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A Self-Adaptive Improved slime mold algorithm for Multi-UAV Path Planning

DRONES 2025年第3期9卷 219-219页

作者： Ma, Yuelin Zhang, Zeren Yao, Meng Fan, Guoliang Chinese Acad Sci Inst Automat Key Lab Cognit & Decis Intelligence Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100190 Peoples R China

Multi-UAV path planning presents a critical challenge in Unmanned Aerial Vehicle (UAV) applications, particularly in environments with various obstacles and restrictions. These conditions transform multi-UAV path planning into a complex optimization problem with multiple constraints, significantly reducing the number of feasible solutions and complicating the generation of optimal flight trajectories. Although the slime mold algorithm (SMA) has proven effective in optimization missions, it still suffers from limitations such as inadequate exploration capacity, premature convergence, and a propensity to become stuck in local optima. These drawbacks degrade its performance in intricate multi-UAV scenarios. This study proposes a self-adaptive improved slime mold algorithm called AI-SMA to address these issues. Firstly, AI-SMA incorporates a novel search mechanism to balance exploration and exploitation by integrating ranking-based differential evolution (rank-DE). Then, a self-adaptive switch operator is introduced to increase population diversity in later iterations and avoid premature convergence. Finally, a self-adaptive perturbation strategy is implemented to provide an effective escape mechanism, facilitating faster convergence. Extensive experiments were conducted on the CEC 2017 benchmark test suite and multi-UAV path-planning scenarios. The results show that AI-SMA improves the quality of optimal fitness by approximately 7.83% over the original SMA while demonstrating superior robustness and effectiveness in generating collision-free trajectories.

关键词： multiple unmanned aerial vehicles path planning slime mold algorithm meta-heuristic algorithm

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slime mold algorithm for topology optimization: metagratings inverse design

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DISCOVER APPLIED SCIENCES 2024年第2期6卷 68页

作者： Edee, Kofi Granet, Gerard Bonnet, Pierre Univ Clermont Auvergne Inst Pascal Clermont Auvergne INP CNRS F-63000 Clermont Ferrand France

In this paper we discuss the use of a metaheuristic (MH) gradient-free optimization method, specifically the slime mold algorithm (SMA), combined with the topology optimization (TO) method to design metasurfaces using a spectral modal method. The motivation behind using a MH approach comes from the drawbacks associated with traditional gradient-based methods. Normally, gradient-based methods require the calculation of the electromagnetic (EM) field at certain nodes within the computation domain. However, in spectral modal methods, this is unnecessary since these methods can compute the EM response without the need for field component values. Second, optimizing metagratings often involves a multimodal objective function with multiple local minimums and gradient-based methods might struggle with finding the global optimum. So to overcome these drawbacks, we propose using a MH approach, specifically the slime mold algorithm (SMA). We apply SMA to a metasurface design, especially in the context of TO and spectral methods, which is relatively unexplored. By coupling both TO with SMA, we successfully design metagratings capable of deflecting incident waves into a desired transmission angle. The slime mold algorithm is inspired by the behavior of slime molds, which are simple organisms with unique problem-solving abilities. Work presented in this paper explore the application of slime mold algorithm to design functional metasurfaces in the framework of topology optimization. The experimental results suggest that the proposed method has considerable potential, offering new possibilities for tackling intricate optimization challenges within the field of photonics.

关键词： Topology optimization slime mold algorithm Metagratings

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The optimal synthesis of thinned concentric circular antenna arrays using slime mold algorithm

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ELECTROMAGNETICS 2020年第8期40卷 541-553页

作者： Durmus, Ali Kayseri Univ Vocat Coll Dept Elect & Energy 15 Temmuz Yerleskesi TR-38280 Kayseri Turkey

In this paper, thinned concentric circular antenna arrays (CCAAs) with low sidelobe levels and fixed half-power beamwidths are synthesized by using the slime mold algorithm (SMA). SMA is a novel stochastic optimization method inspired by the oscillation mode of the slime mold in nature. Entering the literature as a new optimization technique, SMA is based on a matchless mathematical model that finds the most suitable way to find food in the search space thanks to the negative and positive oscillations of the slime mold. SMA is also used to obtain thinned CCAAs having different ring numbers. It is seen that the results obtained by SMA are very good. Besides, the flexibility of SMA in different values of parameters is a promising feature for the other antenna array synthesis problems. The results obtained by SMA are compared with several meta-heuristic algorithms in the literature. SMA shows a better performance in synthesizing CCAAs than the other compared algorithms.

