Solution Search System Using Non-Uniform Fluctuation Probabilities
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Solution Overview
Problem
Existing solution-searching systems for Boolean Satisfiability (SAT) problems, particularly in scheduling and combinatorial optimization, face high computational complexity and inefficiency as problem sizes increase, and are not well-suited for real-world applications like transportation systems with complex scheduling demands.
Innovation Solution
A solution-searching system utilizing non-uniform fluctuations, with an output adjustment unit, data generation and conversion units, and a feedback control unit to optimize the occurrence-frequencies of binary data, allowing for efficient search of optimal solutions in Boolean satisfiability problems expressed by multiple constraints and logical conjunctions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If uniform fluctuations are assigned by internal structure to solve SAT problems, then solution searching capability is provided, but the algorithm becomes complicated and circuit needs to be changed for each problem instance
Solution Approach 1:
The patent applies parameter changes by externally adjusting fluctuation probabilities for different variables rather than using fixed internal uniform fluctuations. This allows the same circuit to adapt to different problem instances by changing parameter values (fluctuation probabilities) without requiring circuit reconfiguration, thereby resolving the contradiction between adaptability and device complexity
Solution Approach 2:
The patent creates a universal solution-searching system that can handle various SAT problem instances using the same circuit structure. By making the fluctuation probabilities externally controllable and problem-instance-specific, the system achieves multi-functionality without increasing circuit complexity, allowing one circuit to serve multiple problem types
2Quantity of substance
If problem size increases for SAT problems, then more comprehensive solution coverage is achieved, but computational resources and time required increase exponentially
Solution Approach 1:
The patent introduces dynamic fluctuation probabilities that can be adjusted during the solution-searching process and across different problem instances. This dynamic approach allows the system to adapt its search behavior to match problem characteristics, improving computational efficiency for larger problems by focusing fluctuations where they are most effective rather than using static uniform fluctuations
Solution Approach 2:
The patent applies local quality by assigning different fluctuation probabilities to different variables based on their importance and characteristics in the specific problem instance. This localized approach to fluctuation distribution allows the system to efficiently search large solution spaces by concentrating computational effort on critical variables, thereby maintaining productivity even as problem size increases
Data Source
AI summary
A solution-searching system encompasses an output adjustment unit having N signal adjustment circuits for converting provisional output-adjustment signals into final output-adjustment signals, 2N data generation units for generating binary data, 2N data conversion units which convert the binary data into information, a fluctuation setting unit for suppling bias probabilities to the signal adjustment circuits, fluctuation probabilities to the data generation units, and threshold values to the data conversion units, setting occurrence-frequencies of the binary data for making an occurrence-frequency of a specific variable, and a feedback control unit for determining whether an optimal solution has been found based on the information converted by the data conversion units and search-problem information preliminarily entered, by repeating transmission of the final output-adjustment signals to the output adjustment units, if the optimal solution has not been found.