2D Swarm Meerkats Behavior Modelling

Animal behavior is the connection or link between the molecular and physiological aspects of biology and the ecological. Behavior is the bridge between organisms and environment also between the nervous system and the ecosystem. Besides that, behavior is generally the animal's "first line of defense" in response to environmental change. Therefore, careful observation of the behavior can provide us a great information. Behavior is one of the most important features of animal life. As a human, behavior plays a critical role in our lives. This is because behavior is the part of an organism that interacts with its environment. Many problems occur in human society are often related to the interaction between environment or genetics with behavior. The fields of socioecology and animal behavior deal with the issue of environment behavioral interactions at an accurate level and a proximate level. Therefore, social scientists are turning to animal behavior as a framework to interpret human society and to find out possible sources of societal problems. In this study, the foraging behavior of Meerkat will be studied. In this thesis, the foraging behavior of Meerkat will be studied and the parameters for simulation of Meerkats foraging behavior are designed. The designed parameters including the number of agents, number of group, range of perception and number of food. However, there are not much works done on Meerkats therefore, survey form is used in designing these 14 sets of parameters. Only the choices that have higher percentage is focused in designing the 14 sets of parameters for simulation. The performance of each 14 sets of simulation are compared based on the result obtained from the simulations such as the highest mean quality the simulation can achieve and the number of ticks required to reach the highest mean quality. The higher the mean quality the better the performance. The smaller the number of ticks required to reach the highest mean quality the better the performance

Energy-Based Acoustic Localization by Improved Elephant Herding Optimization

UIDB/EEA/50008/2020The present work proposes a new approach to address the energy-based acoustic localization problem. The proposed approach represents an improved version of evolutionary optimization based on Elephant Herding Optimization (EHO), where two major contributions are introduced. Firstly, instead of random initialization of elephant population, we exploit particularities of the problem at hand to develop an intelligent initialization scheme. More precisely, distance estimates obtained at each reference point are used to determine the regions in which a source is most likely to be located. Secondly, rather than letting elephants to simply wander around in their search for an update of the source location, we base their motion on a local search scheme which is found on a discrete gradient method. Such a methodology significantly accelerates the convergence of the proposed algorithm, and comes at a very low computational cost, since discretization allows us to avoid the actual gradient computations. Our simulation results show that, in terms of localization accuracy, the proposed approach significantly outperforms the standard EHO one for low noise settings and matches the performance of an existing enhanced version of EHO (EEHO). Nonetheless, the proposed scheme achieves this accuracy with significantly less number of function evaluations, which translates to greatly accelerated convergence in comparison with EHO and EEHO. Finally, it is also worth mentioning that the proposed methodology can be extended to any population-based metaheuristic method (it is not only restricted to EHO), which tackles the localization problem indirectly through distance measurements.publishersversionpublishe

Comprehensive Taxonomies of Nature- and Bio-inspired Optimization: Inspiration Versus Algorithmic Behavior, Critical Analysis Recommendations

In recent algorithmic family simulates different biological processes observed in Nature in order to efficiently address complex optimization problems. In the last years the number of bio-inspired optimization approaches in literature has grown considerably, reaching unprecedented levels that dark the future prospects of this field of research. This paper addresses this problem by proposing two comprehensive, principle-based taxonomies that allow researchers to organize existing and future algorithmic developments into well-defined categories, considering two different criteria: the source of inspiration and the behavior of each algorithm. Using these taxonomies we review more than three hundred publications dealing with nature- inspired and bio-inspired algorithms, and proposals falling within each of these categories are examined, leading to a critical summary of design trends and similarities between them, and the identification of the most similar classical algorithm for each reviewed paper. From our analysis we conclude that a poor relationship is often found between the natural inspiration of an algorithm and its behavior. Furthermore, similarities in terms of behavior between different algorithms are greater than what is claimed in their public disclosure: specifically, we show that more than one-third of the reviewed bio-inspired solvers are versions of classical algorithms. Grounded on the conclusions of our critical analysis, we give several recommendations and points of improvement for better methodological practices in this active and growing research field

