Behavior Of Humpback Whales Research Articles

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

AbstractVertical take‐off and landing (VTOL) unmanned aerial vehicles (UAVs) are becoming invaluable data collection platforms for cetacean research. In particular, multi‐rotors can be used to measure whales and investigate their behavior. Moreover, VTOL UAVs are increasingly accessible for recreational and commercial pilots, and close encounters with whales are widely documented. Unfortunately, quantitative assessments of potential disturbance for the targeted animals are not yet published and guidelines for responsible use of UAVs around cetaceans are still under development. We conducted VTOL UAV surveys on humpback whales in Vava'u, Kingdom of Tonga. Interestingly, whale behaviors such as socializing and nurturing were not detected by trained observers on board the research vessel, but were evident from the UAV. Nevertheless, no significant differences were detected in diving and swim parameters between absence and presence of UAV flying at 30 m altitude. These results suggest that VTOL UAVs can be a noninvasive tool to gather morphometric and behavioral data on baleen whales. However, further research is necessary to establish whether applications that require flight altitudes lower than 30 m and targeting different species may elicit behavioral responses.

A Giant’s Dance: Underwater Social and Vocal Behavior of Humpback Whales (Megaptera novaeangliae) Recorded on the Northern Coast of Ecuador

A Giant’s Dance: Underwater Social and Vocal Behavior of Humpback Whales (Megaptera novaeangliae) Recorded on the Northern Coast of Ecuador

New binary whale optimization algorithm for discrete optimization problems

ABSTRACTThe whale optimization algorithm (WOA) is an intelligence-based technique that simulates the hunting behaviour of humpback whales in nature. In this article, an adaptation of the original version of the WOA is made for handling binary optimization problems. For this purpose, two transfer functions (S-shaped and V-shaped) are presented to map a continuous search space to a binary one. To illustrate the functionality and performance of the proposed binary whale optimization algorithm (bWOA), its results when applied on twenty-two benchmark functions, three engineering optimization problems and a real-world travelling salesman problem are found. Furthermore, the proposed bWOA is compared with five well-known metaheuristic algorithms. The experimental results show its superiority in comparison with other state-of-the-art metaheuristics in terms of accuracy and speed. Finally, Wilcoxon's rank-sum non-parametric statistical test is carried out at the 5% significance level to judge whether the results of the proposed algorithm differ from those of the other comparison algorithms in a statistically significant way.

Laplacian whale optimization algorithm

Whale optimization algorithm is a new member of nature inspired optimization algorithm which is inspired from foraging behaviour of humpback whales. Similar to other heuristic algorithms, Whale optimization algorithm suffers with immature convergence and stagnation problems while solving optimization problems. In this paper, Whale optimization algorithm is hybridized with Laplace Crossover operator and a new algorithm, Laplacian whale optimization algorithm (LXWOA), has been proposed. It has been used to solve a set of 23 classical benchmark functions which consists of scalable unimodal functions, scalable multimodal functions and low dimensional multimodal functions and the results are compared with original whale optimization algorithm, particle swarm optimization, differential evolution, gravitational search algorithm and Laplacian gravitational search algorithm. In this paper, LXWOA and WOA have also been used to solve the problem of extraction of compounds from gardenia.

Multi-strategy boosted mutative whale-inspired optimization approaches

• A multi-strategy boosted mutative WOA is proposed for global optimization. • A chaotic initialization strategy, a chaotic local search and Gaussian mutation are incorporated into WOA. • The extensive results on benchmark problems and three classical engineering design problems reveal the excellent performance. This paper presents an improved Whale Optimization Algorithm (WOA) for global optimization. WOA is a recently introduced meta-heuristic algorithm mimicking the hunting behavior of humpback whales. Owing to its simplicity in exploratory and exploitative operators and the satisfactory efficacy, this algorithm has found its place among the well-established population-based approach utilized in many engineering and science areas. However, this method is easy to fall into local optimum when dealing with some optimization cases. In order to further enhance its exploratory and exploitative performance, three strategies are incorporated into the original method to keep a better balance between exploitation and exploration tendencies. First, the chaotic initialization phase is introduced into the optimizer to initiate the swarm of chaos-triggered whales. Then, Gaussian mutation is employed to intensify the diversity level of the evolving population. At last, a chaotic local search with a ‘shrinking’ strategy is used to enhance the exploitative leanings of the basic optimizer. In order to verify the effectiveness of the improved WOA, it is compared to four meta-heuristic and state-of-the-art evolutionary algorithms on representative benchmark functions. Trial results and simulations reveal that not only the proposed improved WOA is significantly better than those basic algorithms including original WOA but also it is superior to compared state-of-the-art approaches. Moreover, the proposed algorithm is successfully applied to realize three constrained engineering test cases, which the results suggest that the improved WOA can effectively deal with the constrained functions as well.

