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Dynamic Agricultural Pest Classification Using Enhanced SAO-CNN and Swarm Intelligence Optimization for UAVs

International Journal of Cognitive Computing in Engineering 2025年 6卷 588-602页

作者： Shiwei Chu Wenxia Bao School of Electronic Information Engineering Anhui University Hefei 230039 China

The rapid advancement of agricultural modernization demands urgent solutions for accurate and real-time pest monitoring to enhance crop productivity. Traditional manual methods lack efficiency and fail to capture dynamic pest behaviors, while existing deep learning models struggle with robustness in complex environments. To address these challenges, this study proposes a novel Dynamic Agricultural Pest Classification System that integrates an enhanced Self-Activation Optimization Convolutional Neural Network (SAO-CNN) with bio-inspired swarm intelligence for UAVs. The SAO-CNN innovatively combines adaptive convolutional layers, self-supervised learning, and ConvLSTM to optimize spatial-temporal feature extraction, while swarm algorithms (ACO and PSO) enhance UAV path planning and task allocation. Key contributions include: (1) A hybrid SAO-CNN architecture that dynamically adjusts convolution kernels and leverages unlabeled data through self-supervised learning, improving adaptability to lighting and background variations. (2) A UAV swarm intelligence framework optimized via bio-inspired algorithms, reducing flight time by 29.2% and energy consumption by 32% compared to non-optimized systems. (3) Superior performance with 91.2% classification accuracy, 0.89 recall, and 32 FPS processing speed, outperforming state-of-the-art models (e.g., YOLO variants, ResNet, and ConvLSTM) in both static and dynamic scenarios. This work provides a robust solution for real-time pest monitoring, significantly advancing precision agriculture and sustainable crop management.

关键词： Agricultural pest classification SAO-CNN bio-inspired algorithms Self-supervised learning Temporal feature optimization

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A framework for the analysis and synthesis of Swarm Intelligence algorithms

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JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE 2021年第4期33卷 659-681页

作者： Cruz, Davila Patricia Ferreira Maia, Renato Dourado de Castro, Leandro Nunes Univ Prebiteriana Mackenzie Nat Comp & Machine Learning Lab Lcon Grad Program Elect Engn & Comp Consolacao St 930 BR-01302000 Sao Paulo SP Brazil Univ Estadual Montes Claros Comp Sci Dept Montes Claros MG Brazil

Over the last decades, the number of Swarm Intelligence algorithms proposed in the literature has increased considerably. However, most algorithms do not follow an adequate scientific rigour neither the principles of Swarm Intelligence, reproducing similar computational procedures of many other approaches, but by means of a different metaphor. In this scenario, the aim of this paper is to propose a framework for the analysis and synthesis of Swarm Intelligence algorithms that can contribute to a structured understanding (analysis) and development (synthesis) of these algorithms. The main objective of the proposed framework is to guide the construction of Swarm Intelligence metaphors in a consistent and well-founded way, as well as to contribute with the analysis and development of new algorithms that had better encompass the features and abilities of insects' societies.

关键词： Swarm Intelligence bio-inspired algorithms natural computing information processing decision-making social insects

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Hybridization of Moth flame optimization algorithm and quantum computing for gene selection in microarray data

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2021年第2期12卷 2731-2750页

作者： Dabba, Ali Tari, Abdelkamel Meftali, Samy Mohamed Boudiaf Univ Fac Math & Comp Sci Comp Sci Dept Msila Algeria Abderrahmane Mira Univ Fac Sci Comp Sci Dept Bejaia Algeria Med Comp Lab LIMED Bejaia Algeria Univ Lille Lille France Res Ctr Comp Sci Signal & Automat Control Lille C Lille France

