19,776 research outputs found
Application of a new multi-agent Hybrid Co-evolution based Particle Swarm Optimisation methodology in ship design
In this paper, a multiple objective 'Hybrid Co-evolution based Particle Swarm Optimisation' methodology (HCPSO) is proposed. This methodology is able to handle multiple objective optimisation problems in the area of ship design, where the simultaneous optimisation of several conflicting objectives is considered. The proposed method is a hybrid technique that merges the features of co-evolution and Nash equilibrium with a ε-disturbance technique to eliminate the stagnation. The method also offers a way to identify an efficient set of Pareto (conflicting) designs and to select a preferred solution amongst these designs. The combination of co-evolution approach and Nash-optima contributes to HCPSO by utilising faster search and evolution characteristics. The design search is performed within a multi-agent design framework to facilitate distributed synchronous cooperation. The most widely used test functions from the formal literature of multiple objectives optimisation are utilised to test the HCPSO. In addition, a real case study, the internal subdivision problem of a ROPAX vessel, is provided to exemplify the applicability of the developed method
Key Challenges and Opportunities in Hull Form Design Optimisation for Marine and Offshore Applications
New environmental regulations and volatile fuel
prices have resulted in an ever-increasing need for reduction
in carbon emission and fuel consumption. Designs of marine
and offshore vessels are more demanding with complex
operating requirements and oil and gas exploration
venturing into deeper waters and hasher environments.
Combinations of these factors have led to the need to
optimise the design of the hull for the marine and offshore
industry. The contribution of this paper is threefold. Firstly,
the paper provides a comprehensive review of the state-ofthe-
art techniques in hull form design. Specifically, it
analyses geometry modelling, shape transformation,
optimisation and performance evaluation. Strengths and
weaknesses of existing solutions are also discussed.
Secondly, key challenges of hull form optimisation specific
to the design of marine and offshore vessels are identified
and analysed. Thirdly, future trends in performing hull
form design optimisation are investigated and possible
solutions proposed. A case study on the design optimisation
of bulbous bow for passenger ferry vessel to reduce wavemaking
resistance is presented using NAPA software.
Lastly, main issues and challenges are discussed to stimulate
further ideas on future developments in this area, including
the use of parallel computing and machine intelligence
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Three decades of the Shuffled Complex Evolution (SCE-UA) optimization algorithm: Review and applications
Application of Group Method of Data Handling and New Optimization Algorithms for Predicting Sediment Transport Rate under Vegetation Cover
Planting vegetation is one of the practical solutions for reducing sediment
transfer rates. Increasing vegetation cover decreases environmental pollution
and sediment transport rate (STR). Since sediments and vegetation interact
complexly, predicting sediment transport rates is challenging. This study aims
to predict sediment transport rate under vegetation cover using new and
optimized versions of the group method of data handling (GMDH). Additionally,
this study introduces a new ensemble model for predicting sediment transport
rates. Model inputs include wave height, wave velocity, density cover, wave
force, D50, the height of vegetation cover, and cover stem diameter. A
standalone GMDH model and optimized GMDH models, including GMDH honey badger
algorithm (HBA) GMDH rat swarm algorithm (RSOA)vGMDH sine cosine algorithm
(SCA), and GMDH particle swarm optimization (GMDH-PSO), were used to predict
sediment transport rates. As the next step, the outputs of standalone and
optimized GMDH were used to construct an ensemble model. The MAE of the
ensemble model was 0.145 m3/s, while the MAEs of GMDH-HBA, GMDH-RSOA, GMDH-SCA,
GMDH-PSOA, and GMDH in the testing level were 0.176 m3/s, 0.312 m3/s, 0.367
m3/s, 0.498 m3/s, and 0.612 m3/s, respectively. The Nash Sutcliffe coefficient
(NSE) of ensemble model, GMDH-HBA, GMDH-RSOA, GMDH-SCA, GMDH-PSOA, and GHMDH
were 0.95 0.93, 0.89, 0.86, 0.82, and 0.76, respectively. Additionally, this
study demonstrated that vegetation cover decreased sediment transport rate by
90 percent. The results indicated that the ensemble and GMDH-HBA models could
accurately predict sediment transport rates. Based on the results of this
study, sediment transport rate can be monitored using the IMM and GMDH-HBA.
These results are useful for managing and planning water resources in large
basins.Comment: 65 pages, 10 figures, 5 table
Mooring System Design Optimization Using a Surrogate Assisted Multi-Objective Genetic Algorithm
This is the author accepted manuscript. The final version is available from Taylor & Francis via the DOI in this record.This article presents a novel framework for the multi-objective optimization of o shore re-
newable energy mooring systems using a random forest based surrogate model coupled to
a genetic algorithm. This framework is demonstrated for the optimization of the mooring
system for a
oating o shore wind turbine highlighting how this approach can aid in the
strategic design decision making for real-world problems faced by the o shore renewable
energy sector. This framework utilizes validated numerical models of the mooring system
to train a surrogate model, which leads to a computationally e cient optimization routine,
allowing the search space to be more thoroughly searched. Minimizing both the cost and
cumulative fatigue damage of the mooring system, this framework presents a range of op-
timal solutions characterizing how design changes impact the trade-o between these two
competing objectives.This work is funded by the EPSRC (UK) grant for the SuperGen Marine United Kingdom Centre for Marine Energy Research (UKCMER) [grant number: EP/P008682/1]. The authors would also like to thank Jason Jonkman at NREL who provided the hydrodynamic data for the OC4 semi-submersible and Orcina Ltd. for providing OrcaFlex
A Hierarchal Planning Framework for AUV Mission Management in a Spatio-Temporal Varying Ocean
The purpose of this paper is to provide a hierarchical dynamic mission
planning framework for a single autonomous underwater vehicle (AUV) to
accomplish task-assign process in a limited time interval while operating in an
uncertain undersea environment, where spatio-temporal variability of the
operating field is taken into account. To this end, a high level reactive
mission planner and a low level motion planning system are constructed. The
high level system is responsible for task priority assignment and guiding the
vehicle toward a target of interest considering on-time termination of the
mission. The lower layer is in charge of generating optimal trajectories based
on sequence of tasks and dynamicity of operating terrain. The mission planner
is able to reactively re-arrange the tasks based on mission/terrain updates
while the low level planner is capable of coping unexpected changes of the
terrain by correcting the old path and re-generating a new trajectory. As a
result, the vehicle is able to undertake the maximum number of tasks with
certain degree of maneuverability having situational awareness of the operating
field. The computational engine of the mentioned framework is based on the
biogeography based optimization (BBO) algorithm that is capable of providing
efficient solutions. To evaluate the performance of the proposed framework,
firstly, a realistic model of undersea environment is provided based on
realistic map data, and then several scenarios, treated as real experiments,
are designed through the simulation study. Additionally, to show the robustness
and reliability of the framework, Monte-Carlo simulation is carried out and
statistical analysis is performed. The results of simulations indicate the
significant potential of the two-level hierarchical mission planning system in
mission success and its applicability for real-time implementation
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