Comprehensive Taxonomies of Nature- and Bio-inspired Optimization: Inspiration versus Algorithmic Behavior, Critical Analysis and Recommendations

In recent years, a great variety of nature- and bio-inspired algorithms has been reported in the literature. This algorithmic family simulates different biological processes observed in Nature in order to efficiently address complex optimization problems. In the last years the number of bio-inspired optimization approaches in literature has grown considerably, reaching unprecedented levels that dark the future prospects of this field of research. This paper addresses this problem by proposing two comprehensive, principle-based taxonomies that allow researchers to organize existing and future algorithmic developments into well-defined categories, considering two different criteria: the source of inspiration and the behavior of each algorithm. Using these taxonomies we review more than three hundred publications dealing with nature-inspired and bio-inspired algorithms, and proposals falling within each of these categories are examined, leading to a critical summary of design trends and similarities between them, and the identification of the most similar classical algorithm for each reviewed paper. From our analysis we conclude that a poor relationship is often found between the natural inspiration of an algorithm and its behavior. Furthermore, similarities in terms of behavior between different algorithms are greater than what is claimed in their public disclosure: specifically, we show that more than one-third of the reviewed bio-inspired solvers are versions of classical algorithms. Grounded on the conclusions of our critical analysis, we give several recommendations and points of improvement for better methodological practices in this active and growing research field.Comment: 76 pages, 6 figure

Bio-inspired optimization in integrated river basin management

Water resources worldwide are facing severe challenges in terms of quality and quantity. It is essential to conserve, manage, and optimize water resources and their quality through integrated water resources management (IWRM). IWRM is an interdisciplinary field that works on multiple levels to maximize the socio-economic and ecological benefits of water resources. Since this is directly influenced by the river’s ecological health, the point of interest should start at the basin-level. The main objective of this study is to evaluate the application of bio-inspired optimization techniques in integrated river basin management (IRBM). This study demonstrates the application of versatile, flexible and yet simple metaheuristic bio-inspired algorithms in IRBM. In a novel approach, bio-inspired optimization algorithms Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) are used to spatially distribute mitigation measures within a basin to reduce long-term annual mean total nitrogen (TN) concentration at the outlet of the basin. The Upper Fuhse river basin developed in the hydrological model, Hydrological Predictions for the Environment (HYPE), is used as a case study. ACO and PSO are coupled with the HYPE model to distribute a set of measures and compute the resulting TN reduction. The algorithms spatially distribute nine crop and subbasin-level mitigation measures under four categories. Both algorithms can successfully yield a discrete combination of measures to reduce long-term annual mean TN concentration. They achieved an 18.65% reduction, and their performance was on par with each other. This study has established the applicability of these bio-inspired optimization algorithms in successfully distributing the TN mitigation measures within the river basin. Stakeholder involvement is a crucial aspect of IRBM. It ensures that researchers and policymakers are aware of the ground reality through large amounts of information collected from the stakeholder. Including stakeholders in policy planning and decision-making legitimizes the decisions and eases their implementation. Therefore, a socio-hydrological framework is developed and tested in the Larqui river basin, Chile, based on a field survey to explore the conditions under which the farmers would implement or extend the width of vegetative filter strips (VFS) to prevent soil erosion. The framework consists of a behavioral, social model (extended Theory of Planned Behavior, TPB) and an agent-based model (developed in NetLogo) coupled with the results from the vegetative filter model (Vegetative Filter Strip Modeling System, VFSMOD-W). The results showed that the ABM corroborates with the survey results and the farmers are willing to extend the width of VFS as long as their utility stays positive. This framework can be used to develop tailor-made policies for river basins based on the conditions of the river basins and the stakeholders' requirements to motivate them to adopt sustainable practices. It is vital to assess whether the proposed management plans achieve the expected results for the river basin and if the stakeholders will accept and implement them. The assessment via simulation tools ensures effective implementation and realization of the target stipulated by the decision-makers. In this regard, this dissertation introduces the application of bio-inspired optimization techniques in the field of IRBM. The successful discrete combinatorial optimization in terms of the spatial distribution of mitigation measures by ACO and PSO and the novel socio-hydrological framework using ABM prove the forte and diverse applicability of bio-inspired optimization algorithms

