Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The quadratic assignment problem (QAP) consists of assigning n facilities to n locations to minimize the total ighted cost of interactions beten facilities. The QAP arises in many diverse settings, is known to be NP-hard, and can be solved to optimality only for fairly small instances (typically, n ≤ 30). Neighborhood search algorithms are the most popular heuristic algorithms for solving larger instances of the QAP. The most extensively applied neighborhood structure for the QAP is the 2-exchange neighborhood. This neighborhood is obtained by swapping the locations of two facilities; its size is therefore O(n2). Previous efforts to explore larger neighborhoods (such as 3-exchange or 4-exchange neighborhoods) re not very successful, as it took too long to evaluate the larger set of neighbors. ,  propose very large-scale neighborhood (VLSN) search algorithms when the size of the neighborhood is very large, and  propose a novel search procedure to enumerate good neighbors heuristically. Our search procedure relies on the concept of an improvement graph that allows us to evaluate neighbors much faster than existing methods. ,  present extensive computational results of our algorithms when applied to standard benchmark instances.
['Ravindra K. Ahuja', 'Krishna Jha', 'James B. Orlin', 'Dushyant Sharma']
I am writing a research paper on a idea Very Large-Scale Neighborhood Search for the Quadratic Assignment Problem
[{"from": "human", "value": "I am writing a research paper on a idea Very Large-Scale Neighborhood Search for the Quadratic Assignment Problem"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The quadratic assignment problem (QAP) consists of assigning n facilities to n locations to minimize the total ighted cost of interactions beten facilities. The QAP arises in many diverse settings, is known to be NP-hard, and can be solved to optimality only for fairly small instances (typically, n \u2264 30). Neighborhood search algorithms are the most popular heuristic algorithms for solving larger instances of the QAP. The most extensively applied neighborhood structure for the QAP is the 2-exchange neighborhood. This neighborhood is obtained by swapping the locations of two facilities; its size is therefore O(n2). Previous efforts to explore larger neighborhoods (such as 3-exchange or 4-exchange neighborhoods) re not very successful, as it took too long to evaluate the larger set of neighbors. ,  propose very large-scale neighborhood (VLSN) search algorithms when the size of the neighborhood is very large, and  propose a novel search procedure to enumerate good neighbors heuristically. Our search procedure relies on the concept of an improvement graph that allows us to evaluate neighbors much faster than existing methods. ,  present extensive computational results of our algorithms when applied to standard benchmark instances."}]