Multi-objective Optimisation of Marine Propellers

AbstractReal world problems have usually multiple objectives. These objective functions are of- ten in conflict, making them highly challenging in terms of determining optimal solutions and analysing solutions obtained. In this work Multi-objective Particle Swarm Optimisation (MOPSO) is employed to optimise the shape of marine propellers for the first time. The two objectives identified are maximising efficiency and minimising cavitation. Several experiments are undertaken to observe and analyse the impacts of structural parameters (shape and number of blades) and operating conditions (RPM) on both objective. The paper also investigates the negative effects of uncertainties in parameters and operating conditions on efficiency and cavitation. Firstly, the results showed that MOPSO is able to find a very accurate and uniformly distributed approximation of the true Pareto optimal front. The analysis of the results also shows that a propeller with 5 or 6 blades operating between 180 and 190 RPM results in the best trade-offs for efficiency and cavitation. Secondly, the simulation results show the significant negative impacts of uncertainties on both objectives

The Application of PSO in Structural Damage Detection: An Analysis of the Previously Released Publications (2005–2020)

The structural health monitoring (SHM) approach plays a key role not only in structural engineering but also in other various engineering disciplines by evaluating the safety and performance monitoring of the structures. The structural damage detection methods could be regarded as the core of SHM strategies. That is because the early detection of the damages and measures to be taken to repair and replace the damaged members with healthy ones could lead to economic advantages and would prevent human disasters. The optimization-based methods are one of the most popular techniques for damage detection. Using these methods, an objective function is minimized by an optimization algorithm during an iterative procedure. The performance of optimization algorithms has a significant impact on the accuracy of damage identification methodology. Hence, a wide variety of algorithms are employed to address optimization-based damage detection problems. Among different algorithms, the particle swarm optimization (PSO) approach has been of the most popular ones. PSO was initially proposed by Kennedy and Eberhart in 1995, and different variants were developed to improve its performance. This work investigates the objectives, methodologies, and results obtained by over 50 studies (2005-2020) in the context of the structural damage detection using PSO and its variants. Then, several important open research questions are highlighted. The paper also provides insights on the frequently used methodologies based on PSO, the computational time, and the accuracy of the existing methodologies

A review of the application of the simulated annealing algorithm in structural health monitoring (1995-2021)

In recent years, many innovative optimization algorithms have been developed. These algorithms have been employed to solve structural damage detection problems as an inverse solution. However, traditional optimization methods such as particle swarm optimization, simulated annealing (SA), and genetic algorithm are constantly employed to detect damages in the structures. This paper reviews the application of SA in different disciplines of structural health monitoring, such as damage detection, finite element model updating, optimal sensor placement, and system identification. The methodologies, objectives, and results of publications conducted between 1995 and 2021 are analyzed. This paper also provides an in-depth discussion of different open questions and research directions in this area

Trust aware crowd associated network-based approach for optimal waste management in smart cities

This is an accepted manuscript of an article published by CRC Press (Taylor & Francis) in Security and Organization within IoT and Smart Cities (in press), available online: https://www.routledge.com/Security-and-Organization-within-IoT-and-Smart-Cities/Ghafoor-Curran-Kong-Sadiq/p/book/9780367893330 The accepted version of the publication may differ from the final published version.Waste management has been a serious issue in urban areas due to the population growth. An appropriate solid waste management system is needed to improve the cleanliness of the environment. On the other hand, the rapid growth of the wide adoption of the Internet of Things (IoT) within the context of smart cities has motivated numerous number of studies investigating new solutions that could be helpful in mitigating and solving the waste management issue. Despite the existence of such methods have been introduced and used in managing waste’s location, volume and the optimal path for collection, yet these IoT based technologies are vulnerable to misinformation kinds of cyber attack. Consequently these types of attacks will yield crucial impact on the decided collection path and the frequency of garbage trucks visiting the fake reported waste points, which obviously costs money and time. Hence, this chapter proposes a trusted crowd associated network architecture that uses a group of components to monitor waste and provide optimum collection route for the garbage truck. Netlogo a multi-agent platform has been used to simulate a real time monitoring on waste management as a proof of concept. Our proposed approach measures the waste level data then updates and records them continuously. An optimal route will then be provided to the garbage truck for the optimal waste’s collection once a certain number of bins have reached a predefined threshold (combination of weight and height values). Three simulation scenarios are defined, implemented, and their results have been validated. The performance measure shows that our proposed solution could provide an aid waste management companies in reducing cost and time in the waste collection process, which supports the integration plans of IoT technology within smart cities