Adaptive granularity learning distributed particle swarm optimization for large-scale optimization

Large-scale optimization has become a significant and challenging research topic in the evolutionary computation (EC) community. Although many improved EC algorithms have been proposed for large-scale optimization, the slow convergence in the huge search space and the trap into local optima among massive suboptima are still the challenges. Targeted to these two issues, this article proposes an adaptive granularity learning distributed particle swarm optimization (AGLDPSO) with the help of machine-learning techniques, including clustering analysis based on locality-sensitive hashing (LSH) and adaptive granularity control based on logistic regression (LR). In AGLDPSO, a master-slave multisubpopulation distributed model is adopted, where the entire population is divided into multiple subpopulations, and these subpopulations are co-evolved. Compared with other large-scale optimization algorithms with single population evolution or centralized mechanism, the multisubpopulation distributed co-evolution mechanism will fully exchange the evolutionary information among different subpopulations to further enhance the population diversity. Furthermore, we propose an adaptive granularity learning strategy (AGLS) based on LSH and LR. The AGLS is helpful to determine an appropriate subpopulation size to control the learning granularity of the distributed subpopulations in different evolutionary states to balance the exploration ability for escaping from massive suboptima and the exploitation ability for converging in the huge search space. The experimental results show that AGLDPSO performs better than or at least comparable with some other state-of-the-art large-scale optimization algorithms, even the winner of the competition on large-scale optimization, on all the 35 benchmark functions from both IEEE Congress on Evolutionary Computation (IEEE CEC2010) and IEEE CEC2013 large-scale optimization test suites

Wireless multimedia sensor networks, security and key management

Wireless Multimedia Sensor Networks (WMSNs) have emerged and shifted the focus from the typical scalar wireless sensor networks to networks with multimedia devices that are capable to retrieve video, audio, images, as well as scalar sensor data. WMSNs are able to deliver multimedia content due to the availability of inexpensive CMOS cameras and microphones coupled with the significant progress in distributed signal processing and multimedia source coding techniques. These mentioned characteristics, challenges, and requirements of designing WMSNs open many research issues and future research directions to develop protocols, algorithms, architectures, devices, and testbeds to maximize the network lifetime while satisfying the quality of service requirements of the various applications. In this thesis dissertation, we outline the design challenges of WMSNs and we give a comprehensive discussion of the proposed architectures and protocols for the different layers of the communication protocol stack for WMSNs along with their open research issues. Also, we conduct a comparison among the existing WMSN hardware and testbeds based on their specifications and features along with complete classification based on their functionalities and capabilities. In addition, we introduce our complete classification for content security and contextual privacy in WSNs. Our focus in this field, after conducting a complete survey in WMSNs and event privacy in sensor networks, and earning the necessary knowledge of programming sensor motes such as Micaz and Stargate and running simulation using NS2, is to design suitable protocols meet the challenging requirements of WMSNs targeting especially the routing and MAC layers, secure the wirelessly exchange of data against external attacks using proper security algorithms: key management and secure routing, defend the network from internal attacks by using a light-weight intrusion detection technique, protect the contextual information from being leaked to unauthorized parties by adapting an event unobservability scheme, and evaluate the performance efficiency and energy consumption of employing the security algorithms over WMSNs

Nonveridical biosemiotics and the Interface Theory of Perception: implications for perception-mediated selection