Multi-objective whale optimization based minimization of loss, maximization of voltage stability considering cost of DG for optimal sizing and placement of DG

Huge need in electricity causes placement of Distribution Generation (DG)s like Photovoltaics (PV) systems in distribution side for enhancing the loadability by improving the voltage stability and minimization of loss with minimum cost. Many optimal placements of DG have done in focus of minimum loss and improving voltage profile. This Whale optimization is a new optimization technique framed with mathematics of spiral bubble-net feeding behavior of humpback whales for solving a power system multi-objective problem considering cost of the power tariff and DG. Here main objectives are minimizing loss and cost with maximization of voltage stability index. IEEE 69 power system data is used for solution of the proposed method.

WOA-Miner: Classification Rule Discovery Using Whale Optimization Algorithm

This paper proposes a rule discovery tool for classification by using whale optimization algorithm that simulates the foraging behavior of humpback whales. Rule extraction is based on the optimization of randomly selected attributes according to rule fitness value. Algorithm were implemented and tested the most known 13 datasets and the results were compared with other known data mining algorithms including Decision Tree, Naïve Bayes, J48, JRip, Artificial Bee Colony and Ant Colony Optimization. The obtained results showed that whale optimization algorithm proved an appropriate candidate for classification processes.

A hyper-heuristic for improving the initial population of whale optimization algorithm

This paper improves the performance of the recently-proposed Whale Optimization Algorithm (WOA). WOA is a meta-heuristic that simulates the foraging behavior of humpback whales. There are several improvements in the literature for this algorithm of which chaotic maps and Opposition-Based Learning (OBL) are proved to be the most effective. In the former method, however, there are many chaotic maps that make it difficult to choose the best one for a given optimization algorithm. In the latter method, OBL should be applied to a portion of solutions in the population, which is normally obtained manually, which is time-consuming. This work proposed a hyper-heuristic to alleviate these drawbacks by automatically choosing a chaotic map and a portion of the population using the Differential Evolution (DE) algorithm. The proposed algorithm, which called DEWCO, has high ability to improve the exploration and local optima avoidance of WOA. In order to investigate the performance of the proposed DEWCO algorithm, several experiments are conducted on 35 standard CEC2005 functions and using seven algorithms. The experimental results show the superior performance of the proposed DEWCO algorithm to determine the optimal solutions of the test function problems.

IWOA: An improved whale optimization algorithm for optimization problems

Abstract 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. WOA suffers premature convergence that causes it to trap in local optima. In order to overcome this limitation of WOA, in this paper WOA is hybridized with differential evolution (DE) which has good exploration ability for function optimization problems. The proposed method is named Improved WOA (IWOA). The proposed method, combines exploitation of WOA with exploration of DE and therefore provides a promising candidate solution. In addition, IWOA+ is presented in this paper which is an extended form of IWOA. IWOA+ utilizes re-initialization and adaptive parameter which controls the whole search process to obtain better solutions. IWOA and IWOA+ are validated on a set of 25 benchmark functions, and they are compared with PSO, DE, BBO, DE/BBO, PSO/GSA, SCA, MFO and WOA. Furthermore, the effects of dimensionality and population size on the performance of our proposed algorithms are studied. The results demonstrate that IWOA and IWOA+ outperform the other algorithms in terms of quality of the final solution and convergence rate. Highlights The exploration ability of WOA is improved via hybridizing it with DE's mutation. A new adaptive strategy is utilized for balancing the exploration and exploitation abilities. Re-initialization is used to increase the diversity of population. Two improvements are presented for WOA through balancing its exploration and exploitation. The results show that the proposed algorithms can improve the performance of WOA significantly.

Leave or stay? Video-logger revealed foraging efficiency of humpback whales under temporal change in prey density.

