CAJ | 학술논문

分析避风型渔港规划问题的特点，将其抽象为离散型约束选址分配问题，建立该问题的数学模型。根据模型特性，结合基本粒子群算法，提出一种改进的粒子群优化算法，给出用于计算离散型粒子位置和速度的替换变异操作的定义，保持种群的多样性、提高粒子的适应性；设计一种基于贪婪原则的高效的适应度函数。以渔港和渔船坐标为实验数据，用传统算法和改进粒子群优化算法分别在小规模和大规模实验数据上进行测试，实验结果表明，改进粒子群算法在解决大规模数据的渔港规划问题上表现出较高效率，有一定准确性，为解决该问题提供了可靠的方法。
분석피풍형어항규화문제적특점，장기추상위리산형약속선지분배문제，건립해문제적수학모형。근거모형특성，결합기본입자군산법，제출일충개진적입자군우화산법，급출용우계산리산형입자위치화속도적체환변이조작적정의，보지충군적다양성、제고입자적괄응성；설계일충기우탐람원칙적고효적괄응도함수。이어항화어선좌표위실험수거，용전통산법화개진입자군우화산법분별재소규모화대규모실험수거상진행측시，실험결과표명，개진입자군산법재해결대규모수거적어항규화문제상표현출교고효솔，유일정준학성，위해결해문제제공료가고적방법。
To analyze the features of planning problem of fishing port sheltered from typhoon,and to abstract it into discrete con-straint location allocation problem,then mathematical model for the problem was built.According to the characteristics of model and combining the basic particle swarm optimization (PSO)algorithm,an improved PSO algorithm was proposed,and the defini-tion of alternate mutate (AM)operation used to calculate discrete particle position and velocity was described,the diversity of population and the adaptability of particles were ensured.An efficient fitness function based on the principle of greed was de-signed.Experimental data were from fishing port and fishing boat coordinates,experiments on the traditional algorithm and the improved PSO algorithm for small-scale and mass-scale experimental data were carried out respectively.Results of the experi-ments show the high efficiency and certain accuracy of improved PSO algorithm on solving the fishing port planning problem of mass-scale data,and it provides reliable solutions for the problem.