Ever-increasing data in various fields like bioinformatics field, which has led to the need to find a way to reduce the data dimensionality. Gene selection problem has a large number of genes (relevant, redundant or noise), which needs an effective method to help us in detecting diseases and cancer. In this problem, computational complexity is reduced by selecting a small number of genes, but it is necessary to choose the relevant genes to keep a high level of accuracy. Therefore, in order to find the optimal gene subset, it is essential to devise an effective exploration approach that can investigate a large number of possible gene subsets. In addition, it is required to use a powerful evaluation method to evaluate the relevance of these gene subsets. In this paper, we present a novel swarm intelligence algorithm for gene selection called quantum moth flame optimization algorithm (QMFOA), which based on hybridization between quantum computation and moth flame optimization (MFO) algorithm. The purpose of QMFOA is to identify a small gene subset that can be used to classify samples with high accuracy. The QMFOA has a simple two-phase approach, the first phase is a pre-processing that uses to address the difficulty of high-dimensional data, which measure the redundancy and the relevance of the gene, in order to obtain the relevant gene set. The second phase is a hybridization among MFOA, quantum computing, and support vector machine with leave-one-out cross-validation, etc., in order to solve the gene selection problem. We use quantum computing to guarantee a good trade-off between the exploration and the exploitation of the search space, while a new update moth operation using Hamming distance and Archimedes spiral allows an efficient exploration of all possible gene-subsets. The main objective of the second phase is to determine the best relevant gene subset of all genes obtained in the first phase. In order to assess the performance of the proposed QMFOA, we test Q

关键词： Gene expression Feature selection Moth flame optimization algorithm Quantum computing Microarray data Cancer classification bio-inspired algorithms Molecular biology Optimization algorithms Evolutionary algorithms Swarm intelligence

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A novel approach for optimal design of digital FIR filter using grasshopper optimization algorithm

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ISA TRANSACTIONS 2021年 108卷 196-206页

作者： Yadav, Suman Yadav, Richa Kumar, Ashwni Kumar, Manjeet Bharati Vidyapeeth Coll Engn Dept Elect & Commun Engn New Delhi 110063 India Indira Gandhi Delhi Tech Univ Women IGDTUW Dept Elect & Commun Engn New Delhi 110006 India Delhi Technol Univ DTU Rohini Dept Elect & Commun Engn Delhi 110042 India

The idea behind designing digital filters is to compute the optimal filter coefficients such that the magnitude response of the designed matches the ideal frequency response using optimization algorithms. The proposed work employed a recently proposed swarm-based optimization technique, namely, a grasshopper optimization algorithm (GOA) to design a linear phase finite impulse response (FIR) low pass, high pass, band pass , and band stop filters. This proposed algorithm models the behaviour of grasshoppers while seeking food sources to solve optimization problems. For the designing of the FIR filter, an absolute error difference fitness function is used, which is minimized using GOA to obtain optimal filter coefficients. The performance comparison of the proposed work is done with already existing algorithms such as cuckoo search, particle swarm optimization, artificial bee colony to prove its superiority and consistency. It is found that GOA based filter meets the objective efficiently with reduced ripples in pass band and higher attenuation in stop band with least execution time. (c) 2020 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： Digital filter design Filter response Grasshopper optimization algorithm bio-inspired algorithms

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Workload Partitioning of a bio-inspired Simultaneous Localization and Mapping Algorithm on an Embedded Architecture

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2021年第5期12卷 221-229页

作者： Mounir, Amraoui Rachid, Latif El Ouardi, Abdelhafid Tajer, Abdelouahed Ibn Zohr Univ LISTI ENSA Agadir 80000 Morocco Paris Saclay Univ SATIE Digiteo Labs Orsay France Cadi Ayyad Univ LISA ENSA Marrakech 40140 Morocco

Many algorithms were developed to perform visual localization and mapping (SLAM) for robotic applications. These algorithms used monocular or stereovision systems to solve constraints related to the navigation in unknown or dynamic environment. The requirement of SLAM systems in terms of processing time and precision is a factor that limits their use in many embedded applications like UAVs or autonomous vehicles. Meanwhile, trends towards low-cost and low-power processing require massive parallelism on hardware architectures. The emergence of recent heterogeneous embedded architectures should help design embedded systems dedicated to Visual SLAM applications. It was demonstrated in a previous work that bio-inspired algorithms are competitive compared to classical methods based on image processing and environment perception. This paper is a study of a bio-inspired SLAM algorithm with the aim of making it suitable for an implementation on a heterogeneous architecture dedicated for embedded applications. An algorithm-architecture adequation approach is used to achieve a workload partitioning on CPU-GPU architecture and hence speeding up processing tasks.

