REALTIME METAHEURISTIC TECH 2.0
We have created a brand new Metaheuristic technology (RM2) that is able to endow intelligent systems on specific areas with infinite knowledge. It allows for the optimization of a function or process without the need for knowledge of the internal workings of the function or process. This means that the optimization can be done even if the function or process is not fully understood or if it is too complex or expensive to analyze.
RM2 the long-awaited high-efficiency alternative for AI-based innovations, robotics and many areas for particularly efficient business returns;
1. For real-time decisions
This tool can be used for countless decision needs and tasks, because it can always make the best decision in real time (!) considering the infinite combinations of real interactions behind the available information.
2. For much more high-quality training data
In the development of artificial intelligence technologies or robotics, it is a huge advantage if we can produce a large amount of high-quality training data in a very short time... however, in the case of the SINEXTON solution, it is not a problem if the quality of the input data is very low or very noisy (!)
3. For process optimization and parameter optimization
RM2 can be used in all areas where with large amounts of data and complex problems. It can parametrize the missing (even several) components of any process, construction or system in real time mathematically perfectly and simultaneously, taking into account complex conditions. (Always guaranteeing a global optimum.) (!) It's an excellent new, relatively quickly deployable efficiency-enhancing tool, e.g. in marketing, cyber security, drug development and countless other areas.
IN WHICH SECTORS might RM be needed?
optimizing renewable energy systems and energy usage in buildings.
Optimizing routing and scheduling for logistics and delivery.
Unprecedented level of reliable real-time decision in several different areas, or to generate training data for Reinforcement Learning methodology for AI-based self-driving.
Optimizing production processes for increased efficiency and reduced waste.
Optimizing investment portfolios and risk management.
Optimizing patient treatment plans and drug development.
This technology helps by providing a systematic approach to finding the best solution to a problem based on a set of objectives and constraints, allowing organizations to identify and implement new, more efficient and effective solutions.
Additionally, it enables companies to test and evaluate multiple solutions quickly, which can lead to faster innovation.
What sets our solution apart from others?
General black-box optimizer
Almost only global optima
No restart tick
No assumptions are needed about the problem
Not required to be differentiable
Solve noisy data as well
SINEXTON Black Box Optimizer
Exceptionally good fault tolerance
We've been stress testing our solution and comparing it against other well-known methods with great success.
Even the most complex functions do not cause problems
x30 - 68 - 47 * cos( x12 ) ^ x30 + 53 * 22 - x5 + 54 - 52 + x30 + 101 + x25 + 54 * x3 * sinh( x30 ) + 75 - x13 ^ 14 * tanh( x30 ) ^ x6 - 46 * x30 ^ 81 * 55 + 97 + x30 + 15 - 47 * x10 - x11 * x20 * x26 * x30 - sinh( x4 ) + x2 - 2 - sinh( x30 ) - 82 * tan( x15 ) * 21 + 5 * 75 * x23 - x30 * cos( x30 ) - 41 + 25 + 12 * 38 + sinh( x17 ) - 75 * 23 * x29 ^ x22 - tan( x7 ) ^ x19 - sinh( x30 ) * x8 * x30 + x18 - 3 - 18 * tan( x30 ) + 64 - 97 * x21 * x30 ^ 53 - sinh( x30 ) - 27 * 50 + 23 + 30 + 24 + 2 * 83 - 73 * 45 - 80 + 89 * x10 + 15 - x4 * tan( x28 ) * 10 + 55 * 10 * exp( x2 ) - 4 + x9 + cosh( x14 ) ^ x24 * 80 + x1 ^ sin( x27 ) ^ x16 * x4 + 49 + 26 = 100
( error 0, time 2.21s )
x1 = 0.43847643622855487
x2 = 0.46929961542364773
x3 = -0.22739104890647494
x4 = 0.5419443297863478
x5 = -0.5487106409433273
x6 = 0.6862159575182608
x7 = 0.3809093553736538
x8 = -0.2790904768113744
x9 = 0.7098757786923025
x10 = 0.07842643908661656
x11 = -0.0817442135773003
x12 = 0.442795812830219
x13 = 0.38427484689331487
x14 = -0.6565246308968888
x15 = -0.8437142735692804
x16 = 0.12579756898717992
x17 = 0.4923442585462639
x18 = 0.523572942374683
x19 = 0.21694979463611894
x20 = -0.09079774813410064
x21 = 0.519963214975034
x22 = 0.6087180159291697
x23 = 0.8651529146552689
x24 = 0.52404542812148
x25 = 0.6183467400711733
x26 = -0.44056998043975537
x27 = 0.17027625860936058
x28 = -0.13984902234792382
x29 = 0.16878526770059354
x30 = 0.1762607949000223
How did we get to this result?
Our technological research and development of inventory optimization needed help in general problem-solving. This is how this solution was created.
The development has taken 2 years.