The efficacy of state-of-the-art optimization algorithms often stems from complex, serial decision making logic that is fundamentally incompatible with the throughput oriented nature of modern GPU architectures. This conflict presents a major bottleneck for solving large-scale problems in scientific computing. We present Helios (Heterogeneous, Evolutionary, and Landscape-aware Interacting Optimization System) to resolve this algorithm–architecture challenge. We present the co-design of two distinct implementations: Helios-AS (Adaptive Serial), a novel CPU-based algorithm that employs heterogeneous agent roles and a state machine that dynamically triggers specialized search procedures based on real-time analysis of the fitness landscape and Helios-MP (Massively Parallel), its GPU-native counterpart. Our core contribution is a principled methodology for translating the intent of the serial adaptive logic into novel, parallel-friendly operators, effectively distilling complex state-based control into a high-throughput swarm intelligence. Validated on a suite of challenging benchmarks and the Lennard-Jones atomic cluster problem, we demonstrate that Helios-AS achieves performance statistically comparable to state-of-the-art methods like CMA-ES. Furthermore, we show that Helios-MP matches this high solution quality while delivering speedups of up to 15×. Helios provides both a powerful, validated tool for computational science and a successful blueprint for porting algorithmic intelligence to massively parallel hardware.