Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  first introduced the rough searching and subtle searching process of ant colony optimization (ACO) in solving multi-dimensional and constrained functions, and then analyzed the key parameters affecting the optimization performance of ACO. A conclusion was drawn which required that the parameter α and β should be adjusted in descending order and ρ in ascending order. Next three fuzzy dynamic regulators of α, β and ρ re built respectively, and their corresponding parameter adjustment procedures, the executed strategies and the control procedures re also given. Finally the performance of the proposed ACO with adaptive parameter adjustment was verified in the case of a crane girder's optimization. Comparison results show that the self-adaptive adjusting ACO has many advantages such as higher solution precision, higher optimization efficiency and less number of ant colonies over conventional ACO. This method is a promising optimization algorithm for solving complex optimization problems.
['Xiaoying Li', 'Jin Li', 'Daohua Liu']
I am writing a research paper on a idea Ant colony optimization with self-adaptive parameter adjustment
[{"from": "human", "value": "I am writing a research paper on a idea Ant colony optimization with self-adaptive parameter adjustment"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  first introduced the rough searching and subtle searching process of ant colony optimization (ACO) in solving multi-dimensional and constrained functions, and then analyzed the key parameters affecting the optimization performance of ACO. A conclusion was drawn which required that the parameter \u03b1 and \u03b2 should be adjusted in descending order and \u03c1 in ascending order. Next three fuzzy dynamic regulators of \u03b1, \u03b2 and \u03c1 re built respectively, and their corresponding parameter adjustment procedures, the executed strategies and the control procedures re also given. Finally the performance of the proposed ACO with adaptive parameter adjustment was verified in the case of a crane girder's optimization. Comparison results show that the self-adaptive adjusting ACO has many advantages such as higher solution precision, higher optimization efficiency and less number of ant colonies over conventional ACO. This method is a promising optimization algorithm for solving complex optimization problems."}]