BEIJING, Sept. 22, 2023 /PRNewswire/ -- MicroAlgo Inc. (NASDAQ: MLGO) (the "Company" or "MicroAlgo"), today announced that a knowledge-enhanced backtracking search algorithm was developed, while the research and development of evolutionary computational methods provided the technical basis for the emergence of the MicroAlgo's knowledge-enhanced backtracking search algorithm. The algorithm aims to improve the efficiency, accuracy and adaptability of problem-solving and provide more possibilities for optimization and decision support for enterprises and research institutions. The development and application of the algorithm is expected to have a significant impact in various fields.

Knowledge-enhanced backtracking search algorithm combines backtracking search strategy and knowledge learning to improve the performance and efficiency of the algorithm. The basis of the Knowledge-enhanced backtracking search algorithm is backtracking search. Backtracking search is an iterative optimization method that starts with one possible solution and then searches for an optimal or near-optimal solution to the problem by progressively adjusting and improving the current solution. At each step, the algorithm tries different alternatives and then evaluates the quality of those alternatives and decides on the next move.

MicroAlgo Inc.'s knowledge-enhanced backtracking search algorithm introduces adaptive control parameters to enable dynamic adjustment of the search step size. The values of these parameters are automatically adjusted based on global and local information about the population in the current iteration. This means that the algorithm is able to flexibly adjust the depth and breadth of the search according to the characteristics of the problem and the progress of the search. This helps to balance the exploration and exploitation capabilities of the algorithm, thus improving search efficiency.

Knowledge-enhanced backtracking search algorithm uses different mutation strategies which are guided by various information. These strategies guide the algorithm to generate new solutions based on prior search experience and domain knowledge. The goal of these strategies is to increase the diversity of the search, help the algorithm to jump out of the local optimal solution and improve the efficiency of the global search. The selection and adaptation of mutation strategies can be based on the nature and needs of the problem.

To further improve the performance of the algorithm, the knowledge-enhanced backtracking search algorithm introduces multiple population strategies. This means that the algorithm can process multiple populations simultaneously and operate in different search regions. Each population can use different parameter settings and search strategies to increase the efficiency of the global search. The multiple population strategy helps the algorithm to better explore the solution space and find better solutions.

The core of MicroAlgo Inc.'s knowledge-enhanced backtracking search algorithm lies in the knowledge-learning mechanism. At each iteration of the algorithm, it accumulates and updates knowledge about the problem. This knowledge include solutions that have been tried, their quality assessments, and information about the structure of the problem. Through knowledge learning, the algorithm is able to converge to better solutions faster because it utilizes the experience of previous searches.

Key points of the technical logic and principles: First, the algorithm initializes the initial solution and sets the initial values of the control parameters. Then in each iteration, the algorithm selects a candidate solution or generates a new solution and evaluates its quality. Among other things, the adaptive control parameters are adjusted based on global and local information to determine the depth and breadth of the search in the next step. Second, the mutation strategy guides the generation of new solutions based on knowledge to increase search diversity. The multi-population strategy allows running multiple populations in parallel to increase the global search efficiency. Finally, the knowledge learning mechanism updates the algorithm's knowledge base with attempted solutions and their evaluations.

The algorithm optimizes the search process of the problem in a highly flexible and intelligent way by means of adaptive control parameters, novel mutation strategies, multi-population strategies, and knowledge-learning mechanisms, thus improving the performance and efficiency of the algorithm. This makes it a powerful tool for dealing with complex optimization problems and decision support.

MicroAlgo Inc.'s knowledge-enhanced backtracking search algorithm is an innovative technology with a vast potential for future development. Knowledge-enhanced backtracking search algorithms will be used in more industries, including healthcare, energy, transportation, retail, and more. Problems and challenges in different industries will drive the algorithms to evolve and improve. With the continuous progress of the technology and the practical application of the algorithms, we can expect the continuous optimization of the knowledge-enhanced backtracking search algorithms, including more efficient search strategies, more flexible knowledge-learning mechanisms, and more powerful multi-group strategies.

In the future, the algorithm may be extended to handle multi-modal problems where there are multiple local optimal solutions. This will involve more complex search spaces and finer-grained strategies. MicroAlgo Inc.'s knowledge-enhanced backtracking search algorithm may be integrated with machine learning and deep learning methods to handle large-scale data and complex problems. This integration could provide more powerful problem-solving capabilities. Further development of algorithms may lead to the emergence of automated decision-support systems that can provide real-time optimization recommendations and decision support to businesses and organizations based on information from real-time data and knowledge bases.

The knowledge-enhanced backtracking search algorithm represents a promising technology that can open up new possibilities for optimization problem-solving and decision making in the enterprise. Through continuous research and innovation, we can expect to see a wider range of applications and more efficient performance of this algorithm in various domains. It will become a key driver of technological innovation for enterprises, bringing more opportunities and competitive advantages for future development.

About MicroAlgo Inc.

MicroAlgo Inc. (the "MicroAlgo"), a Cayman Islands exempted company, is dedicated to the development and application of bespoke central processing algorithms. MicroAlgo provides comprehensive solutions to customers by integrating central processing algorithms with software or hardware, or both, thereby helping them to increase the number of customers, improve end-user satisfaction, achieve direct cost savings, reduce power consumption, and achieve technical goals. The range of MicroAlgo's services includes algorithm optimization, accelerating computing power without the need for hardware upgrades, lightweight data processing, and data intelligence services. MicroAlgo's ability to efficiently deliver software and hardware optimization to customers through bespoke central processing algorithms serves as a driving force for MicroAlgo's long-term development.