Central place foraging theory (CPF) has been used to predict the optimal patch residence time for air-breathing marine predators in response to patch quality. Humpback whales (Megaptera novaeangliae) forage on densely aggregated prey, which may induce drastic change in prey density in a single feeding event. Thus, the decision whether to leave or stay after each feeding event in a single dive in response to this drastic change, should have a significant effect on prey exploitation efficiency. However, whether humpback whales show adaptive behavior in response to the diminishing prey density in a single dive has been technically difficult to test. Here, we studied the foraging behavior of humpback whales in response to change in prey density in a single dive and calculated the efficiency of each foraging dive using a model based on CPF approach. Using animal-borne accelerometers and video loggers attached to whales, foraging behavior and change in relative prey density in front of the whales were successfully quantified. Results showed diminishing rate of energy intake in consecutive feeding events, and humpback whales efficiently fed by bringing the rate of energy intake close to maximum in a single dive cycle. This video-based method also enabled us to detect the presence of other animals around the tagged whales, showing an interesting trend in behavioral changes where feeding duration was shorter when other animals were present. Our results have introduced a new potential to quantitatively investigate the effect of other animals on free-ranging top predators in the context of optimal foraging theory.

Whale Optimization Algorithm Based on Lamarckian Learning for Global Optimization Problems

Whale optimization algorithm (WOA) is a population-based meta-heuristic imitating the hunting behavior of humpback whales, which has been successfully applied to solve many real-world problems. Although WOA has a good convergence rate, it cannot achieve good results in finding the global optimal solution of high-dimensional complex optimization problems. The learning mechanism of Lamarckian evolutionism has the advantages of speeding up and strengthening local search. Through this learning mechanism, solutions with certain conditions can acquire higher adaptability with a higher probability by active learning. To enhance the global convergence speed and get better performance, this paper presents a WOA based on Lamarckian learning (WOALam) for solving high-dimensional function optimization problems. First, the population is initialized by good point set theory so that individuals can be evenly distributed in the solution space. Second, the upper confidence bound algorithm is used to calculate the development potential of the individual. Finally, based on the evolutionary theory of Lamarck, individuals with more development potentials are selected to perform the local enhanced search to improve the performance of the algorithm. The WOALam was compared with six variants of WOA on 44 benchmark functions. The experiments proved that the proposed algorithm can balance the global exploring ability and the exploiting ability well. It could obtain better results with fewer iterations and had good convergence speed and accuracy.

Optimal Tuning of SVC-PI Controller using Whale Optimization Algorithm for Angle Stability Improvement

This paper proposed a new swarm-based optimization technique for tuning conventional proportional-integral (PI) controller parameters of a static var compensator (SVC) which controls a synchronous generator in a single machine infinite bus (SMIB) system. As one of the Flexible Alternating Current Transmission Systems (FACTS) devices, SVC is designed and implemented to improve the damping of a synchronous generator. In this study, two parameters of PI controller namely proportional gain, K<sub>P</sub> and integral gain, K<sub>I</sub> are tuned with a new optimization method called Whale Optimization Algorithm (WOA). This technique mimics the social behavior of humpback whales which is characterized by their bubble-net hunting strategy in order to enhance the quality of the solution. Validation with respect to damping ratio and eigenvalues determination confirmed that the proposed technique is more efficient than Evolutionary Programming (EP) and Artificial Immune System (AIS) in improving the angle stability of the system. Comparison between WOA, EP and AIS optimization techniques showed that the proposed computation approach gives better solution and faster computation time.

Change in singing behavior of humpback whales caused by shipping noise.

Reactions of singing behavior of individual humpback whales (Megaptera novaeangliae) to a specific shipping noise were examined. Two autonomous recorders separated by 3.0 km were used for the acoustic monitoring of each individual song sequence. A passenger-cargo liner was operated once per day, and other large ship noise was excluded given the remote location of the Ogasawara Islands, 1000 km south of Tokyo. In total, locations of between 26 and 27 singers were measured acoustically using time arrival difference at both stereo recorders on the ship presence and absence days, respectively. Source level of the ship (157 dB rms re 1μPa) was measured separately in deep water. Fewer whales sang nearby, within 500 m, of the shipping lane. Humpback whales reduced sound production after the ship passed, when the minimum distance to the whale from the ship trajectory was 1200 m. In the Ogasawara water, humpback whales seemed to stop singing temporarily rather than modifying sound characteristics of their song such as through frequency shifting or source level elevation. This could be a cost effective adaptation because the propagation loss at 500 m from the sound source is as high as 54 dB. The focal ship was 500 m away within several minutes. Responses may differ where ship traffic is heavy, because avoiding an approaching ship may be difficult when many sound sources exist.

