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Results

Optimization Solver

Benchmarking BQPhy®'s QIEO Algorithm: Up to 3.9x Faster Than Genetic Algorithms

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Challenges

High-dimensional engineering optimization in digital missions is computationally expensive for traditional classical, gradient, and meta-heuristics algorithms.
Numerous cost function evaluations are required for complex problems which leads to
- Convergence at local minima
- Suboptimal designs
- Inefficient simulations

Results

Implementing QIEO on existing GPU systems enhances parallelization, increases search space exploration capabilities and achieves greater accuracy

QIEO achieved speedups of up to 3.9x across benchmark functions (2.9x for Ackley, 3.9x for Rosenbrock, and 3.84x for Rastrigin).

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It required significantly fewer function evaluations (up to 12x fewer for Ackley) and converged up to four times faster than GA (one-fourth the time for Rosenbrock and Rastrigin). Accurate identification of global minima (even if multiple) leading to More optimal design
Akley, Rastrigin, and Rosenbrock Test functions, known as artificial landscapes, were used to evaluate characteristics of BQPhy QIEO such as convergence rate, precision, robustness and general performance

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QIEO Outperforms Genetic Algorithms