CAJ | 학술논문

如何获取多目标问题更多的Pareto 最优解具有十分重要的意义。在重新定义蝙蝠位置和速度更新公式的基础上，提出了一种用于求解多目标0－1规划问题的改进的蝙蝠算法。通过测试函数进行仿真实验，结果表明：与遗传算法、蚁群算法、元胞蚁群算法和粒子群算法相比，所提出的算法能够为多目标0－1规划问题找到更多的Pareto解，体现了蝙蝠算法在解决该问题上的有效性和优越性。
여하획취다목표문제경다적Pareto 최우해구유십분중요적의의。재중신정의편복위치화속도경신공식적기출상，제출료일충용우구해다목표0－1규화문제적개진적편복산법。통과측시함수진행방진실험，결과표명：여유전산법、의군산법、원포의군산법화입자군산법상비，소제출적산법능구위다목표0－1규화문제조도경다적Pareto해，체현료편복산법재해결해문제상적유효성화우월성。
Obtaining more Pareto solutions is very important for the multi?objective problem. This paper presented an improved bat algorithm for solving the multi?objective 0-1 programming problem with linear constrains. The pro?posed algorithm, which is based on redefining the updating formulas of the velocity and position about every bat, is implemented through several tests. The algorithm is compared with a genetic algorithm, an ant colony optimization algorithm, a cellular ant colony algorithm and a particle swarm optimization algorithm. The comparisons showed that the proposed algorithm can get more Pareto solutions and be much more effective to solve such problems.