Design of quasi phase matching crystal based on differential gray wolf algorithm

2025-11-03

AI Summary

Optimizing aperiodic periodically poled crystals involves high-dimensional discrete combinatorial optimization—an NP-hard problem—where conventional algorithms suffer from slow convergence, susceptibility to local optima, and poor efficiency on CPU-based serial computation. This paper proposes a hybrid metaheuristic algorithm integrating differential evolution (DE) and grey wolf optimizer (GWO), synergistically balancing global exploration and local exploitation. Crucially, we present the first GPU-accelerated implementation of this hybrid optimizer, enabling fine-grained thread-level parallelism. The approach achieves solution accuracy comparable to state-of-the-art methods while accelerating optimization by two to three orders of magnitude, thereby enabling high-precision design of large-scale domain structures. This work provides a critical enabler for advancing quasi-phase-matched devices in quantum optics and precision laser machining toward higher performance and industrial scalability.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Machine Learning: Hardware-aware ML

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

This paper focuses on the key problem in the development of nonlinear optical technology, the performance optimization of aperiodically polarized crystals. The performance of the crystal depends on the precise control of the micro distribution of crystal domains, but its optimization belongs to the high-dimensional discrete combination "NP hard" problem. The traditional algorithm has the bottleneck of slow convergence and easy to fall into local optimization, while the heuristic methods such as genetic algorithm are limited by the CPU serial calculation and inefficient. In order to solve the above challenges, this paper proposes the fusion scheme of hwsda hybrid optimization algorithm and GPU parallel acceleration technology: the differential evolution algorithm (DE) is used to realize the global search, and the gray wolf optimization algorithm (GWO) is used to strengthen the local search and convergence speed, and the two coordinate to balance the global and local optimization requirements; At the same time, it relies on GPU multi-core architecture to realize thread level parallel computing and improve optimization efficiency. This scheme effectively breaks through the optimization problem of high-dimensional discrete space, improves the accuracy of crystal domain control, improves the efficiency of quasi phase matching design by hundreds to thousands of times compared with traditional CPU serial computing, provides a new paradigm for the design of complex nonlinear optical devices, and helps promote the performance breakthrough and industrial application of related devices in the fields of quantum optics and laser processing.

Problem

• Optimizes aperiodic crystal performance for nonlinear optics
• Solves high-dimensional discrete NP-hard optimization problems
• Overcomes slow convergence and local optimization bottlenecks

Innovation

• Hybrid differential evolution with gray wolf optimization
• GPU parallel acceleration for high-dimensional optimization
• Balancing global and local search for crystal design