关键词： Antenna array synthesis concentric circular antenna array evolutionary algorithms slime mold algorithm thinning

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A novel threshold score based multiclass segmentation technique for brain magnetic resonance images using adaptive opposition slime mold algorithm

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INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY 2022年第4期32卷 1397-1413页

作者： Naik, Manoj Kumar Panda, Rutuparna Samantaray, Leena Abraham, Ajith Siksha O Anusandhan Fac Engn & Technol Bhubaneswar Odisha India Veer Surendra Sai Univ Technol Dept Elect & Telecommun Engn Burla 768018 Odisha India Ajay Binay Inst Technol Dept Elect & Commun Engn Cuttack Odisha India Sci Network Innovat & Res Excellence Machine Intelligence Res Labs Auburn WA USA Innopolis Univ Ctr Artificial Intelligence Innopolis Russia

Clinicians can detect diseases early, thanks to the digital image processing methodologies, which improve health together with the treatment experience. The technology of magnetic resonance imaging (MRI) is frequently employed in the brain, research for any kind of related illness. The brain MR image requires precise automated thresholding for a meaningful representation to aid doctors, because of its different modalities and complexity. The majority of the threshold selection strategies are based on entropy. However, these strategies are limited by their reliance on the spatial distribution of gray values. There is also a pressing need to develop a thresholding technique that is independent of the spatial distribution, making it more suitable for a variety of modalities and complexity, such as the brain MR images. A novel non-entropic maximizing objective function for the multilevel thresholding approach using a threshold score (TS) is presented in this paper, to address these concerns. An evolutionary TS-AOSMA approach, using the optimizer called adaptive opposition slime mold algorithm (AOSMA), is suggested to lower the computational cost of TS-based multiclass segmentation, which is a novel idea. The proposed approach is evaluated on T2-weighted brain MR imaging slices from Harvard Medical School's whole brain atlas dataset. When compared to the state-of-the-art Kapur's, Tsallis, and Masi entropy-based technologies, the proposed scheme offered better quantitative and qualitative outcomes. The recommended strategies may be useful in medical image analysis.

关键词： brain MR image evolutionary computing multilevel thresholding slime mold algorithm

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An autonomous mobile robot path planning strategy using an enhanced slime mold algorithm

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FRONTIERS IN NEUROROBOTICS 2023年 17卷 1270860页

作者： Zheng, Ling Hong, Chengzhi Song, Huashan Chen, Rong Huazhong Univ Sci & Technol Sch Elect Informat & Commun Wuhan Peoples R China Cent China Normal Univ Shenzhen Res Inst Shenzhen Peoples R China Wuhan Univ State Key Lab Informat Engn Surveying Mapping & Re Wuhan Peoples R China China Mobile Intelligent Mobil Network Technol Co Space Time Informat Dept Wuhan Peoples R China Wuhan Tech Coll Commun Inst Traff Engn Wuhan Peoples R China