Minimum energy transmission forest-based Geocast in software-defined wireless sensor networks

© 2021 The Authors. Published by Wiley. This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website: https://doi.org/10.1002/ett.4253Wireless Sensor Networks (WSNs)-based geographic addressing and routing have many potential applications. Geocast protocols should be made energy efficient to increase the lifetime of nodes and packet delivery ratio. This technique will increase the number of live nodes, reduce message costs, and enhance network throughput. All geocast protocols in the literature of WSN apply mostly restricted flooding and perimeter flooding, which is why still the redundancy they produce significantly high message transmission costs and unnecessarily eats up immense energy in nodes. Moreover, perimeter flooding cannot succeed in the presence of holes. The present article models wireless sensor networks with software-defined constructs where the network area is divided into some zones. Energy-efficient transmission tree(s) are constructed in the geocast area to organize the flow of data packets and their links. Therefore, redundancy in the transmission is eliminated while maintaining network throughput as good as regular flooding. This proposed technique significantly reduces energy cost and improves nodes' lifetime to function for higher time duration and produce a higher data packet delivery ratio. To the best of the author's knowledge, this is the first work on geocast in SD-WSNs

A New Hybrid PSOGSA Algorithm for Function Optimization

Abstract-In this paper, a new hybrid population-based algorithm (PSOGSA) is proposed with the combination of Particle Swarm Optimization (PSO) and Gravitational Search Algorithm (GSA). The main idea is to integrate the ability of exploitation in PSO with the ability of exploration in GSA to synthesize both algorithms' strength. Some benchmark test functions are used to compare the hybrid algorithm with both the standard PSO and GSA algorithms in evolving best solution. The results show the hybrid algorithm possesses a better capability to escape from local optimums with faster convergence than the standard PSO and GSA

Transmission power adaption scheme for improving IoV awareness exploiting: evaluation weighted matrix based on piggybacked information

This is an accepted manuscript of an article published by Elsevier in Computer Networks on 04/06/2018, available online: https://doi.org/10.1016/j.comnet.2018.03.019 The accepted version of the publication may differ from the final published version.© 2018 Elsevier B.V. As part of the new era the Internet of Things, an evolved form of Vehicle Ad-hoc Networks has recently emerged as the Internet of Vehicles (IoV). IoV has obtained a lot of attention among smart vehicle manufactures and illustrations due to its promising potential, but there are still some problems and challenges that need to be addressed. Transmission error occurs when an emergency message is disseminated to provide traffic awareness, and vehicles have to increase their channel transmission power to ensure further coverage and mitigate possible accidents. This might cause channel congestion and unnecessary power consumption due to an inaccurate transmission power setup. A promising solution could be achieved via periodically and predictively evaluating channel and GEO information that is transmitted over piggybacked beacons. Thus, in this paper we propose a Transmission Power Adaptation (TPA) scheme for obtaining better power tuning, which senses and examines the probability of channel congestion. Afterwards, it proactively predicts upcoming channel statuses using developed evaluation-weighted matrix, which observes correlations between coefficients of variance for estimated metrics. Considering beacon transmission error rate, crowding inter-vehicle distance, and channel delay, the matrix is periodically constructed and proavtively weighted for each metric based on a predefined threshold value. Eventually, predicted channel status is used as an indicator to adjust transmission power. This leads to decreased channel congestion and better awareness in IoV. The performance of the proposed TPA scheme is evaluated using OMNeT++ simulation tools. The simulation results show that our proposed TPA scheme performs better than existing method in terms of overall throughput, average beacon congestion rate, beacon recipient rate probabilities, channel-busy time, transmission power over distance, and accident probabilities.Published versio

Evolutionary mating algorithm

This paper proposes a new evolutionary algorithm namely Evolutionary Mating Algorithm (EMA) to solve constrained optimization problems. The algorithm is based on the adoption of random mating concept from Hardy–Weinberg equilibrium and crossover index in order to produce new offspring. In this algorithm, effect of the environmental factor (i.e. the presence of predator) has also been considered and treated as an exploratory mechanism. The EMA is initially tested on the 23 benchmark functions to analyze its effectiveness in finding optimal solutions for different search spaces. It is then applied to Optimal Power Flow (OPF) problems with the incorporation of Flexible AC Transmission Systems (FACTS) devices and stochastic wind power generation. The extensive comparative studies with other algorithms demonstrate that EMA provides better results and can be used in solving real optimization problems from various fields