Algorithm design.
Optimisation is only as good as the search engine behind it. I design and benchmark genetic algorithms, evolutionary methods, and hybrid strategies — including custom NDCI-aware fitness functions — and evaluate them across OEM, operator, and MRO perspectives so the chosen algorithm matches the decision context, not the trend cycle.
Pick one. Watch it converge.
Six full implementations on the same DTLZ2 benchmark (M=3, n=12). Switch between them with the dropdown — same population size, same evaluation budget, same metrics (HV via Monte-Carlo, IGD against a 200-point true-front sample). The cyan octant is the analytic Pareto front; the orange spheres are the algorithm's current rank-0 set.
Closed-form vs. evolutionary.
The cockpit above runs an evolutionary search — population, generations, Pareto sorting. The two simulations below run a classical alternative for the same family of problem: closed-form proportional-navigation guidance. Each engagement is a single trajectory shaped by one analytic control law (acmd = N · Vc · σ̇, where σ̇ is the line-of-sight rate and Vc the closing velocity). PN intercepts cleanly when the target is non-cooperative and slower than the pursuer; it loses the lock when target lateral acceleration exceeds the pursuer's. Same maths, opposite outcomes.
Which sensor earns its place?
The benchmark question for an NDCI-aware GA is not "what's the best HV on a synthetic problem?" — it's "which sensor in the candidate suite would I lose first?" Below: each sensor's diagnostic score, computed from the published thesis matrix as the sum of its relative NDCI contribution across 12 fault modes. Click any bar to remove that sensor — the rest re-rank live, because removing a strong sensor lifts the relative score of whatever now dominates each fault.
Non-dominated sorting genetic algorithm. Default workhorse for 2–4 objectives.
Reference-point variant for many-objective problems (5+).
Multi-objective particle swarm — fast on smooth landscapes.
Decomposition-based — strong when objective shape is known.
Bespoke fitness using the Normalised Diagnostic Contribution Index.
Memetic methods for ill-conditioned constraint sets.
Need the right algorithm?
If your team is reaching for whatever's in the closest library, that's a sign you'd benefit from someone who's benchmarked the alternatives in production conditions.
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