IntroductionAutonomous mobile robot encompasses modules such as perception, path planning, decision-making, and control. Among these modules, path planning serves as a prerequisite for mobile robots to accomplish tasks. Enhancing path planning capability of mobile robots can effectively save costs, reduce energy consumption, and improve work efficiency. The primary slime mold algorithm (SMA) exhibits characteristics such as a reduced number of parameters, strong robustness, and a relatively high level of exploratory ability. SMA performs well in path planning for mobile robots. However, it is prone to local optimization and lacks dynamic obstacle avoidance, making it less effective in real-world *** paper presents an enhanced SMA (ESMA) path-planning algorithm for mobile robots. The ESMA algorithm incorporates adaptive techniques to enhance global search capabilities and integrates an artificial potential field to improve dynamic obstacle *** and discussionCompared to the SMA algorithm, the SMA-AGDE algorithm, which combines the Adaptive Guided Differential Evolution algorithm, and the Levy Flight-Rotation SMA (LRSMA) algorithm, resulted in an average reduction in the minimum path length of (3.92%, 8.93%, 2.73%), along with corresponding reductions in path minimum values and processing times. Experiments show ESMA can find shortest collision-free paths for mobile robots in both static and dynamic environments.

关键词： autonomous mobile robots path planning slime mold algorithm dynamic environment artificial potential field

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Application of slime mold algorithm to optimize time, cost and quality in construction projects

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INTERNATIONAL JOURNAL OF CONSTRUCTION MANAGEMENT 2024年第13期24卷 1375-1386页

作者： Son, Pham Vu Hong Khoi, Luu Ngoc Quynh Vietnam Natl Univ Ho Chi Minh City Univ Technol HCMUT Construct Engn & Management Dept Ho Chi Minh City Vietnam Ho Chi Minh City Univ Technol HCMUT Vietnam Natl Univ Construct Engn & Management Dept Ho Chi Minh City 700000 Vietnam Vietnam Natl Univ Ho Chi Minh City Linh Trung Ward Ho Chi Minh City Vietnam

Numerous optimization techniques have been proposed for use in order to address issues with various building project components. It has become easier to solve difficult optimization issues in practice due to the development of numerous innovative algorithms in recent years. The utilization of the slime mold algorithm model to optimize time, cost, and quality trade-off problems (TCQT) in this article. The effectiveness of the SMA model was analyzed, evaluated, and compared with the outcomes produced by the opposition-based multi-objective differential evolution (OMODE), nondominated sorting genetic algorithm II (NSGA-II), multiobjective particle swarm optimization (MOPSO), and multi-objective particle swarm optimization (MODE). The aforementioned results demonstrated the model's potential as a strategy by substituting the three - factor approach and processing-time optimal. The management of project time, cost and quality is decisive for investment management activities.

关键词： slime mold algorithm artificial intelligence optimization

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Maiden Application of the slime mold algorithm for Optimal Operation of Energy Management on a Microgrid Considering Demand Response Program

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SN Computer Science 2023年第5期4卷 1-16页

作者： Behera, Sadasiva Dev Choudhury, Nalin B. Biswas, Satyaki Department of Electrical Engineering NIT Silchar Silchar 788010 India

Microgrids are cutting-edge technology that helps electricity systems incorporate with renewable energy sources (RESs). Due to the intermittent nature of RESs and the continuous variation in their output levels, operating them optimally in isolated microgrids is a tedious effort. This study examines how to operate an autonomous microgrid at its best while taking the demand response program (DRP) into account. To reduce operating expenses and emissions, a model for managing energy has been presented with two different scenarios. In DRPs, interruptible and curtailable loads are taken into account. In addition, the focus of this study is on employing the slime mold meta-heuristic algorithm for the operation management of microgrids due to the growing concern with establishing effective optimization methods and algorithms in line with the expanding needs of microgrids. According to the results, the slime mold algorithm method performs better than other well-known algorithms such as particle swarm optimization and genetic algorithms. In addition, the findings demonstrate that the adoption of DRPS significantly affects operating expenses and emissions. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Energy management systems Incentive-based demand response Microgrid Renewable energy sources integrations slime mold algorithm

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An Improved slime mold algorithm and its Application for Optimal Operation of Cascade Hydropower Stations

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IEEE ACCESS 2020年 8卷 226754-226772页

