Simulated Annealing Device for Constraint Satisfaction

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Solution Overview

Problem

Existing simulated annealing methods face difficulties in efficiently solving combinatorial optimization problems with multiple constraints, as they struggle to satisfy all constraints simultaneously, leading to reduced accuracy and increased computation time.

Innovation Solution

A simulated annealing device and method that converts combinatorial optimization problems into Boolean Satisfiability Testing (SAT) expressed in Conjunctive Normal Form (CNF), using a SAT solver to find candidate solutions that satisfy all constraints, thereby facilitating the search for optimal solutions.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If the simulated annealing method is used to solve combinatorial optimization problems with multiple constraints, then the problem can be solved in a general manner, but the accuracy of the obtained solution deteriorates and the computation time increases

Engineering Contradiction:

Improveability to handle multiple constraintsVSAvoidsolution accuracy

Core Design Contradiction:

Adaptability or versatilityVSManufacturing precision

Solution Approach 1:

The patent introduces an intermediary mechanism (constraint satisfaction check and constraint-violating spin identification) between the standard simulated annealing process and the solution output. This intermediary ensures that only spin flips maintaining constraint satisfaction are considered, thereby maintaining solution accuracy while preserving the general applicability of the simulated annealing method to combinatorial optimization problems with multiple constraints.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Adaptability or versatility

If the simulated annealing method is used to solve combinatorial optimization problems with multiple constraints, then the problem can be solved in a general manner, but the computation time required increases

Engineering Contradiction:

Improveability to handle multiple constraintsVSAvoidcomputation time

Core Design Contradiction:

Adaptability or versatilityVSLoss of time

Solution Approach 1:

The patent performs preliminary actions by pre-identifying and storing the relationships between spins and constraints before the simulated annealing process begins. During the annealing process, this pre-established information allows for rapid determination of whether a spin flip violates constraints, eliminating the need for time-consuming re-evaluation of all constraints during each iteration and thus reducing overall computation time.

Inventive Principle:

Principle #10Preliminary action

3Reliability

If constraint terms are added to the energy function to impose constraints, then the constraints can be enforced, but the change amount in energy becomes large and transitions become difficult

Engineering Contradiction:

Improveconstraint satisfactionVSAvoidspin transition ease

Core Design Contradiction:

ReliabilityVSEase of operation

Solution Approach 1:

The patent segments the constraint satisfaction problem by identifying and tracking individual spins that are associated with specific constraints. Instead of treating constraints as a monolithic energy penalty, the method divides the problem into discrete spin-constraint relationships, allowing for targeted and efficient spin flips that maintain constraint satisfaction while minimizing energy changes.

Inventive Principle:

Principle #1Segmentation

Data Source

PatentUS20240289640A1Simulated annealing device and simulated annealing method

Publication Date: 2024.08.29 NEC CORP

AI summary

A simulated annealing device includes a solving unit which solves a SAT (Boolean Satisfiability Testing) expressed in a CNF (Conjunctive Normal Form) form with constraint information indicating one or more constraints imposed on a combinatorial optimization problem converted, to obtain sets of values of multiple variables of the combinatorial optimization problem that satisfy the one or more constraints, which are candidate solutions to the combinatorial optimization problem.