107 research outputs found
Unknown area exploration for robots with energy constraints using a modified Butterfly Optimization Algorithm
Butterfly Optimization Algorithm (BOA) is a recent metaheuristic that has
been used in several optimization problems. In this paper, we propose a new
version of the algorithm (xBOA) based on the crossover operator and compare its
results to the original BOA and 3 other variants recently introduced in the
literature. We also proposed a framework for solving the unknown area
exploration problem with energy constraints using metaheuristics in both
single- and multi-robot scenarios. This framework allowed us to benchmark the
performances of different metaheuristics for the robotics exploration problem.
We conducted several experiments to validate this framework and used it to
compare the effectiveness of xBOA with wellknown metaheuristics used in the
literature through 5 evaluation criteria. Although BOA and xBOA are not optimal
in all these criteria, we found that BOA can be a good alternative to many
metaheuristics in terms of the exploration time, while xBOA is more robust to
local optima; has better fitness convergence; and achieves better exploration
rates than the original BOA and its other variants
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
A Novel Path Planning Optimization Algorithm for Semi-Autonomous UAV in Bird Repellent Systems Based in Particle Swarm Optimization
Bird damage to fruit crops causes significant monetary losses to farmers annually. The
application of traditional bird repelling methods such as bird cannons and tree netting
became inefficient in the long run, keeping high maintenance and reduced mobility. Due to
their versatility, Unmanned Aerial Vehicles (UAVs) can be beneficial to solve this problem.
However, due to their low battery capacity that equals low flight duration, it is necessary to
evolve path planning optimization.
A path planning optimization algorithm of UAVs based on Particle Swarm Optimization
(PSO) is presented in this dissertation. This technique was used due to the need for an easy
implementation optimization algorithm to start the initial tests. The PSO algorithm is
simple and has few control parameters while maintaining a good performance. This path
planning optimization algorithm aims to manage the drone's distance and flight time,
applying optimization and randomness techniques to overcome the disadvantages of the
traditional systems. The proposed algorithm's performance was tested in three study cases:
two of them in simulation to test the variation of each parameter and one in the field to test
the influence on battery management and height influence. All cases were tested in the three
possible situations: same incidence rate, different rates, and different rates with no bird
damage to fruit crops.
The proposed algorithm presents promising results with an outstanding reduced average
error in the total distance for the path planning obtained and low execution time. However,
it is necessary to point out that the path planning optimization algorithm may have difficulty
finding a suitable solution if there is a bad ratio between the total distance for path planning
and points of interest. The field tests were also essential to understand the algorithm's
behavior of the path planning algorithm in the UAV, showing that there is less energy
discharged with fewer points of interest, but that do not correlates with the flight time. Also,
there is no association between the maximum horizontal speed and the flight time, which
means that the function to calculate the total distance for path planning needs to be
adjusted.Anualmente, os danos causados pelas aves em pomares criam perdas monetárias
significativas aos agricultores. A aplicação de métodos tradicionais de dispersão de aves,
como canhões repelentes de aves e redes nas árvores, torna-se ineficiente a longo prazo,
sendo ainda de alta manutenção e de mobilidade reduzida. Devido à sua versatilidade, os
VeÃculos Aéreos Não Tripulados (VANT) podem ser benéficos para resolver este problema.
No entanto, devido à baixa capacidade das suas baterias, que se traduz num baixo tempo de
voo, é necessário otimizar o planeamento dos caminhos.
Nesta dissertação, é apresentado um algoritmo de otimização para planeamento de
caminhos para VANT baseado no Particle Swarm Optimization (PSO). Para se iniciarem os
primeiros testes do algoritmo proposto, a técnica utilizada foi a supracitada devido Ã
necessidade de um algoritmo de otimização fácil de implementar. O algoritmo PSO é
simples e possuà poucos parâmetros de controlo, mantendo um bom desempenho. Este
algoritmo de otimização de planeamento de caminhos propõe-se a gerir a distância e o
tempo de voo do drone, aplicando técnicas de otimização e de aleatoriedade para superar a
sua desvantagem relativamente aos sistemas tradicionais. O desempenho do algoritmo de
planeamento de caminhos foi testado em três casos de estudo: dois deles em simulação para
testar a variação de cada parâmetro e outro em campo para testar a capacidade da bateria.
Todos os casos foram testados nas três situações possÃveis: mesma taxa de incidência, taxas
diferentes e taxas diferentes sem danos de aves.
Os resultados apresentados pelo algoritmo proposto demonstram um erro médio muto
reduzido na distância total para o planeamento de caminhos obtido e baixo tempo de
execução. Porém, é necessário destacar que o algoritmo pode ter dificuldade em encontrar
uma solução adequada se houver uma má relação entre a distância total para o planeamento
de caminhos e os pontos de interesse. Os testes de campo também foram essenciais para
entender o comportamento do algoritmo na prática, mostrando que há menos energia
consumida com menos pontos de interesse, sendo que este parâmetro não se correlaciona
com o tempo de voo. Além disso, não há associação entre a velocidade horizontal máxima e
o tempo da missão, o que significa que a função de cálculo da distância total para o
planeamento de caminhos requer ser ajustada
Differential Evolution in Wireless Communications: A Review
Differential Evolution (DE) is an evolutionary computational
method inspired by the biological processes of evolution and mutation. DE has
been applied in numerous scientific fields. The paper presents a literature review
of DE and its application in wireless communication. The detailed history,
characteristics, strengths, variants and weaknesses of DE were presented. Seven
broad areas were identified as different domains of application of DE in wireless
communications. It was observed that coverage area maximisation and energy
consumption minimisation are the two major areas where DE is applied.
Others areas are quality of service, updating mechanism where candidate positions
learn from a large diversified search region, security and related field applications.
Problems in wireless communications are often modelled as multiobjective
optimisation which can easily be tackled by the use of DE or hybrid of
DE with other algorithms. Different research areas can be explored and DE will
continue to be utilized in this contex
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