Ultimate Limit State Based Ship Structural Design Using Multi-Objective Discrete Particle Swarm Optimization

2015 ◽  
Author(s):  
Ming Ma ◽  
Owen Hughes ◽  
Tobin McNatt
Keyword(s):  
Finite Element ◽  
Particle Swarm Optimization ◽  
Limit State ◽  
Particle Swarm ◽  
Structural Safety ◽  
Ultimate Limit State ◽  
Discrete Particle Swarm Optimization ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Ultimate Limit

Multi-objective optimization problems consist of several objectives that must be handled simultaneously. These objectives usually conflict with each other, and optimizing a particular solution with respect to a single objective can result in unacceptable results with respect to the other objectives. A reasonable solution to a multi-objective problem is to investigate a set of solutions, each of which satisfies the objectives at an acceptable level without being dominated by any other solution. Genetic or evolution algorithms have been demonstrated to be particularly effective to determine excellent solutions to these problems. Among many algorithms, the particle swarm optimization (PSO) has been found to be faster with less computational overhead. In this paper a multi-objective discrete particle swarm optimization is formulated and used to optimize a large and complex thin-wall structure on the basis of weight, safety and cost. The structure weight and cost are calculated using realistic finite element models. The design process has two stages: (1) the actual stresses are obtained by finite element analysis of the full ship, (2) for a midship segment of the ship (referred to as a “control cluster”) the structural safety is evaluated using the ALPS/ULSAP set of ultimate limit state criteria, and then the segment is optimized using any suitable optimization method (in this paper, the PSO method). Both stages involve iteration, but the process is arranged so as to keep the number of full ship finite element analyses to a minimum. The complete design process is illustrated for a 200,000 ton oil tanker. The numerical results show that the PSO method is very useful to perform ultimate strength based ship structural optimization with multi-objectives, namely minimization of the structural weight and cost and maximization of structural safety. The example also demonstrates that the proper definition of boundary conditions and design load cases is of paramount importance for design optimization.

scholarly journals Decision-Making of Underwater Cooperative Confrontation Based on MODPSO

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 2211
Author(s):  
Na Wei ◽  
Mingyong Liu ◽  
Weibin Cheng
Keyword(s):  
Decision Making ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Communication Delay ◽  
Discrete Particle Swarm Optimization ◽  
Swarm Optimization ◽  
Discrete Particle ◽  
Multi Objective ◽  
Decision Making Model ◽  
The Impact

This paper proposes a multi-objective decision-making model for underwater countermeasures based on a multi-objective decision theory and solves it using the multi-objective discrete particle swarm optimization (MODPSO) algorithm. Existing decision-making models are based on fully allocated assignment without considering the weapon consumption and communication delay, which does not conform to the actual naval combat process. The minimum opponent residual threat probability and minimum own-weapon consumption are selected as two functions of the multi-objective decision-making model in this paper. Considering the impact of the communication delay, the multi-objective discrete particle swarm optimization (MODPSO) algorithm is proposed to obtain the optimal solution of the distribution scheme with different weapon consumptions. The algorithm adopts the natural number coding method, and the particle corresponds to the confrontation strategy. The simulation result shows that underwater communication delay impacts the decision-making selection. It verifies the effectiveness of the proposed model and the proposed multi-objective discrete particle swarm optimization algorithm.

scholarly journals A Novel Multi-Objective Discrete Particle Swarm Optimization with Elitist Perturbation for Reconfiguration of Ship Power System

2017 ◽  
Vol 24 (s3) ◽  
pp. 79-85
Author(s):  
Lingjie Zhang ◽  
Jianbo Sun ◽  
Chen Guo
Keyword(s):  
Particle Swarm Optimization ◽  
Power System ◽  
Particle Swarm ◽  
Index Test ◽  
Discrete Particle Swarm Optimization ◽  
Swarm Optimization ◽  
Discrete Particle ◽  
Multi Objective ◽  
Shipboard Power System ◽  
Shipboard Power

Abstract A novel multi-objective discrete particle swarm optimization with elitist perturbation strategy (EPSMODPSO) is proposed and applied to solve the reconfiguration problem of shipboard power system(SPS). The new algorithm uses the velocity to decide each particle to move one step toward positive or negative direction to update the position. An elitist perturbation strategy is proposed to improve the local search ability of the algorithm. Reconfiguration model of SPS is established with multiple objectives, and an inherent homogeneity index is adopted as the auxiliary estimating index. Test results of examples show that the proposed EPSMODPSO performs excellent in terms of diversity and convergence of the obtained Pareto optimal front. It is competent to solve network reconfiguration of shipboard power system and other multi-objective discrete optimization problems.

Applications of Vector Evaluated Genetic Algorithms (VEGA) in Ultimate Limit State Based Ship Structural Design

2014 ◽  
Author(s):  
Owen Hughes ◽  
Ming Ma ◽  
Jeom Kee Paik
Keyword(s):  
Genetic Algorithms ◽  
Structural Design ◽  
Limit State ◽  
Structural Safety ◽  
Wall Structure ◽  
Ultimate Limit State ◽  
Thin Wall ◽  
Limit States ◽  
Multi Objective ◽  
Ultimate Limit

Ship structural design often deals with multiple objectives such as weight, safety, and cost. These objectives usually conflict with each other, and optimizing a particular solution with respect to a single objective can result in unacceptable results with respect to the other objectives. A reasonable solution to a multi-objective problem is to investigate a set of solutions, each of which satisfies the objectives at an acceptable level without being dominated by any other solution. Genetic algorithms have been demonstrated to be particularly effective to determine excellent solutions to these problems. In this paper a multi-objective GA, called Vector Evaluated Genetic Algorithm (VEGA) is formulated and used to optimize a large and complex thin-wall structure (a complete cargo hold of a 200,000 ton oil tanker) on the basis of weight, safety and cost. The structure weight and cost and all of the stresses are calculated using a realistic finite element model. The structure adequacy is then evaluated using the ALPS/ULSAP computer program (Paik and Thayamballi, 2003) which can efficiently evaluate all six ultimate limit states for stiffened panels and grillages. This example was chosen because the initial design is severely inadequate. The results show that the proposed method can perform ultimate strength based structural optimization with multi-objectives, namely minimization of the structural weight and cost and maximization of structural safety, and also that the method is very robust.

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