Binary whale optimization algorithm and its application to unit commitment problem

Whale optimization algorithm is a novel metaheuristic algorithm that imitates the social behavior of humpback whales. In this algorithm, the bubble-net hunting strategy of humpback whales is exploited. However, this algorithm, in its present form, is appropriate for continuous problems. To make it applicable to discrete problems, a binary version of this algorithm is being proposed in this paper. In the proposed approach, the solutions are binarized and sigmoidal transfer function is utilized to update the position of whales. The performance of the proposed algorithm is evaluated on 29 benchmark functions. Furthermore, unpaired t test is carried out to illustrate its statistical significance. The experimental results depict that the proposed algorithm outperforms others in respect of benchmark test functions. The proposed approach is applied on electrical engineering problem, a real-life application, named as “unit commitment”. The proposed approach uses the priority list to handle spinning reserve constraints and search mechanism to handle minimum up/down time constraints. It is tested on standard IEEE systems consisting of 4, 10, 20, 40, 80, and 100 units and on IEEE 118-bus system and Taiwan 38-bus system as well. Experimental results reveal that the proposed approach is superior to other algorithms in terms of lower production cost.

The Sonar Model for Humpback Whale Song Revised.

Why do humpback whales sing? This paper considers the hypothesis that humpback whales may use song for long range sonar. Given the vocal and social behavior of humpback whales, in several cases it is not apparent how they monitor the movements of distant whales or prey concentrations. Unless distant animals produce sounds, humpback whales are unlikely to be aware of their presence or actions. Some field observations are strongly suggestive of the use of song as sonar. Humpback whales sometimes stop singing and then rapidly approach distant whales in cases where sound production by those whales is not apparent, and singers sometimes alternately sing and swim while attempting to intercept another whale that is swimming evasively. In the evolutionary development of modern cetaceans, perceptual mechanisms have shifted from reliance on visual scanning to the active generation and monitoring of echoes. It is hypothesized that as the size and distance of relevant events increased, humpback whales developed adaptive specializations for long-distance echolocation. Differences between use of songs by humpback whales and use of sonar by other echolocating species are discussed, as are similarities between bat echolocation and singing by humpback whales. Singing humpback whales are known to emit sounds intense enough to generate echoes at long ranges, and to flexibly control the timing and qualities of produced sounds. The major problem for the hypothesis is the lack of recordings of echoes from other whales arriving at singers immediately before they initiate actions related to those whales. An earlier model of echoic processing by singing humpback whales is here revised to incorporate recent discoveries. According to the revised model, both direct echoes from targets and modulations in song-generated reverberation can provide singers with information that can help them make decisions about future actions related to mating, traveling, and foraging. The model identifies acoustic and structural features produced by singing humpback whales that may facilitate a singer’s ability to interpret changes in echoic scenes and suggests that interactive signal coordination by singing whales may help them to avoid mutual interference. Specific, testable predictions of the model are presented.

Effects of whale watching on the activity budgets of humpback whales,Megaptera novaeangliae(Borowski, 1781), on a feeding ground

AbstractWhale watching can affect cetacean behaviour, and can in some cases lead to long‐term negative effects on survival and reproduction.The waters of Juneau (Alaska) represent a summer feeding ground for the Central North Pacific stock of humpback whales,Megaptera novaeangliae(Borowski, 1781). The recent dramatic expansion of the local whale‐watching industry has raised concerns over the potential negative impact of such activity on the whales.The aim of this study was to understand the impact of whale‐watching vessels on humpback whale behaviour in Juneau. From June to September 2015, land‐based observations were conducted to record whale behaviour in the presence and absence of vessels.Markov chains and model simulations showed that the presence of vessels was not associated with a reduction in the time spent feeding. Whales exhibited more surface‐active behaviour and travelled less in the presence of vessels.The lack of an overt response of feeding whales to whale‐watching disturbance could result from the importance of the Southeast Alaska study region as a feeding ground, which could make the whales less apt to interrupt feeding under disturbance; however, The high proportion of travelling observed may indicate that the study area represents a travelling corridor between other, localized feeding areas. The increase in surface‐active behaviour may represent a response to vessel presence that could potentially lead to energetic consequences.The large number of whale‐watching vessels operating could lead to the increased tolerance of whales towards whale‐watching activities. The lack of responses in this study may suggest that the reactions of whales are context specific, depending on their previous experience with vessels and their foraging behaviour, for example. Subtler negative effects could still occur, however, and a prolonged monitoring programme is required to assess less overt behavioural responses of whales over time.

