ADVANCED HEURISTIC-BASED ALGORITHMIC SYSTEM FOR REAL-TIME NON-LINEAR OPTIMIZATION ACROSS ADAPTIVE DOMAINS

Abstract

ADVANCED HEURISTIC-BASED ALGORITHMIC SYSTEM FOR REAL-TIME NON-LINEAR OPTIMIZATION ACROSS ADAPTIVE DOMAINS ABSTRACT The present invention relates to a heuristic-based algorithmic system 100 designed for real-time optimization of non-linear systems across adaptive domains. The system comprises a data acquisition module 110 configured to gather real-time data, including system parameters, performance metrics, and environmental conditions from multiple non-linear systems. An adaptive heuristic engine 112 processes the acquired data using adaptive algorithms to determine optimal solutions for complex non-linear optimization problems. A decision-making module 114 evaluates the performance of the non-linear systems based on the outputs of the adaptive heuristic engine 112, generating actionable insights for system adjustments. The system also includes a feedback mechanism 116 that continuously refines the adaptive heuristic engine by integrating feedback from the decision-making module 114 and responding to changes in operating conditions. An integration interface 118 enables seamless communication between the algorithmic system and external control systems, allowing for real-time implementation of optimized parameters. This invention enhances dynamic optimization in complex, real-time environments.

Specification

Description:ADVANCED HEURISTIC-BASED ALGORITHMIC SYSTEM FOR REAL-TIME NON-LINEAR OPTIMIZATION ACROSS ADAPTIVE DOMAINS

FIELD OF THE INVENTION

[001] Various embodiments of the present invention generally relate to adaptive domains. More particularly, the invention relates to an advanced heuristic-based algorithmic system for real-time non-linear optimization across adaptive domains.

BACKGROUND OF THE INVENTION

[002] The optimization of non-linear systems is a critical challenge in various fields, including engineering, manufacturing, logistics, and artificial intelligence. Non-linear systems exhibit complex behaviors where changes in input variables do not result in proportional changes in output, making traditional linear optimization techniques inadequate for solving such problems. As non-linear systems become more prevalent in industries like aerospace, robotics, and power systems, the need for more sophisticated and dynamic optimization methods has grown.

[003] Traditional optimization techniques often rely on static models and predefined algorithms, which can struggle to cope with the dynamic and unpredictable nature of real-world non-linear systems. These methods may fail to adapt to fluctuating environmental conditions or unexpected system changes, resulting in suboptimal performance, higher operational costs, and reduced system efficiency.

[004] Heuristic-based algorithms, such as genetic algorithms, swarm intelligence, and simulated annealing, have shown potential in addressing the complexities of non-linear systems. These algorithms use exploratory and adaptive techniques to search for optimal solutions across a broader range of possibilities. However, current heuristic methods often lack the real-time adaptability needed to handle continuously changing environments or multi-domain systems effectively.

[005] Moreover, the lack of seamless integration between optimization frameworks and control systems hampers the ability to implement optimal solutions in real-time. As a result, there is a need for an advanced system capable of real-time optimization that can adapt dynamically to changing conditions while integrating easily with external control systems.

[006] The present invention addresses these issues by providing a heuristic-based algorithmic system for real-time optimization of non-linear systems across adaptive domains. It leverages adaptive heuristic algorithms, real-time data acquisition, continuous feedback mechanisms, and seamless system integration to optimize non-linear systems effectively and efficiently in various operational environments. This invention aims to overcome the limitations of existing optimization techniques by providing a more responsive, adaptable, and high-performance solution for complex non-linear optimization challenges.
SUMMARY OF THE INVENTION

[007] The present invention discloses a heuristic-based algorithmic system for real-time optimization of non-linear systems across adaptive domains. The system includes a data acquisition module configured to gather real-time data from multiple non-linear systems, including system parameters, performance metrics, and environmental conditions. This data is processed by an adaptive heuristic engine, which utilizes advanced adaptive algorithms, such as genetic algorithms and swarm intelligence, to identify optimal solutions to complex non-linear optimization problems.
[008] The invention further comprises a decision-making module that evaluates the performance of the non-linear systems based on the results of the adaptive heuristic engine, generating actionable insights for system adjustments. A feedback mechanism ensures continuous learning and refinement of the heuristic engine by incorporating feedback from system performance and environmental changes. Additionally, an integration interface enables seamless communication with external control systems, allowing for the real-time implementation of optimized parameters.

[009] This system offers significant advantages in terms of dynamic adaptability, continuous optimization, and scalability, making it particularly effective for industries dealing with complex non-linear systems.

