Computing device, calculation method, program, and circuit information

The computing device employs a simulated bifurcation algorithm to efficiently calculate approximate Pareto solutions in multi-objective optimization problems, addressing the limitations of existing methods by iteratively optimizing composite objective functions and predicting Pareto hypervolume changes, thus overcoming computational and balance determination challenges.

JP7864665B2Active Publication Date: 2026-05-25KK TOSHIBA +1
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Patent Information

Authority / Receiving Office
JP · JP
Patent Type
Patents
Current Assignee / Owner
KK TOSHIBA
Filing Date
2023-06-07
Publication Date
2026-05-25

AI Technical Summary

Technical Problem

Existing methods for solving multi-objective optimization problems, such as the method of constraints and linear weighted sum method, struggle with rationally determining constraints and weight coefficients, leading to difficulties in obtaining multiple Pareto solutions with different balances, especially in non-convex scenarios, and require significant computational resources.

Method used

A computing device and method that uses a simulated bifurcation algorithm with a solver device to generate and optimize composite objective functions, predicting Pareto hypervolume changes to efficiently calculate approximate Pareto solutions by iteratively updating candidate solutions with different weight patterns, utilizing quantum-inspired algorithms for parallel processing.

Benefits of technology

Accurately calculates multiple approximate Pareto solutions with reduced computational effort, effectively handling both convex and non-convex scenarios, and constraints, while providing a comprehensive set of solutions close to the Pareto front.

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Abstract

To calculate, with satisfactory accuracy and with less computing amount, an approximate Pareto solution group in a multi-purpose minimization problem.SOLUTION: A computing apparatus according to the present invention has a prediction unit, a selection unit, an update unit, a repeat control unit, and an output unit. The prediction unit executes prediction processing for predicting an increased amount of a Pareto hyper volume by using a wight pattern corresponding to each of a plurality of weight patterns. The selection unit executes selection processing for selecting a maximum weight pattern with a maximum increased amount. The update unit acquires, from a solver device, a plurality of solutions in a problem of minimizing a synthesized object function as a linear weighted sum of a plurality of object functions and a plurality of weight coefficient represented by the maximum weight patterns to add them to a candidate solution group, thereby carrying out update processing for updating the candidate solution group. The repeat control unit repeatedly controls a series of the prediction processing, the selection processing, and the update processing. The output unit outputs, as an approximate Pareto solution group, a group including a non-dominated solution of the candidate solution group.SELECTED DRAWING: Figure 6
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