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

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.

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 2211
Author(s):  
Na Wei ◽  
Mingyong Liu ◽  
Weibin Cheng

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.


2013 ◽  
Vol 433-435 ◽  
pp. 1226-1229
Author(s):  
Yang Chen ◽  
Yan Cheng Liu ◽  
Chuan Wang ◽  
Liang Xiong Shen

The shipboard power system (SPS) supplies energy to sophisticated systems for weapons, communications, navigation and operation. It is critical for the system to be reconfigurable for the purpose of survivability and reliability. The present paper proposes a new variation of PSO model for restoration of shipboard power system. A new inertia weight of S-Curve Decreasing variation is introduced. Particle Swarm Optimization with S-Curve Decreasing Inertia Weight (SDW-PSO) approach can improve the speed of convergence as well as fine tune the search. The proposed Particle Swarm Optimization algorithm enables to find the optimal combination of loads that can be supplied after the occurrence of the fault, in which the priorities of the loads and the constraint of balance between the total load and total generation are considered.


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