Modelling foraging movements of diving predators : A theoretical study exploring the effect of heterogeneous landscapes on foraging efficiency

Peer reviewedPublisher PD

The movement of a forager: Strategies for the efficient use of resources

We study a simple model of a foraging animal that modifies the substrate on which it moves. This substrate provides its only resource, and the forager manage it by taking a limited portion at each visited site. The resource recovers its value after the visit following a relaxation law. We study different scenarios to analyze the efficiency of the managing strategy, corresponding to control the bite size. We observe the non trivial emergence of a home range, that is visited in a periodic way. The duration of the corresponding cycles and the transient until it emerges is affected by the bite size. Our results show that the most efficient use of the resource, measured as the balance between gathering and travelled distance, corresponds to foragers that take larger portions but without exhausting the resource. We also analyze the use of space determining the number of attractors of the dynamics, and we observe that it depends on the bite size and the recovery time of the resource

Space use by foragers consuming renewable resources

We study a simple model of a forager as a walk that modifies a relaxing substrate. Within it simplicity, this provides an insight on a number of relevant and non-intuitive facts. Even without memory of the good places to feed and no explicit cost of moving, we observe the emergence of a finite home range. We characterize the walks and the use of resources in several statistical ways, involving the behavior of the average used fraction of the system, the length of the cycles followed by the walkers, and the frequency of visits to plants. Preliminary results on population effects are explored by means of a system of two non directly interacting animals. Properties of the overlap of home ranges show the existence of a set of parameters that provides the best utilization of the shared resource

Optimal frequency control in microgrid system using fractional order PID controller using Krill Herd algorithm

This paper investigates the use of fractional order Proportional, Integral and Derivative (FOPID) controllers for the frequency and power regulation in a microgrid power system. The proposed microgrid system composes of renewable energy resources such as solar and wind generators, diesel engine generators as a secondary source to support the principle generators, and along with different energy storage devices like fuel cell, battery and flywheel. Due to the intermittent nature of integrated renewable energy like wind turbine and photovoltaic generators, which depend on the weather conditions and climate change this affects the microgrid stability by considered fluctuation in frequency and power deviations which can be improved using the selected controller. The fractional-order controller has five parameters in comparison with the classical PID controller, and that makes it more flexible and robust against the microgrid perturbation. The Fractional Order PID controller parameters are optimized using a new optimization technique called Krill Herd which selected as a suitable optimization method in comparison with other techniques like Particle Swarm Optimization. The results show better performance of this system using the fractional order PID controller-based Krill Herd algorithm by eliminates the fluctuations in frequency and power deviation in comparison with the classical PID controller. The obtained results are compared with the fractional order PID controller optimized using Particle Swarm Optimization. The proposed system is simulated under nominal conditions and using the disconnecting of storage devices like battery and Flywheel system in order to test the robustness of the proposed methods and the obtained results are compared.У статті досліджено використання регуляторів пропорційного, інтегрального та похідного дробового порядку (FOPID) для регулювання частоти та потужності в електромережі. Запропонована мікромережева система складається з поновлюваних джерел енергії, таких як сонячні та вітрогенератори, дизельних генераторів як вторинного джерела для підтримки основних генераторів, а також з різних пристроїв для накопичування енергії, таких як паливна батарея, акумулятор і маховик. Через переривчасту природу інтегрованої відновлювальної енергії, наприклад, вітрогенераторів та фотоелектричних генераторів, які залежать від погодних умов та зміни клімату, це впливає на стабільність мікромережі, враховуючи коливання частоти та відхилення потужності, які можна поліпшити за допомогою вибраного контролера. Контролер дробового порядку має п’ять параметрів порівняно з класичним PID-контролером, що робить його більш гнучким та надійним щодо збурень мікромережі. Параметри PID-контролера дробового порядку оптимізовані за допомогою нової методики оптимізації під назвою «зграя криля», яка обрана як підходящий метод оптимізації порівняно з іншими методами, такими як оптимізація методом рою частинок. Результати показують кращі показники роботи цієї системи за допомогою алгоритму «зграя криля», заснованого на PID-контролері дробового порядку, виключаючи коливання частоти та відхилення потужності порівняно з класичним PID-контролером. Отримані результати порівнюються з PID-контролером дробового порядку, оптимізованим за допомогою оптимізації методом рою частинок. Запропонована система моделюється в номінальному режимі роботи та використовує відключення накопичувальних пристроїв, таких як акумулятор та маховик, щоб перевірити надійність запропонованих методів та порівняти отримані результати

A Simple Iterative Model Accurately Captures Complex Trapline Formation by Bumblebees Across Spatial Scales and Flower Arrangements