Recently, the relationship between evolutionary ecology and perceptual science has received renewed attention under perception-mediated selection, a mode of natural selection linking perceptual saliency, rather than veridicality, to fitness. The Interface Theory of Perception (ITP) has been especially prominent in claiming that an organism’s perceptual interface is populated by icons, which arise as a function of evolved, species-specific perceptual interfaces that produce approximations of organisms’ environments through fitness-tuned perceptions. According to perception-mediated selection, perception and behavior calibrate one another as organisms’ capacities to experience and know the objects and properties of their environments lead to responses highlighting certain environmental features selected for survival. We argue this occurs via the Umwelt/Umgebung distinction in ethology, demonstrating that organisms interact with their external environments (Umgebung) through constructed perceptual schema (Umwelt) that produce constrained representations of environmental objects and their properties. Following Peircean semiotics, we claim that ITP’s focus on icons as saliency-simplified markers corresponds to biosemiotics’ understanding of perceptual representations, which manifest as iconic (resembling objects), indexical (referring), or symbolic (arbitrary) modalities, which provide for organisms’ semiotic scaffolding. We argue that ITP provides the computational evidence for biosemiotics’ notion of iconicity, while biosemiotics provides explanation within ITP for how iconicity can build up into indices and symbols. The common contention of these separate frameworks that the process of perception tracks saliency rather than veridicality suggests that digital/dyadic perceptual strategies will be outcompeted by their semiotic/triadic counterparts. This carries implications for evolutionary theory as well as theories of cognition

The evolution of cooperation in an arid-zone bird: bet-hedging, plasticity and constraints

The evolution of cooperation represents a long-standing puzzle in evolutionary biology. From insects to mammals, this behaviour has evolved multiple times in separate lineages. Even though inclusive fitness theory provides a solid theoretical framework to understand the evolution of cooperation, there are still many research challenges in the evolutionary study of cooperation. First, most of the evidence for the effects of cooperation on the reproductive success of beneficiaries in vertebrate societies is based on correlational studies, which can be confounded by several environmental factors. Second, there are recent theoretical formulations to explain the evolution of cooperation that have not been empirically tested yet but could shed new light on the selective pressures that facilitate the evolution of cooperation. Third, we still have a poor understanding of the sources of among individual variation in cooperative behaviours. In particular, few studies have investigated whether the level of cooperation expressed by individuals is heritable and, therefore, could respond to natural selection. In this thesis, I combine nine years of life-history and behavioural information with field experiments and genomics to investigate (i) the routes for non-breeding individuals to acquire indirect fitness benefits and (ii) the sources of among-individual variation in cooperation in white-browed sparrow-weavers (Plocepasser mahali), an arid-zone cooperative breeder. After a general introduction to the subject of cooperative breeding, in Chapter 2 I test a novel hypothesis for the evolution of cooperation, the ‘altruistic bet-hedging’ hypothesis. There, I show that non-breeding helpers reduce variation in the reproductive success of breeders without affecting their arithmetic mean reproductive success. Furthermore, I show that this reproductive variance compression appears to arise because helpers specifically reduce unpredictable rainfall-induced variation in reproductive success, just as hypothesised by global comparative studies of the evolution of cooperative breeding in birds. Then, I investigate alternative routes through which helpers may gain indirect fitness benefits. Specifically, in Chapter 3 I investigate the effects of helpers on pre- and post-natal maternal investment in reproduction. The findings in Chapter 3 provide clear evidence for maternal plasticity in pre-natal investment in reproduction (egg volume) in response to the number of helpers. Moreover, the helper effect of increased pre-natal maternal investment is associated with a decrease in post-natal maternal investment. In Chapter 4, I test the philopatry hypothesis for the evolution of sex differences in cooperation within animal societies and find strong support for this hypothesis in white-browed sparrow-weavers. Furthermore, Chapter 4 highlights the need to consider both sex differences in direct fitness benefits and costs when trying to understand sex differences in cooperation. Finally, in Chapter 5 I investigate among-helper variation in cooperative generosity, finding consistent individual differences and providing evidence for heritable variation in this trait. To conclude, in Chapter 6 I discuss the implications of these results for our general understanding of the evolution of cooperation in animal societies and highlight methodological approaches for future empirical studies of cooperation in the wild.Centre for Environment, Fisheries and Aquaculture Scienc