[010] One or more advantages of the prior art are overcome, and additional advantages are provided through the invention. Additional features are realized through the technique of the invention. Other embodiments and aspects of the disclosure are described in detail herein and are considered a part of the invention.

BRIEF DESCRIPTION OF THE FIGURES

[011] The accompanying figures where like reference numerals refer to identical or functionally similar elements throughout the separate views and which together with the detailed description below are incorporated in and form part of the specification, serve to further illustrate various embodiments and to explain various principles and advantages all in accordance with the invention.

[012] FIG. 1 is a diagram that illustrates a heuristic-based algorithmic system for real-time optimization of non-linear systems across adaptive domains, in accordance with an embodiment of the invention.
[013] FIG. 2 is a diagram that illustrates a flow diagram with a method for real-time optimization of non-linear systems across adaptive domains using a heuristic-based algorithmic system 100, in accordance with an embodiment of the invention.

[014] Skilled artisans will appreciate the elements in the figures are illustrated for simplicity and clarity and have not necessarily been drawn to scale. For example, the dimensions of some of the elements in the figures may be exaggerated relative to other elements to help to improve understanding of embodiments of the present invention.

DETAILED DESCRIPTION OF THE INVENTION

[015] While various embodiments of the invention have been shown and described herein, it will be obvious to those skilled in the art that such embodiments are provided by way of example only. Numerous variations, changes, and substitutions may occur to those skilled in the art without departing from the invention. It should be understood that various alternatives to the embodiments of the invention described herein may be employed. It shall be understood that different aspects of the invention can be appreciated individually, collectively, or in combination with each other.

[016] FIG. 1 is a diagram that illustrates a heuristic-based algorithmic system 100 for real-time optimization of non-linear systems across adaptive domains, in accordance with an embodiment of the invention.

[017] Referring to FIG. 1, the system 100 the comprises a memory 102, a processor 104, one or more communication interfaces 106, a communication bus 108, a data acquisition module 110, an adaptive heuristic engine 112, a decision-making module 114, a feedback mechanism 116, and an integration interface 118.

[018] The memory 102 often referred to as RAM (Random Access Memory), is the component of a computer system that provides temporary storage for data and instructions that the processor needs to access quickly. It holds the information required for running programs and performing calculations. The memory 102 can be thought of as a workspace where the processor can read from and write to data.

[019] The processor 104 referred to as the Central Processing Unit (CPU), is the "brain" of the computer system. It carries out instructions, performs calculations, and manages the flow of data within the system. The processor 104 fetches instructions and data from memory, processes them, and produces results.

[020] The one or more communication interfaces 106 refer to the various methods and protocols used to transfer data between different systems, devices, or components. These interfaces can be hardware-based, software-based, or a combination of both.

[021] The memory 102 and the processor 104 are connected through buses, which are electrical pathways for transferring data and instructions.

[022] The communication bus 108 plays a vital role in enabling effective and efficient communication within a system. It establishes the foundation for exchanging information, coordinating actions, and synchronizing operations among different components, ensuring the system functions as an integrated whole.

[023] In an embodiment, a heuristic-based algorithmic system 100 is provided for real-time optimization of non-linear systems across adaptive domains. The system 100 is designed to dynamically process real-time data and optimize complex, non-linear systems in various operational environments.

[024] The system 100 includes a data acquisition module 110, which is configured to gather real-time data from multiple non-linear systems. This data includes, but is not limited to, system parameters such as input-output relationships, performance metrics like efficiency and response time, and external environmental conditions such as temperature, pressure, and humidity. In an embodiment, the data acquisition module 110 may interface with sensors and external data sources to ensure comprehensive data collection.

[025] The gathered data is then processed by an adaptive heuristic engine 112, which employs a set of adaptive algorithms, such as genetic algorithms, simulated annealing, or swarm intelligence techniques, to identify optimal solutions for non-linear optimization problems. The adaptive heuristic engine 112 is configured to dynamically adjust its processing techniques based on the complexity of the non-linear systems and the data it receives, ensuring high efficiency in identifying optimal parameters.

[026] A decision-making module 114 is integrated into the system 100 to evaluate the performance of the non-linear systems based on the outputs of the adaptive heuristic engine 112. The decision-making module 114 analyzes the optimization results and generates actionable insights, which may include recommendations for adjusting system parameters or control strategies in real time. This ensures that the non-linear systems continuously operate at peak performance levels.

[027] The system 100 also comprises a feedback mechanism 116, which plays a crucial role in enabling continuous learning and refinement of the adaptive heuristic engine 112. The feedback mechanism 116 collects real-time feedback from the decision-making module 114 and monitors changes in the system's operational conditions. This feedback is used to update the adaptive algorithms within the heuristic engine, allowing the system 100 to refine its optimization strategies and improve performance over time.

