Adaptive Tabu Tenure Selection for Combinatorial Optimization
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Solution Overview
Problem
Existing tabu search methods for combinatorial optimization problems often require manual adjustment of tabu tenure, which is time-consuming and may not yield optimal results due to the variability of problems, leading to suboptimal solution finding performance.
Innovation Solution
A data processing device that automatically determines an appropriate tabu tenure by analyzing the slope of change in the number of unique moves (UM) during tabu search, using a second tabu search with a first tabu tenure optimized based on this analysis to improve solution finding performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual adjustment of tabu tenure is performed, then the tabu search can be executed, but the solution finding performance is suboptimal and the process is time-consuming
Solution Approach 1:
The system automatically determines the tabu tenure by analyzing the slope of change in the number of unique moves during tabu search execution, eliminating the need for manual adjustment. The processor monitors the relationship between tabu tenure and unique moves, and autonomously selects the optimal tabu tenure value that maximizes solution finding performance.
Solution Approach 2:
The system implements feedback by continuously monitoring the number of unique moves during tabu search and using this information to adjust the tabu tenure. The processor analyzes the slope of change in unique moves with respect to tabu tenure and uses this feedback to determine the optimal tabu tenure for subsequent searches, creating a closed-loop optimization system.
2Adaptability or versatility
If a fixed tabu tenure is used, then the tabu search is simple to implement, but it cannot adapt to different problem characteristics leading to suboptimal results
Solution Approach 1:
The system transitions from a static fixed tabu tenure approach to a dynamic adaptive approach. The tabu tenure is no longer fixed but is determined dynamically based on the actual performance of the tabu search, specifically the slope of change in the number of unique moves. This allows the system to adapt to different problem characteristics automatically.
Solution Approach 2:
The system changes the tabu tenure parameter based on observed performance metrics. By monitoring the relationship between tabu tenure and the number of unique moves, the system identifies the optimal tabu tenure value and adjusts the parameter accordingly, enabling adaptation to different problem characteristics without manual intervention.
3Measurement precision
If trial and error method is used to determine tabu tenure, then various tabu tenure values can be tested, but the determination process takes excessive time
Solution Approach 1:
The system performs preliminary analysis during the tabu search execution by monitoring the number of unique moves at different tabu tenure values. Instead of performing exhaustive trial and error after the search, the system collects performance data during the search process and uses this preliminary information to quickly determine the optimal tabu tenure, significantly reducing the time required.
Solution Approach 2:
The system skips the time-consuming exhaustive trial and error process by using the slope analysis method. By analyzing the relationship between tabu tenure and unique moves during the search, the system can rapidly identify the optimal tabu tenure without needing to test numerous values, thus rushing through the determination process efficiently while maintaining accuracy.
Data Source
AI summary
A non-transitory computer-readable recording medium storing a program for causing a computer to execute a process includes searching for a solution to a combinatorial optimization problem represented by a combination of values of a plurality of state variables by first tabu search in which a tabu tenure in which a value of a state variable whose value has changed is fixed is changed at intervals of a predetermined period, storing a number of the state variable whose value has changed in the predetermined period in a storage unit by the tabu tenure, determining a first tabu tenure in which a value of the state variable is fixed based on a slope of change in the number with respect to change in the tabu tenure, and searching for the solution by second tabu search that uses the determined first tabu tenure.