Adaptive Tabu Tenure Search for Combinatorial Optimization
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Solution Overview
Problem
Existing tabu search methods face challenges in determining an appropriate tabu tenure for combinatorial optimization problems, requiring manual adjustment through trial and error, which is time-consuming and inefficient.
Innovation Solution
A data processing program that automatically determines an appropriate tabu tenure by analyzing the slope of change in the number of state variables during tabu search, using a storage unit to store this information and adjusting the tabu tenure based on a calculated slope to optimize the search process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual trial and error method is used to determine tabu tenure, then appropriate tabu tenure can be found, but it takes a long time
Solution Approach 1:
The patent implements feedback by automatically monitoring the number of state variable changes during tabu search execution and using this information to dynamically adjust the tabu tenure. The system continuously feeds back the search state (number of changed variables) to the tabu tenure determination mechanism, enabling real-time optimization without manual intervention. This resolves the contradiction by replacing time-consuming manual trial-and-error with automated feedback-driven adjustment.
Solution Approach 2:
The tabu search system performs self-service by automatically determining its own optimal tabu tenure based on the observed number of state variable changes. The system monitors its own search progress and autonomously adjusts parameters without external human input. This eliminates the time loss associated with manual parameter tuning while maintaining accurate tabu tenure determination.
2Productivity
If common tabu tenure is used for different problems, then setup time is reduced, but solution finding performance deteriorates
Solution Approach 1:
The patent applies dynamics by making the tabu tenure adjustable and adaptive rather than fixed. The system dynamically changes the tabu tenure based on the actual search state (number of state variable changes) observed during execution. This allows the tabu search to adapt to different problem characteristics automatically, maintaining high solution finding performance across diverse problems while requiring minimal setup time.
Solution Approach 2:
The system changes the tabu tenure parameter automatically based on the number of state variable changes observed during the search. By monitoring this parameter and adjusting the tabu tenure accordingly, the system optimizes solution finding performance for different problems without requiring manual parameter setting. This resolves the contradiction between setup time and performance by making the parameter adaptive rather than static.
Data Source
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AI summary
A data processing 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.