关键词： Simultaneous localization and mapping (SLAM) bio-inspired algorithms CPU-GPU workload partitioning embedded systems visual acuity (VA) hardware/software codesign

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A Gaussian random walk salp swarm algorithm for optimal dynamic charging of electric vehicles

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APPLIED SOFT COMPUTING 2023年 147卷

作者： Ahmed, Mohamed Kamel, Sara H. Abbasy, Nabil H. Abouelseoud, Yasmine Alexandria Univ Dept Engn Math & Phys Alexandria 21544 Egypt Alexandria Univ Dept Elect Engn Alexandria 21544 Egypt

Salp swarm algorithm (SSA) is one of the recently developed meta-heuristic optimization algorithms. Since SSA outperforms other swarm-based algorithms, it has recently been employed in various applications, including feature selection, neural network training and renewable energy systems. In this paper, an improved salp swarm algorithm based on a Gaussian random walk is proposed, which enhances the algorithm's performance particularly for multidimensional constrained global optimization problems. The integration of a Gaussian random walk into the algorithm balances between its exploration and exploitation capabilities. Furthermore, the proposed algorithm introduces a new re-dispersion strategy in the case of stagnation at local optimum points, which considerably enhances exploration. The performance of the proposed algorithm is evaluated using a set of twentythree benchmark test functions and is compared to the performance of prevalent metaheuristic algorithms. Statistical analysis is performed using Wilcoxon signed-rank test, and the results reveal considerable improvement over the competing algorithms. Then, 21 real-world optimization problems are used to further evaluate the efficacy of the proposed algorithm. The winners of the CEC2020 Competition on Real-World Single Objective Constrained Optimization, SASS, sCMAgES, EnMODE, and COLSHADE algorithms, are used as four comparable algorithms in the real-world optimization problems. The convergence curves and simulations provide very competitive performance compared to the comparative algorithms. The proposed algorithm is used to address one of the most challenging real-world constrained problems in power system applications, namely, determining the optimal charging schedule for electric vehicles at charging stations. The results reveal that the proposed algorithm outperforms other existing algorithms in terms of increasing the charging revenues and achieving maximum power grid stability.(c) 2023 Elsevier B.V. All r

关键词： Salp swarm algorithm Swarm intelligent algorithms bio-inspired algorithms Electric vehicle charging Charging stations

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Intelligent Escape of Robotic Systems: A Survey of Methodologies, Applications, and Challenges

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JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS 2023年第3期109卷 55-55页

作者： Li, Junfei Yang, Simon X. Univ Guelph Sch Engn 50 Stone Rd East Guelph ON N1G 2W1 Canada

Intelligent escape is an interdisciplinary field that employs artificial intelligence (AI) techniques to enable robots with the capacity to intelligently react to potential dangers in dynamic, intricate, and unpredictable scenarios. As the emphasis on safety becomes increasingly paramount and advancements in robotic technologies continue to advance, a wide range of intelligent escape methodologies has been developed in recent years. This paper presents a comprehensive survey of state-of-the-art research work on intelligent escape of robotic systems. Four main methods of intelligent escape are reviewed, including planning-based methodologies, partitioning-based methodologies, learning-based methodologies, and bio-inspired methodologies. The strengths and limitations of existing methods are summarized. In addition, potential applications of intelligent escape are discussed in various domains, such as search and rescue, evacuation, military security, and healthcare. In an effort to develop new approaches to intelligent escape, this survey identifies current research challenges and provides insights into future research trends in intelligent escape.

关键词： Escape algorithms Pursuit-evasion Collision avoidance bio-inspired algorithms

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A bio-inspired Cluster Optimization Schema for Efficient Routing in Vehicular Ad Hoc Networks (VANETs)

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ENERGIES 2023年第3期16卷 1456页

作者： Husnain, Ghassan Anwar, Shahzad Sikander, Gulbadan Ali, Armughan Lim, Sangsoon Iqra Natl Univ Dept Comp Sci Peshawar 25100 Pakistan Univ Engn & Technol Dept Mechatron Engn Peshawar 25100 Pakistan Univ Engn & Technol Natl Ctr Artificial Intelligence NCAI Intelligent Informat Proc Lab Peshawar 25000 Pakistan COMSATS Univ Islamabad Attock Campus Islamabad 43600 Pakistan Sungkyul Univ Dept Comp Engn Anyang 14097 South Korea

