CAJ | 학술논문

为了解决规模复杂的旅行商问题，提出了融合蚁群算法和粒子群算法的一种群体智能混合算法，并构建了惯性权值模糊自适应调整模型。针对此混合算法易陷入局部最优，设计了参数自动调节机制，以达到局部搜索和全局搜索之间的平衡。在搜索的初期时，参数ω会自适应调整为较大值，则算法应具有很强的全局搜索能力；当进入搜索的后期时，参数ω会自适应调整为较小值，则算法应具有较强的局部搜索能力。通过大量仿真实验表明，改进的混合算法搜索能力优于同类算法和传统算法，而且该模型应用在大规模TSP中，获得了满意的效果。
위료해결규모복잡적여행상문제，제출료융합의군산법화입자군산법적일충군체지능혼합산법，병구건료관성권치모호자괄응조정모형。침대차혼합산법역함입국부최우，설계료삼수자동조절궤제，이체도국부수색화전국수색지간적평형。재수색적초기시，삼수ω회자괄응조정위교대치，칙산법응구유흔강적전국수색능력；당진입수색적후기시，삼수ω회자괄응조정위교소치，칙산법응구유교강적국부수색능력。통과대량방진실험표명，개진적혼합산법수색능력우우동류산법화전통산법，이차해모형응용재대규모TSP중，획득료만의적효과。
In order to solve the complex scale problem of traveling salesman, it puts forward a kind of swarm intelligent hybrid algorithm of combination of ant colony algorithm and particle swarm algorithm and constructs fuzzy self-adapted adjustment model of inertia weight. For the hybrid algorithm trapped into local optimaleasily, the parameter automatic adjust-ment mechanism is designed to achieve local search and global search of equilibrium. In the early of search, the parametersω would adaptively adjust to a larger value, after that the algorithm has strong global search ability. In the late stage of search, the parameters ω would adaptively adjust to a smaller value, after that the algorithm has strong local search capa-bility. A number of simulation experiments show that search ability of the improved hybrid algorithm is superior to the similar algorithm and traditional algorithm. And it has satisfactory results applied in large scale TSP.