Dynamic Tabu Tenure Adjustment in Combinatorial Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing solution search methods for combinatorial optimization problems, such as the tabu search method, face challenges in improving solution finding performance due to inappropriate determination of the period of time during which updates of state variable values are suppressed.
Innovation Solution
A data processing device calculates a first index value related to the change amount of the evaluation function when a state variable is updated, and based on this index value, determines the length of the period of time during which updates of the state variable value are suppressed, thereby dynamically adjusting the tabu tenure for each state variable.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a fixed tabu tenure is used for all state variables, then the search process is simple to implement, but the solution finding performance is insufficient
Solution Approach 1:
The patent applies dynamics by transitioning from a fixed tabu tenure to a dynamic determination method. The tabu tenure for each state variable is calculated based on the change amount of the evaluation function, allowing the suppression period to adapt automatically to the actual search situation, thereby improving solution finding performance while maintaining reasonable implementation complexity
Solution Approach 2:
The patent changes the parameter of tabu tenure from a fixed value to a dynamically calculated value. By using the change amount of the evaluation function as a basis for determining tabu tenure, the system adjusts the suppression period parameter according to the search progress and problem characteristics, resolving the contradiction between performance and complexity
2Stability of the object's composition
If the tabu tenure is extended to prevent cycling, then the search stability improves, but the search time increases
Solution Approach 1:
The patent applies local quality by determining different tabu tenure values for different state variables based on their individual impact on the evaluation function. Instead of using a uniform extended tabu tenure for all variables, the system locally adjusts the suppression period for each state variable according to its change amount, thereby maintaining search stability while avoiding unnecessary time consumption
Solution Approach 2:
The patent uses dynamic adjustment of tabu tenure based on the change amount of the evaluation function. When the change amount is large, a longer tabu tenure is applied to prevent cycling; when the change amount is small, a shorter tenure is used to reduce search time. This dynamic approach balances stability and time efficiency
3Loss of time
If the tabu tenure is shortened to reduce search time, then the search speed increases, but the solution quality deteriorates
Solution Approach 1:
The patent changes the tabu tenure parameter from a fixed short value to a dynamically calculated value based on the evaluation function's change amount. This allows the system to extend the tenure when needed to maintain solution quality and shorten it when appropriate to reduce search time, resolving the contradiction between speed and quality
Data Source
AI summary
A non-transitory computer-readable recording medium storing a program for causing a computer to execute processing including: calculating, when a value of any one state variable among a plurality of state variables is updated in a solution search based on an evaluation function that includes the plurality of state variables, a first index value related to a change amount of a value of the evaluation function in a case where a value of a first state variable among the plurality of state variables is updated from values of the plurality of state variables before the update; and determining, based on the first index value, a length of a period of time during which update of the value of the first state variable is suppressed after the value of the first state variable is updated.