Proceedings of the Thirty-second (2022) International Ocean and Polar Engineering Conference
Shanghai, China, June 5–10, 2022
Copyright © 2022 by the International Society of Offshore and Polar Engineers (ISOPE)
ISBN 978-1-880653-81-4; ISSN 1098-6189

www.isope.org

Optimization design of ship main dimensions with diffusion algorithm based on P systems
Jie XIE1,2, Li Zheng WANG1,2, Hao WANG1,2, Long ZHENG1,2
1. School of Marine and Energy Power Engineering, Wuhan University of Technology, Ministry of Education, P. R. China.
2. School of Marine and Energy Power Engineering, Wuhan University of Technology, P. R. China.
Wuhan, Hubei Province, P.R. China

ABSTRACTmain scale parameters of energy-efficient ships.

Greening and decarbonization has become an inevitable trend in thedevelopment of the shipping industry. At the conceptual design stage ofa ship, a newly built ship must meet the energy efficiency standard ofCO2 emission. In view of the shortcomings in the solution accuracy andsolution efficiency of existing ship main scale optimization methods, thispaper proposes a P-system based diffusion algorithm (DAPS) for theoptimal solution of ship main scale. The 7,500 DWT bulk carrier of theYangtze River mainline is taken as the research object and the ship
energy efficiency design index (EEDI) is the objective function. Firstly,
the meaning of EEDI is analyzed. The correlation model between EEDI
and the main scale elements of the ship is also constructed. Then, theDAPS algorithm is used to optimize the design of the ship's main scaleand compared with the traditional genetic algorithm. It can be seen thatthe DAPS algorithm shows better performance in terms of solution
accuracy and efficiency, and can obtain the ship's main scale parametersthat meet the requirements of EEDI in a short time.

Ship main scale optimization problems with EEDI as the objective areoften complex. The objective function may be a function with multiple
peaks, nonlinear, discontinuous and non-differentiable. The design
variables and constraint functions may be linear, nonlinear, continuous,or discrete sets of variables. Traditional numerical optimization anddirect search methods are long-period, costly, and unable to find theglobal optimal solution. Intelligent optimization algorithms, as a rapidly
developing solution technique, can not only solve the problem thatclassical optimization algorithms often fall into local optimal solutions,but also give full play to computer computing power and improve
solution efficiency. A large number of intelligent optimization
algorithms have been proposed and used for solving the main scale
optimization problems of ships. Zou et al. (2014) studied the formula andbaseline equation of EEDI for high performance ships and proposed amulti-objective optimization method for high performance ships. A
particle swarm algorithm and genetic algorithm were used to optimize
Attained EEDI for an aluminum alloy fast response monohull ship, anda more optimal ship solution was derived. Hou et al. (2016) used themain scale of the ship and the ship type coefficient as design variables
and the non-drastic change of the displacement volume as the constraint
to form the optimization objective of minimizing the total resistance.
Particle swarm algorithm and simulated annealing algorithm were used
for uncertainty ship type optimization. Su et al. (2018) parametrized theship resistance and EEDI with a multipurpose ship and used the secondgeneration non-dominated ranking genetic algorithm (NSGA-II) toobtain the optimized ship main scale elements. Zhang et al. (2012)
established a neural network-based short-term prediction model of wave
resistance increase as well as wave resistance performance in typical sea
conditions, clarified the influence law of ship geometry elements on theresistance, wave resistance and EEDI in waves of container ships, andthen carried out specific ship optimization with total resistance andenergy efficiency design index as the target using multidisciplinary
optimization algorithm. Tang et al. (2020) applied multi-objectivegenetic algorithm based on MATLAB optimization toolbox to deriveenergy-efficient and efficient small waterline surface catamaran sheet
scale elements. Wang et al. (2021) used multi-island genetic algorithm(MIGA), particle swarm optimization algorithm (PSO) and adaptive
simulated annealing algorithm (ASA) to optimize the design of the main
scale of YUKUN vessel. The value of the energy efficiency design index

KEY WORDS: Ship Energy Efficiency Design Index, Ship Main
Dimensions, Optimization Design, DAPS algorithm

INTRODUCTION
Inland waterway shipping has shown a pivotal role in the transportationof bulk cargo. The design and construction of ships are getting larger,
greener and more intelligent. The emission of greenhouse gases orharmful gases such as carbon emission and sulfur emission caused byship transportation is also becoming more and more serious. The
environmental pollution problem brought by ship shipping industry hasreceived great attention from the international community. The
International Maritime Organization (IMO) has formulated a series ofconventions, regulations and rules on the prevention of pollution fromships. The "Energy Efficiency Design Index (EEDI)" as the energy
efficiency measurement standard for newly built ships came into effecton January 1, 2013. The mandatory implementation of the Energy
Efficiency Design Index for ships accelerates the process of energy
saving and emission reduction in ship construction. Higher requirements
have been put forward for the development of green ships. Therefore, inthe initial stage of ship design, it is especially important to find out the3374