when compared to the original WOA as well as some state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Whale optimization algorithm (WOA) is a recently proposed meta-heuristic algorithm which imitates the hunting behavior of humpback whales. Due to its characteristic advantages, it has found its place in the mature population-based methods in many scientific and engineering fields. Because WOA was proposed for continuous optimization, it cannot be directly used to solve discrete optimization problems. For this purpose, we first give a new V -shaped function by drawing lesson from the existing discretization methods, which transfer a real vector to an integer vector. On this basis, we propose a novel discrete whale optimization algorithm (DWOA). DWOA uses the new proposed V -shaped function to generate an integer vector, and it can be used to solve discrete optimization problems with solution space {0,1,...,m1}×{0,1,...,m2}×... ×{0,1,...,mn}. To verify effectiveness of DWOA for the 0-1 knapsack problem and the discount {0-1} knapsack problem, we solve their benchmark instances from published literature and compare with the state-of-the-art algorithms. The comparison results show that the DWOA has more superiority than existing algorithms for the two kinds of knapsack problems. [ABSTRACT FROM AUTHOR]

The effect of nature-based tourism on wildlife has been the focus of much attention. Studies have demonstrated how boat-based cetacean-watching tourism can cause both short-term and long-term effects on targeted populations. However, limited attention has been given to the effect of swim-with activities on humpback whales (Megaptera novaeangliae). This study qualified whale responses to swim-with activities off Reunion Island during the 2018 humpback whales breeding season. We used both under- and above-water videos collected from social media outlets, commercial whale-watching operators, and audiovisual professionals. We documented a high rate of agonistic whale behaviors (during 42.1% of all observations; n = 164) towards swimmers within videos containing swim-with events. We documented seven agonistic behaviors including threat, attack, or defense behaviors that were predominantly exhibited by mother/calf groups (73.8%; n = 121) and by singletons (16.5%; n = 27). Pectoral shears (27.4%) and fluke thrashes (23.2%) were the most exhibited agonistic whale behaviors aimed towards swimmers, both of which pose a danger and serious injury to swimmers. During swim-with attempts whales changed their behavioral state (82.3%, n = 159) and used avoidance tactics to avoid swimmers (56.1%, n = 92). Whales exhibited a higher rate of agonistic behaviors when swim groups were active and dispersed, in contrast to when they were quiet and compact. To mitigate whale disturbance and improve swimmer safety, we recommend avoiding swimming with whale groups containing calfs. Our findings support the implementation of strong regulations and educational tools to ensure a sustainable practice of whale watching off the Reunion Island. [ABSTRACT FROM AUTHOR]

Whale watching, including swim-with-whales activities, is developing at an enormous rate in Reunion Island. This is raising concerns about its impacts on breeding humpback whales and challenging the sustainability of the activity. In 2017, a dedicated-at-sea patrol team, "Quiétude," was created to observe, monitor, and raise awareness to the whale watchers in order to improve compliance with local guidelines. In this context, the team assessed whale watching in Reunion Island during two humpback whale breeding seasons in 2017 and 2018, between June and October. Sighted groups were mostly composed of mother/calf pairs spending most of their time resting near or at the surface. Whale-watching vessels were present in 85.1% of sightings, of which 68.4% were recreative. Swimwith-cetaceans activities were very frequently observed in 42% of sightings. Overall, compliance with the charter was as high as 68% of whale sightings with vessels/swimmers were in line with the recommendations. However, low compliance (32.8%) was observed with the specific recommendations of swim-with-cetaceans activities. Swimmers were reported in surface active groups displaying agonistic behaviors, which poses evident human safety concerns. Humpback whale resting behaviors were disrupted significantly by whale-watching activities. Humpback whales tended to avoid vessels and swimmers, especially when their behavior was intrusive or not compliant with the charter. Positive humpback whale responses were more likely to occur if the charter's recommendations were not breached. Our results highlight how a nonbinding regulation, with recommendations scrupulously pursued, allows for a reduction in whale-watching disturbances and supports a better tourism experience. Our results advocate for reinforcing tourism education, whale-watching adaptive management and regulations, specifically for vulnerable groups with a calf, and for the swim-with-whales activities. This could be efficiently achieved by engaging all stakeholders and the permanent team on the field "Quietude" to enhance dissemination of best practices and sensitivity around sustainable values of implemented regulations. [ABSTRACT FROM AUTHOR]

The article focuses on marine wildlife tourism and safety. Topics include the interactions with coastal and marine wildlife in natural settings have increased significantly, the interactions may be facilitated by commercial operators, deliberately sought by individuals and private groups, or incidental during other marine-based activities, and the focal species range from marine mammals to reptiles, birds, elasmobranch and other fish species, and more.