Vehicular ad hoc networks (VANETs) are vital to many Intelligent Transportation System (ITS)-enabled technologies, including efficient traffic control, media applications, and encrypted financial transactions. Due to an increase in traffic, vehicular network topology is constantly changing, and sparse vehicle distribution (on highways) hinders network scalability. Thus, there is a challenge for all vehicles (in the network) to maintain a stable route, which would increase network instability. Concerning IoT-based network transportation, this study proposes a bio-inspired, cluster-based algorithm for routing, i.e., the intelligent, probability-based, and nature-inspired whale optimization algorithm (p-WOA), which produces cluster formation in vehicular communication. Various parameters, such as communication range, number of nodes, velocity, and route along the highway were considered, and their probaabilities were incorporated into the fitness function, hence resulting in randomness reduction. Results were compared to existing methods such as Ant Lion Optimizer (ALO) and Grey Wolf Optimization (GWO), demonstrating that the developed p-WOA technique produces an optimal number of cluster heads (CH). The results achieved by calculating the Packet Delivery Ratio (PDR), average throughput, and latency demonstrate the superiority of the proposed method over other well-established methodologies (ALO and GWO). This study confirms statistically that VANETs employing ITS applications optimize their clusters by a factor of 75, which has the twin benefits of decreasing communication costs and routing overhead and extending the life of the cluster as a whole.

关键词： bio-inspired algorithms clustering vehicular networks whale optimization algorithm

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Predicting building energy utilization with energy plus simulation and advanced sea lion optimization algorithm based on Elman neural network

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2023年第10期35卷

作者： Al-Fadheeli, Mohammed Saud Ali Bouteraa, Yassine Mohammed, Adil Hussein Salman, Hayder Mahmood Ghasemi, Ghadir Univ Sfax Ecole Natl Ingenieurs Sfax ENIS Inst Super Biotechnol Sfax ISBS Control & Energy Management Lab Sfax Tunisia Prince Sattam Bin Abdulaziz Univ Coll Comp Engn & Sci Dept Comp Engn Al Kharj Saudi Arabia Cihan Univ Erbil Fac Engn Dept Commun & Comp Engn Erbil Kurdistan Regio Iraq Al Turath Univ Coll Dept Comp Sci Baghdad Iraq Prince Sattam bin Abdulaziz Univ Coll Comp Engn & Sci Comp Engn Dept Al Kharj 11942 Saudi Arabia

There are different methods for modeling a structure and the building with the heat from the exterior and interior sources for evaluating a good performance, predicting energy utilization. Various methods, differing from simple regression to techniques, which are according to the physical ideology, could be utilized in simulation. There is a prevalent explanation for whole these methods, in which the input parameters are according to an actual database when they are accessible, or else, the energy utilization assessment would be underestimated or over-estimated. In this investigation, two procedures have been presented that one is according to Elman neural network based on advanced sea lion optimization algorithm and the other one is according to physical ideology, that is, energy plus, which is prediction equipment for predicting building energy utilization. It can be seen that both methods can be proper for energy utilization prediction. In addition, an analysis that is parametric is implemented for the reflected structure on energy plus for evaluating the impact of some variables like the occupation of the building profile and weather database on predicting.

关键词： ASLOA-ENN building energy utilization prediction bio-inspired algorithms building simulation prediction

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Distributed Fair Resource Allocation Algorithm in Wireless Multihop Network Systems

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IEEE SYSTEMS JOURNAL 2021年第4期15卷 4713-4724页

作者： Jung, Ji-Young Lee, Jung-Ryun Chung Ang Univ Sch Elect Engn Seoul 06974 South Korea

Fair resource allocation in a wireless multihop (MH) network is very important for ensuring high bandwidth reuse and high end-to-end throughput without a bottle-neck problem. However, it is very challenging because the available bandwidth for a node in wireless MH networks is limited by both the number of neighboring nodes and their geographic locations, which are always changeable due to users' mobility. To address this problem, we propose a distributed resource allocation algorithm that enables collision-free and fair resource allocation among neighbors within two-hop distance. In the proposed algorithm, each node has a phase that linearly increases from 0 to 1 with a period of 1, and shares the phase information with its one- and two-hop neighbors. The shared phase information is used for distributed fair resource allocation for each node in a time division multiple access (TDMA) based network. We elaborate various characteristics of the proposed algorithm attributed to the MH properties. The necessary condition to ensure stability of the proposed algorithm is derived and analytically proved. Simulation results verify the stability of the proposed algorithm under various scenarios, and show faster convergence and higher bandwidth reuse compared to a comparison algorithm.

关键词： Oscillators Resource management Wireless communication Bandwidth Wireless sensor networks Synchronization Spread spectrum communication bio-inspired algorithms pulse-coupled-oscillator (PCO) time division multiple access (TDMA) based resource allocation wireless multihop (MH) network

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