A Novel Whale Optimization Algorithm for Cryptanalysis in Merkle-Hellman Cryptosystem

With the advance of the communication technology and the massive flow of information across the internet, it is becoming urgent to keep the confidentiality of the transmitted information. Using the internet has been extended to several fields such as e-mail, e-commerce, e-learning, health and medicine, shopping, and so on. Cryptography is the study of different techniques for securing the communication between the sender and the receiver. One of the most known cryptosystems is Merkle–Hellman Knapsack Cryptosystem (MHKC). It is one of the earliest Public Key Cryptosystem (PKC) that is used to secure the messages between the sender and the receiver. Developing a powerful cryptosystem comes after studying the fragility points of the current cryptosystems. The Whale Optimization Algorithm (WOA) is one of the most recent nature-inspired meta-heuristic optimization algorithms, which simulates the social behavior of humpback whales. WOA has validated excellent performance in solving the continuous problems and the engineering optimization problems. This paper introduces a novel Modified version of WOA (MWOA) for cryptanalysis of MHKC. The sigmoid function is used to map the continuous values into discrete one. A penalty function is added to the evaluation function to deal with the infeasible solutions. The mutation operation is employed for improving the solutions. The results show that MWOA is more effective and robust than other algorithms in the literature.

Identifying behavioral states and habitat use of acoustically tracked humpback whales in Hawaii

AbstractAlthough humpback whales have been well‐studied on their Hawaiian breeding grounds, it is difficult to track individual animals over long distances without tags, particularly when they move offshore. Here, singing humpback whales were localized in three dimensions on the Pacific Missile Range Facility off Kauai, Hawaii, located between 20 km and 80 km offshore, from January 2011 through June 2014. Detailed behavioral analyses were conducted on the resulting tracks. One hundred and eight individual tracks were identified and metrics of these tracks were examined. Using these metrics, the tracks were classified into four behavior categories, described herein as Directed Travel, Repeated Stationary Dives, Mill, and tracks with Combinations of behavioral states. Some diel and seasonal patterns were identified, with Mill tracks occurring more at night than the other behaviors, Repeated Stationary Dive tracks occurring more during the day, and Directed Travel occurring only at the start and end of the breeding season. These results provide detailed insights into the movement of singing humpback whales, particularly in offshore waters where they may be migrating into or out of breeding grounds. This also contributes valuable information on the baseline behavior of humpback whales on a US Navy training range.

Optimal placement of TCSC and SVC for reactive power planning using Whale optimization algorithm

Abstract In the present work, Whale optimization algorithm (WOA), Differential evolution (DE), Grey wolf optimization (GWO), Quasi-opposition based Differential Evolution (QODE) and Quasi-opposition based Grey wolf optimization (QOGWO) algorithm has been applied for the solution of reactive power planning with FACTS devices i.e., Thyristor controlled series compensator (TCSC) and Static Var compensator (SVC). WOA is a recently developed nature-inspired meta-heuristic algorithm based on hunting behaviour of Humpback Whales; DE is a stochastic real-parameter optimization technique comprising of genetic parameters namely - mutation & cross-over; and GWO is a nature-inspired meta-heuristic algorithm based on hunting behaviour of Grey wolf. Standard IEEE 30 and IEEE 57 bus test system has been adopted for the testing purposes. Location of TCSC has been determined by the power flow analysis method and location of SVC has been determined by the voltage collapse proximity indication (VCPI) method. Further, WOA, GWO, DE, QODE and QOGWO algorithms have been applied to find the optimal setting of all control variables including TCSC, the series type and SVC, the shunt kind of FACTS device in the test system which minimizes active power loss and system operating cost while maintaining voltage profile within permissible limit. The superiority of the proposed WOA technique has been illustrated by comparing the results obtained with all other techniques discussed in the present problem. ANOVA test has also been conducted to show the statistical analysis between different techniques. The proposed approach shows lesser number of iterations which does not gets trapped in the local minima and offers promising convergence characteristics.

PSS Based Angle Stability Improvement Using Whale Optimization Approach

This paper introduced a new swarm based optimization technique for tuning Power System Stabilizer (PSS) that attached to a synchronous generator in a single machine infinite bus (SMIB) system. PSS which is installed with Lead-Lag (LL) controller is introduced to elevate the damping capability of the generator in the low frequency mode. For tuning PSS-LL parameters, a new technique called Whale Optimization Algorithm (WOA) is proposed. This method mimics the social behavior of humpback whales which is characterized by their bubble-net hunting strategy in order to enhance the quality of the solution. Based on eigenvalues and damping ratio results, it is confirmed that the proposed technique is more efficient than Particle Swarm Optimization (PSO) and Evolutionary Programming (EP) in improving the angle stability of the system. Comparison between WOA, PSO and EP optimization techniques showed that the proposed computation approach give better solution and faster computation time.