WiMi Proposed An Adaptive Learning Backtracking Search Algorithm
- ALBSA improves adaptivity, optimization ability, and searchability of BSA algorithm
- ALBSA introduces adaptive control parameters, novel mutation strategies, and multiple swarm strategies
- Experimental results show competitive and effective performance
- None.
WiMi improved the BSA through ALBSA to make it more competitive in optimizing problems. The goal of ALBSA is to improve the search efficiency and the quality of the solution through the introduction of adaptivity and flexibility, and the full use of global and local information. Experiments have demonstrated that ALBSA has better performance performance relative to traditional BSA and other evolutionary algorithms, allowing for better application and usefulness in real-world problems.
ALBSA proposed by WiMi has the following advantages over traditional BSA:
Adaptability and flexibility: An adaptive control parameter based on the global and local information of the swarms in the current iteration is designed to adjust the search step length of individuals. This allows the algorithm to better balance exploration and exploitation capabilities and adapt to the search needs of different problems.
Optimization capability enhancement: ALBSA introduces a novel mutation strategy based on the guidance of different information, which guides the mutation operation by making full use of the global and local information of the swarm. This enhances the optimization ability of the algorithm, allowing it to search the potential solution space in a more targeted way, improving the quality of the solution and the convergence speed.
Multiple swarm strategy: ALBSA implements a multiple swarm strategy, which means that multiple swarms are maintained at the same time, and each swarm can independently search a specific search region. This enhances the algorithm's ability to search different search regions, thus exploring the solution space more comprehensively and increasing the probability of finding a globally optimal solution.
Competitiveness and effectiveness: Experimental results show that ALBSA is competitive and effective in solving optimization problems compared to traditional BSA and other optimization algorithms. It is able to find solutions with better performance higher convergence speed and search efficiency.
Overall, ALBSA improves on the traditional BSA by improving the adaptivity, optimization ability and searchability of the algorithm through the introduction of adaptive control parameters, novel mutation strategies and multiple swarm strategies. These advantages make ALBSA more application potential and effective in solving various optimization problems.
WiMi's ALBSA, aims to optimize the BSA. ALBSA optimizes the search step size and optimization capability by designing adaptive control parameters and novel mutation strategies, and implements multiple swarm strategies to enhance the search capability for different search regions. The technical framework of ALBSA is as follows:
Initialization: Initialize the population and individuals, and set the initial values of the control parameters. Set other parameters of the algorithm, such as a maximum number of iterations, population size, etc.
Swarm information update: In each iteration, global information about the swarm is calculated based on the fitness of the current population, e.g., average fitness and optimal fitness. Local information of the swarm is computed based on the fitness of individuals, e.g., the relative fitness between an individual and its neighbors.
Adaptive tuning of control parameters: Adaptively adjusting control parameters based on global and local information about the swarm in the current iteration. Adjusting the control parameters can change the individual search step size to balance the exploration and exploitation capabilities of the algorithm.
New mutation strategy: Design a novel mutation strategy based on different information guidance to improve the optimization ability of the algorithm. The novel mutation strategy can determine the direction and magnitude of mutation based on the global and local information of the swarm in the current iteration.
Update and evaluation of solutions: Use control parameters and mutation strategies to update individual solutions. The updated solutions are evaluated and fitness values are calculated.
Termination condition check: Check whether the termination condition is satisfied, such as reaching the maximum number of iterations or finding a satisfactory solution. If the termination condition is satisfied, the algorithm ends; otherwise, return to step 2 for the next round of iteration.
ALBSA continuously adjusts the control parameters and mutation strategies to utilize global and local information to guide the search in order to improve the overall performance of the algorithm. Multiple swarm strategies further enhance the algorithm's ability to search different search regions. Through experimental verification, ALBSA is competitive and effective compared with other evolutionary algorithms to find better solutions.
WiMi's ALBSA has a broad market prospect in the field of optimizing problem solutions. For example, in logistics and transportation, problems such as optimizing route planning, vehicle scheduling, and distribution paths are critical for improving logistics efficiency and reducing transportation costs. ALBSA can be applied to these problems to provide optimal path planning and resource scheduling to optimize logistics and transportation networks. In the field of data analysis and machine learning ALBSA has potential for feature selection, model parameter tuning and model optimization in the field of data analysis and machine learning. With the rapid growth of data and the increase of complex problems, more efficient optimization algorithms are needed to improve the performance of data analytics and machine learning models. The industrial and manufacturing sectors often face complex optimization problems, such as production scheduling, resource allocation, and supply chain optimization, etc. ALBSA can provide efficient and accurate solutions to these problems, helping companies improve productivity, reduce costs, and optimize resource utilization.
In addition, ALBSA, as an optimization algorithm with superior performance, has a broad market prospect in several fields. Such as artificial intelligence and intelligent systems, finance and investment, energy and environment, healthcare, e-commerce, smart cities, etc., all of which have application potential. It can solve various optimization problems, provide better decision support and solutions, and promote the development and efficiency of various industries.
WiMi's ALBSA can be applied to a wide range of problems that require optimization for solutions. It is flexible and versatile, and can be adapted and optimized to specific problems for better performance and results. ALBSA As the complexity of problems and the size of data increase, the demand for efficient and accurate optimization algorithms will grow further, and ALBSA is expected to become an important tool and solution provider in these fields.
About WIMI Hologram Cloud
WIMI Hologram Cloud, Inc. (NASDAQ:WIMI) is a holographic cloud comprehensive technical solution provider that focuses on professional areas including holographic AR automotive HUD software, 3D holographic pulse LiDAR, head-mounted light field holographic equipment, holographic semiconductor, holographic cloud software, holographic car navigation and others. Its services and holographic AR technologies include holographic AR automotive application, 3D holographic pulse LiDAR technology, holographic vision semiconductor technology, holographic software development, holographic AR advertising technology, holographic AR entertainment technology, holographic ARSDK payment, interactive holographic communication and other holographic AR technologies.
Safe Harbor Statements
This press release contains "forward-looking statements" within the Private Securities Litigation Reform Act of 1995. These forward-looking statements can be identified by terminology such as "will," "expects," "anticipates," "future," "intends," "plans," "believes," "estimates," and similar statements. Statements that are not historical facts, including statements about the Company's beliefs and expectations, are forward-looking statements. Among other things, the business outlook and quotations from management in this press release and the Company's strategic and operational plans contain forward−looking statements. The Company may also make written or oral forward−looking statements in its periodic reports to the US Securities and Exchange Commission ("SEC") on Forms 20−F and 6−K, in its annual report to shareholders, in press releases, and other written materials, and in oral statements made by its officers, directors or employees to third parties. Forward-looking statements involve inherent risks and uncertainties. Several factors could cause actual results to differ materially from those contained in any forward−looking statement, including but not limited to the following: the Company's goals and strategies; the Company's future business development, financial condition, and results of operations; the expected growth of the AR holographic industry; and the Company's expectations regarding demand for and market acceptance of its products and services.
Further information regarding these and other risks is included in the Company's annual report on Form 20-F and the current report on Form 6-K and other documents filed with the SEC. All information provided in this press release is as of the date of this press release. The Company does not undertake any obligation to update any forward-looking statement except as required under applicable laws.
View original content:https://www.prnewswire.com/news-releases/wimi-proposed-an-adaptive-learning-backtracking-search-algorithm-301940070.html
SOURCE WiMi Hologram Cloud Inc.