作者： Nguyen, Trong-The Wang, Hong-Jiang Dao, Thi-Kien Pan, Jeng-Shyang Liu, Jian-Hua Weng, Shaowei Fujian Univ Technol Fujian Prov Key Lab Big Data Min & Applicat Fuzhou 350118 Peoples R China Haiphong Univ Management & Technol Dept Informat Technol Haiphong 18000 Vietnam Shandong Univ Sci & Technol Coll Comp Sci & Engn Qingdao 266510 Peoples R China

A recently modern stochastic optimization algorithm has been developed by observing the life of slime mold physarum polycephalum in nature. The algorithm is called the slime mold algorithm (SMA) with an excellent exploratory capacity and exploitation inclination. Still, slipping into optimal local is easy to happen and slowly converges speed while dealing with complicated problems. This article proposes a new process of improving SMA (namely ISMA) by adapting the weight coefficient and cooperating the reverse learning strategy in the expression of agents updating locations to enhance the algorithm's optimization performance. Many selected benchmark functions and the optimal operation of cascade reservoirs are applied to evaluate the performance of the proposed algorithm. Comparisons of the proposed approach's results with the various algorithms under the case situations show that the recommended solution produces better performance than the different competing algorithms.

关键词： Heuristic algorithms Reservoirs Hydroelectric power generation Optimization Classification algorithms Veins Particle swarm optimization slime mold algorithm optimal dispatching of cascade hydropower reverse learning dynamic weight

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Optimization time-cost-quality-work continuity in construction management using mutation-crossover slime mold algorithm

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APPLIED SOFT COMPUTING 2023年 147卷

作者： Son, Pham Vu Hong Khoi, Luu Ngoc Quynh Vietnam Natl Univ Ho Chi Minh City Univ Technol HCMUT Construct Engn & Management Dept Ho Chi Minh 700000 Vietnam

This study presents the mutation-crossover slime mold algorithm (MCSMA) to balance the time, cost, quality, and work continuity in a particular construction project. The slime mold algorithm is combined with the mutation-crossover method to modify the operating mechanism and increase the ability of finding optimal solutions in the exploration and exploitation space during the optimization process. The optimal exchange problem considers all the logical aspects of occurring activities and the obvious reasoning applied to choose a compromise solution in the project implementation. The MCSMA is compared with five well-known algorithms (i.e., OMOSOS, MOABC, MODE, MOPSO, and NSGA-II) to verify the effectiveness and performance of the proposed model. According to the analysis results, the MCSMA generates a diversification measure for both case studies with 0.501 and 0.485 C-Metric, 0.785 and 0.923 Spread, and 0.843 and 0.806 Hyper-volume. The algorithmic model represents development, improvement, and diversification in the problem of achieving model convergence and wide distribution and gives a better uniformity of optimal solutions compared to the other algorithms. (c) 2023 Elsevier B.V. All rights reserved.

关键词： Mutation-crossover slime mold algorithm Time-cost-quality-work continuity trade-off problem slime mold algorithm Repetitive projects

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An Improved Solution to Generation Scheduling Problem Using slime mold algorithm

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FRONTIERS IN ENERGY RESEARCH 2022年 10卷

作者： Zhu, Zixuan China Three Gorges Univ Yichang Peoples R China

The slime mold algorithm (SMA) is a novel meta-heuristic search that replicates the characteristics of slime mold during oscillation. This is presented in a novel mathematical formulation that employs changeable weights to modify the sequence of both negative and positive propagation waves in order to build a mechanism for linking food availability with intensive exploration capacity and exploitation affinity. The study demonstrates how to solve a non-convex and cost-effective load dispatch issue (ELD) in an electric power system using the SM method. The efficacy of SMA is explored for a single-area economic load dispatch on small-scale power systems, using 3-, 5-, and 6-unit test systems, and the results are validated by comparing the results to those of other well-known meta-heuristic algorithms.

关键词： slime mold algorithm non-convex optimization electric power system metaheuristic algorithms load dispatch

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