EES Optimization — Multi-Objective Optimization for Engineering Equation Solver

EES ships a capable equation solver but only basic built-in optimization. EES MO upgrades any EES model to research-grade multi-objective optimization — genetic algorithms, particle swarms, parallel solves — without installing anything inside EES.

How does EES MO connect to EES?

Through two directives you already know. Your model reads its design variables with $Import and writes its objectives with $Export — that is the entire interface. EES MO writes candidate values into the input file, calls EES.exe /solve in hidden command-line mode, reads the exported objectives, and evolves the population. No DLL, no add-in, no macro programming:

$Warnings Off
$Import 'input.txt' A_coll, V_tank, m_dot
{ ... your equations ... }
$Export /H /N 'output.txt' Cost, W_comp

Every objective is minimized; to maximize a quantity like COP, export its negative. Both saved .ees binaries and plain-text equation files work.

Everything included

• 10 algorithms — NSGA-II, NSGA-III, U-NSGA-III, MOPSO, SPEA2, MOEA/D, AGE-MOEA, AGE-MOEA-II, SMS-EMOA, RVEA
• Parallel EES solves — each core gets a private work folder with its own copy of the model; the master file is never touched; near-N× speedup on N cores
• 1–99 objectives, up to 20 design variables, discrete variables supported
• Dialog-proof — $Warnings Off is injected automatically into per-core copies, stray dialogs are auto-dismissed, hung or non-converging solves are killed after a time limit and penalized, so overnight runs always finish
• Live Pareto plotting with zoom/pan and 2D/3D axis pickers, quality metrics, suggested best design
• CSV reports + settings save/load for repeatable studies; read-only install folders handled automatically
• Worked example included — a refrigeration cycle trading compressor power against heat-exchanger size (3 variables, 2 objectives)

What can you optimize with it?

Anything EES can solve: vapor-compression and absorption refrigeration cycles, heat pumps, organic Rankine cycles, solar thermal systems, cogeneration plants, heat-exchanger networks, and component sizing studies. The pattern is always the same — thermodynamic performance against size or cost — and it is exactly the two-objective trade-off a Pareto front answers. For algorithm guidance, see NSGA-II vs NSGA-III vs MOPSO.

Common questions

Which EES edition do I need?

EES Professional — its /solve command-line mode is what lets EES MO run solves hidden and in parallel. Check that your model solves normally inside the EES window first; then EES MO can drive it.

Does parallel solving risk corrupting my model?

No. Each core works in its own folder on its own copy; the master model is read once at the start of the run and never written. Work folders remain after the run for inspection and are recreated fresh next time.

How is this different from EES’s built-in min/max?

EES’s built-in optimizer is single-objective and runs one solve at a time. EES MO searches multiple objectives simultaneously, returns the whole Pareto front rather than one point, runs solves on every core in parallel, and logs every design to CSV.

See EES MO pricing and plans →