[028] Finally, an integration interface 118 is provided to facilitate seamless communication between the algorithmic system 100 and external control systems. The integration interface 118 enables the real-time implementation of optimized parameters and ensures that any adjustments recommended by the decision-making module 114 are applied immediately to the non-linear systems. This interface supports various communication protocols to ensure compatibility with a wide range of control systems and devices.

[029] In operation, the system 100 allows for continuous real-time optimization of non-linear systems across adaptive domains, leveraging advanced heuristic techniques to handle complex optimization problems efficiently. The combination of real-time data acquisition, adaptive algorithms, decision-making capabilities, and continuous feedback enables the system to achieve high levels of performance in dynamic and variable environments.

[030] FIG. 2 is a diagram that illustrates a flow diagram 200 with a method for real-time optimization of non-linear systems across adaptive domains using a heuristic-based algorithmic system 100, in accordance with an embodiment of the invention.

[031] In an embodiment, a method for real-time optimization of non-linear systems across adaptive domains using a heuristic-based algorithmic system 100 is disclosed. The method comprises the following steps:

[032] At step 202, real-time data is acquired from multiple non-linear systems through a data acquisition module 110. The real-time data includes system parameters such as operational metrics, performance indicators, and environmental conditions like temperature and pressure. The data acquisition module 110 is equipped to interface with various sensors and external sources to ensure a comprehensive and accurate capture of the system's state and external influences.

[033] At step 204, the acquired data is processed by an adaptive heuristic engine 112. The adaptive heuristic engine 112 applies a set of adaptive algorithms, such as genetic algorithms, swarm intelligence, or simulated annealing, to identify optimal solutions for non-linear optimization problems. In an embodiment, the heuristic engine 112 adjusts the search space and optimization techniques based on the complexity of the non-linear system and the incoming data, allowing for real-time optimization under changing conditions.

[034] At step 206, the performance of the non-linear systems is evaluated using a decision-making module 114. The decision-making module 114 analyzes the optimization outputs generated by the adaptive heuristic engine 112, comparing the results with the system's operational objectives. Based on this evaluation, the decision-making module 114 generates actionable insights, such as suggested changes to system parameters or control strategies. These insights are crucial for ensuring that the non-linear systems operate at their peak efficiency and performance.

[035] At step 208, a feedback mechanism 116 is used to enable continuous learning and refinement of the adaptive heuristic engine 112. The feedback mechanism 116 monitors the changes in system conditions and gathers real-time feedback from the decision-making module 114. This feedback loop continuously updates the adaptive algorithms within the heuristic engine 112, allowing for improved decision-making and more effective optimization over time.

[036] At step 210, the optimized parameters, generated as a result of the decision-making module's analysis, are implemented in real time via an integration interface 118. The integration interface 118 ensures seamless communication between the heuristic-based system 100 and external control systems. This allows the optimized system parameters to be immediately applied to the non-linear systems, enabling them to respond dynamically to changes in operating conditions or objectives. The integration interface 118 supports various protocols, ensuring compatibility with different control architectures and enabling a smooth real-time implementation process.

[037] The method enables continuous real-time optimization of non-linear systems by leveraging the capabilities of adaptive heuristic algorithms, performance evaluation, continuous feedback, and seamless integration with external control systems. This dynamic and real-time optimization allows for better handling of complex non-linear optimization problems and ensures efficient operation in constantly changing environments.
[038] Dynamic Adaptability: The system can continuously adjust its optimization strategies based on real-time data, allowing it to respond effectively to changing conditions and operational requirements.

[039] Enhanced Performance: By employing advanced adaptive algorithms, the system identifies optimal solutions for complex non-linear problems, leading to improved efficiency and performance of the non-linear systems.

[040] Real-Time Data Processing: The integration of real-time data acquisition allows for immediate analysis and decision-making, facilitating timely adjustments that enhance system responsiveness.

[041] Continuous Learning: The feedback mechanism enables the system to refine its algorithms and strategies based on past performance, promoting ongoing improvement and adaptability over time.

[042] Actionable Insights: The decision-making module generates clear and actionable recommendations based on the optimization results, aiding users in implementing effective changes to system parameters.

[043] Seamless Integration: The integration interface supports compatibility with various external control systems, ensuring that the optimized parameters can be applied without disruption to ongoing operations.

[044] Scalability: The system is designed to handle multiple non-linear systems simultaneously, making it suitable for large-scale applications across different industries.

