Annealing Transition Control for Faster Combinatorial Optimization
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Solution Overview
Problem
Current methods for solving combinatorial optimization problems, such as the traveling salesman problem and knapsack problem, face challenges in obtaining optimal solutions due to their NP-hard nature, leading to excessively long calculation times and often result in local minimum solutions rather than global optima.
Innovation Solution
An optimization device that employs a simulated annealing method with a transition control unit capable of selecting state transitions based on priority information and thermal noise, allowing for sequential or parallel search strategies to efficiently find the lowest energy state, thereby accelerating the solution-finding process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional optimization methods are used to solve combinatorial optimization problems, then solution accuracy can be maintained, but calculation time becomes excessively long
Solution Approach 1:
The patent applies simulated annealing by changing the temperature parameter to control the optimization process. The temperature is gradually reduced according to a cooling schedule, allowing the system to transition from exploring diverse solutions at high temperature to converging on optimal solutions at low temperature, thus balancing solution accuracy and calculation time
Solution Approach 2:
The patent introduces dynamic control of the temperature parameter and transition probability throughout the optimization process. The system dynamically adjusts its behavior based on current temperature, enabling efficient exploration early in the process and focused exploitation near the end, thereby reducing overall calculation time while maintaining solution quality
2Reliability
If exhaustive search methods are used to ensure optimal solutions, then solution quality is guaranteed, but computational complexity increases significantly
Solution Approach 1:
The patent uses many inexpensive, temporary solution states that are generated and discarded during the annealing process. Each state is evaluated and either accepted or rejected based on the Metropolis criterion, allowing the system to explore numerous potential solutions without maintaining complex data structures or performing expensive operations, thus reducing computational complexity while maintaining solution quality
Solution Approach 2:
By changing the temperature parameter, the system transitions between different computational regimes. At high temperature, the system accepts more diverse states including suboptimal ones, reducing the need for exhaustive exploration. As temperature decreases, the system becomes more selective, approaching optimal solutions without requiring complete enumeration of all possible states
3Productivity
If random search methods are used to reduce calculation time, then speed improves, but the system gets trapped in local minimum solutions
Solution Approach 1:
The temperature parameter is dynamically changed throughout the optimization process. At high temperatures, the system has high probability of accepting worse solutions, enabling escape from local minima. As temperature gradually decreases, the system becomes more selective, converging toward global optima. This parameter change strategy maintains both speed and reliability
Solution Approach 2:
The optimization process uses periodic temperature reduction steps (cooling schedule) to systematically transition between exploration and exploitation phases. This periodic action allows the system to repeatedly attempt escapes from local minima at higher temperatures before settling into optimal solutions, balancing speed and global optimality
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach significantly reduces the time required to find optimal or near-optimal solutions for combinatorial optimization problems by strategically selecting state transitions, improving the speed and efficiency of the solution-finding process compared to traditional methods.
Implementation Method 1
select any of the plurality of state transitions, based on priority information set based on state transition information updated last time with respect to identification information for identifying each state transition, and transition acceptance information indicating transition acceptance determined based on the change value of the energy, and a generated thermal noise
Data Source
AI summary
An information processing device includes: a memory; and a processor configured to: hold each of values of state variables included in an evaluation function representing energy; calculate the change value of the energy for each of state transitions, when a state transition occurs due to a change of any of the values of the state variables; control a temperature value representing temperature; select any of the state transitions, based on priority information set based on state transition information updated last time with respect to identification information for identifying each state transition, and transition acceptance information indicating transition acceptance determined based on the change value, and a generated thermal noise; and output a lowest energy state which is the values of the state variables when the energy to be updated based on the selected state transition becomes a lowest value.