PMCID: PMC3591286This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

A Review on the Application of Natural Computing in Environmental Informatics

Natural computing offers new opportunities to understand, model and analyze the complexity of the physical and human-created environment. This paper examines the application of natural computing in environmental informatics, by investigating related work in this research field. Various nature-inspired techniques are presented, which have been employed to solve different relevant problems. Advantages and disadvantages of these techniques are discussed, together with analysis of how natural computing is generally used in environmental research.Comment: Proc. of EnviroInfo 201

A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments

In recent years, due to the unnecessary wastage of electrical energy in residential buildings, the requirement of energy optimization and user comfort has gained vital importance. In the literature, various techniques have been proposed addressing the energy optimization problem. The goal of each technique was to maintain a balance between user comfort and energy requirements such that the user can achieve the desired comfort level with the minimum amount of energy consumption. Researchers have addressed the issue with the help of different optimization algorithms and variations in the parameters to reduce energy consumption. To the best of our knowledge, this problem is not solved yet due to its challenging nature. The gap in the literature is due to the advancements in the technology and drawbacks of the optimization algorithms and the introduction of different new optimization algorithms. Further, many newly proposed optimization algorithms which have produced better accuracy on the benchmark instances but have not been applied yet for the optimization of energy consumption in smart homes. In this paper, we have carried out a detailed literature review of the techniques used for the optimization of energy consumption and scheduling in smart homes. The detailed discussion has been carried out on different factors contributing towards thermal comfort, visual comfort, and air quality comfort. We have also reviewed the fog and edge computing techniques used in smart homes

Spatial memory shapes density dependence in population dynamics

Most population dynamics studies assume that individuals use space uniformly, and thus mix well spatially. In numerous species, however, individuals do not move randomly, but use spatial memory to visit renewable resource patches repeatedly. To understand the extent to which memorybased foraging movement may affect density-dependent population dynamics through its impact on competition, we developed a spatially explicit, individual-based movement model where reproduction and death are functions of foraging efficiency. We compared the dynamics of populations of with- and without-memory individuals. We showed that memory-based movement leads to a higher population size at equilibrium, to a higher depletion of the environment, to a marked discrepancy between the global (i.e. measured at the population level) and local (i.e. measured at the individual level) intensities of competition, and to a nonlinear density dependence. These results call for a deeper investigation of the impact of individual movement strategies and cognitive abilities on population dynamics

An independent framework for off-grid hybrid renewable energy design using Optimal Foraging Algorithm (OFA)

The rapidly increase in electrical energy demand from residential, commercial and industrial sectors is one of the major challenge in power system, especially in the current period of high oil prices, steadily reducing energy sources and increased concerns about environmental pollution. Renewable energy is considered as one of the solution to this increase in power demand. The conventional method of power system cannot meet the power demand for many reasons such as environmental effects, location of the consumer, price of fuel and others. This paper presents the design of an off-grid Hybrid Renewable Energy System (HRES) for electrification of a typical remote area. The designed hybrid system consists of three different configurations of PV/Battery, Wind/Battery and PV/Wind/Battery systems. The system components are modelled and the objective function is designed as a function of total annualized cost of the system subject to some constraints binding the decision variables. The total annual cost is formulated as a function of annual capital cost and annual maintenance cost of the system subject to some operational constraints. In order to determine the optimal number of the decision variables that would satisfy the load demand in the most cost effect manner, Optimal Foraging Optimization (OFA) algorithm was used. Finally, a simulation experiment shows that the total annual cost obtained by each algorithm for the PV/Battery system is $9,340.42 or N3,876,274.30,$9,446.77 or N3,920,409.55 and $10,076.34 or N4,181,681.1 for OFA, GA and PSO respectively. For the Wind/Battery configuration, the total annual cost obtained by OFA, GA and PSO are$17,508.20 or N7,265,903, $12,493.27 or N 5,184,707.05 and$16,535.93 or N6,862,410.95 respectively. Similarly, the PV/Wind/Battery configuration showed that the OFA, GA and PSO obtained an annualized cost of $15,926.07 or N6,609,319.05,$18,167.09 or N7,539,342.35 and $16,535.93 or N6,862,410,95 respectively. From the results obtained by OFA are compared with that of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithm. Results showed that all the algorithm can efficiently size the hybrid system with OFA obtaining the most economical design. Therefore, for economically and efficiently electrification of a remote area in Abuja using an off-grid hybrid renewable energy system, GA optimization algorithm is recommended for wind/Battery system and OFA optimization algorithm is recommended for PV/Wind/Battery system