基于融合算法的復(fù)雜優(yōu)化問題求解與應(yīng)用拓展
中圖分類號：TN957.51 文獻標志碼：A 文章編碼：1672-7274（2025）05-0007-03
Abstract： In the field of complex science and engineering，complex optimization problems are frequent and difficult to solve. Given the limitations of traditional single algorithms，this article focuses on the combination of dynamic programmingand heuristic algorithms forresearch，with the aim ofusing emerging technologies to expand their applications and beter overcome such chalenges.In the research，the advantages of combining the two were first analyzed，and then quantum heuristic algorithms were introduced to optimize the initial solution generation， incorporating multi-agent reinforcement learningcolaborative adjustment strategies，and elaborating on the specific methods strategies of the combination.Subsequently，comparative experiments were conducted by selecting multiple types of optimization problems such as travel agents，and practical verification was also caried out in logistics distribution scenarios.The results show that the combination method after integrating new technologies significantly improves indicators such as running time and solution quality compared to traditional methods.In application， it can reduce costs and improve efficiency.After innovative expansion，this combination methodcan providean effective path for solving complex optimization problems.
Keywords： dynamic programming; heuristic algorithm; complex optimization problems; quantum heuristic algorithm; multi-agent reinforcement learning; algorithm combination strategy
在科技飛速發(fā)展的當下，旅行商問題等復(fù)雜優(yōu)化問題求解難度高，是學(xué)術(shù)界與業(yè)界需攻克的關(guān)鍵。（剩余4480字）