[045] Reduced Operational Costs: By optimizing performance in real time, the system can help minimize energy consumption and resource usage, ultimately leading to lower operational costs.

[046] Robustness in Complex Environments: The system's ability to manage complex, non-linear dynamics makes it valuable for applications in industries such as manufacturing, aerospace, and smart cities, where unpredictable conditions are common.

[047] User-Friendly Visualization: The implementation of a real-time monitoring dashboard within the feedback mechanism can provide users with intuitive visual insights into system performance, making it easier to understand and act upon optimization results.

[048] Those skilled in the art will realize that the above-recognized advantages and other advantages described herein are merely exemplary and are not meant to be a complete rendering of all of the advantages of the various embodiments of the present invention.

[049] In the foregoing complete specification, specific embodiments of the present invention have been described. However, one of ordinary skill in the art appreciates that various modifications and changes can be made without departing from the scope of the present invention. Accordingly, the specification and the figures are to be regarded in an illustrative rather than a restrictive sense. All such modifications are intended to be included with the scope of the present invention and its various embodiments.
, Claims:I/WE CLAIM:
1. A heuristic-based algorithmic system 100 for real-time optimization of non-linear systems across adaptive domains, comprising:
a data acquisition module 110 configured to gather real-time data from multiple non-linear systems, wherein the data includes system parameters, performance metrics, and environmental conditions;
an adaptive heuristic engine 112 that processes the gathered data using a set of adaptive algorithms to identify optimal solutions for non-linear optimization problems;
a decision-making module 114 that evaluates the performance of the non-linear systems based on the outputs of the adaptive heuristic engine and generates actionable insights for system adjustments;
a feedback mechanism 116 that enables continuous learning and refinement of the adaptive heuristic engine by incorporating feedback from the decision-making module and changes in system conditions; and
an integration interface 118 that facilitates seamless communication between the algorithmic system and external control systems for implementing the optimized parameters in real-time.
2. The heuristic-based algorithmic system 100 of Claim 1, wherein the data acquisition module 110 further includes a sensor network configured to capture and transmit environmental data, such as temperature, humidity, and pressure, enhancing the adaptability of the system.
3. The heuristic-based algorithmic system 100 of Claim 1, wherein the adaptive heuristic engine 112 employs multi-fidelity modeling techniques that allow for a trade-off between computational efficiency and solution accuracy in identifying optimal parameters for the non-linear systems.
4. The heuristic-based algorithmic system 100 of Claim 1, wherein the decision-making module 114 utilizes machine learning algorithms to analyze historical performance data and improve the predictive capabilities of the system for future optimizations.
5. The heuristic-based algorithmic system 100 of Claim 1, wherein the feedback mechanism 116 is configured to implement a real-time monitoring dashboard that visualizes system performance and optimization outcomes, providing users with intuitive insights and control options.
6. A method for real-time optimization of non-linear systems across adaptive domains using a heuristic-based algorithmic system 100, the method comprising:
Acquiring real-time data from multiple non-linear systems via a data acquisition module 110, wherein the data includes system parameters, performance metrics, and environmental conditions;
Processing the acquired data using an adaptive heuristic engine 112, wherein the adaptive heuristic engine applies a set of adaptive algorithms to identify optimal solutions for non-linear optimization problems;
Evaluating the performance of the non-linear systems using a decision-making module 114, based on the outputs of the adaptive heuristic engine 112, and generating actionable insights for adjusting system parameters;
Providing continuous feedback via a feedback mechanism 116, wherein the feedback mechanism refines the adaptive heuristic engine by incorporating real-time system performance data and changes in operating conditions; and
Integrating the optimized parameters into the control systems of the non-linear systems through an integration interface 118, enabling real-time implementation of the optimization results.
7. The method of Claim 6, wherein the step of acquiring real-time data further comprises aggregating data from distributed sensor networks and external data sources, allowing for a comprehensive analysis of both system-specific and environmental factors influencing the non-linear systems.
8. The method of Claim 6, wherein the step of processing the acquired data using the adaptive heuristic engine 112 includes the application of genetic algorithms and simulated annealing to enhance the search for globally optimal solutions across complex, non-linear optimization problems.
9. The method of Claim 6, wherein the step of evaluating the performance includes the use of predictive analytics and historical data analysis, enabling the system to anticipate system behaviors and preemptively adjust the optimization strategy.
10. The method of Claim 6, wherein the step of providing continuous feedback includes dynamic threshold adjustments in the feedback mechanism 116, allowing the system to adapt to changing system conditions and refine the optimization process in real-time without human intervention.

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