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Article
Computer Science, Interdisciplinary Applications
Yundi Rao et al.
Summary: The Crow Search Algorithm is a new meta-heuristic optimizer inspired by the intelligent behavior of crows, and it has great potential for applications in engineering. This paper introduces a hybrid algorithm called PSCCSA, which combines the Crow Search Algorithm with a probability simplified sine cosine algorithm to overcome the limitations of blind location updates in CSA. The results of comparing the proposed algorithm with five other meta-heuristic algorithms and applying it to four classic engineering problems demonstrate its feasibility and effectiveness.
ENGINEERING WITH COMPUTERS
(2023)
Article
Computer Science, Artificial Intelligence
Malik Braik et al.
Summary: Crow Search Algorithm (CSA) is a promising meta-heuristic method that mimics the intelligent behavior of crows in nature. By combining it with Particle Swarm Optimization (PSO), the Memory based Hybrid CSA (MHCSA) achieves a stronger diversity ability and a better balance between exploration and exploitation, effectively overcoming the early convergence and imbalance issues. Test results have demonstrated the superiority of MHCSA over other methods in terms of accuracy and stability.
ARTIFICIAL INTELLIGENCE REVIEW
(2023)
Article
Mathematics
Luis Fernando Grisales-Norena et al.
Summary: This paper presents an efficient master-slave methodology to integrate photovoltaic generators into DC grids over a 20-year planning period. The methodology utilizes a discrete-continuous version of the Crow search algorithm to determine the installation positions and power ratings of the generators. Numerical results on test systems demonstrate the applicability, efficiency, and robustness of the developed approach compared to other methodologies discussed in the scientific literature.
Article
Engineering, Electrical & Electronic
Cenk Andic et al.
Summary: In this study, a robust Crow Search Algorithm (CSA) is proposed for solving both the optimal placement of Phasor Measurement Units (PMUs) and the optimal static estimation for the entire power system. The effectiveness of the CSA-based state estimator is shown by comparing it with other well-known algorithms. This study contributes to the more accurate and reliable operation of the power system.
ELECTRIC POWER SYSTEMS RESEARCH
(2023)
Article
Computer Science, Artificial Intelligence
Songwei Zhao et al.
Summary: This paper presents a method for COVID-19 X-ray image recognition and segmentation based on an improved crow search algorithm. By introducing variable neighborhood descent and information exchange mutation strategies, a new algorithm (VMCSA) is proposed, which shows better performance in optimization. The proposed algorithm has significant advantages in segmentation results of COVID-19 images and exhibits better robustness compared to other algorithms.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Robotics
Jiazhao Zhang et al.
Summary: This paper focuses on the problem of high-dimensional nonlinear state estimation in inertial-aided navigation systems. The authors propose a method based on random optimization and active subspace to tackle the challenges posed by large interframe transformations. The method efficiently explores the state space and achieves accurate and robust depth inertial odometry.
IEEE TRANSACTIONS ON ROBOTICS
(2023)
Article
Computer Science, Hardware & Architecture
Sanjay Kumar et al.
Summary: This paper proposes a deep learning approach for community detection in complex networks, combining stacked autoencoders and the Crow Search Algorithm. Experimental results demonstrate that the proposed method achieves excellent performance.
JOURNAL OF SUPERCOMPUTING
(2023)
Article
Computer Science, Hardware & Architecture
Nguyen Van Thieu et al.
Summary: This paper introduces a new Python library called Mealpy for implementing nature-inspired optimization algorithms. Compared to existing libraries, Mealpy has the largest collection of classical and state-of-the-art meta-heuristic algorithms, with over 160 algorithms. It is an open-source library with a simple interface and minimal dependencies. Mealpy can be used for optimizing challenge benchmark functions as well as practical problems, such as parameter optimization for machine learning models.
JOURNAL OF SYSTEMS ARCHITECTURE
(2023)
Article
Computer Science, Interdisciplinary Applications
Zhonglai Wang et al.
Summary: This paper proposes a novel flexible-constrained time-variant hybrid reliability-based design optimization model to balance resources and reliability during the design stage under lower and upper bounds. The infeasible region pruning-based Kriging method is used for hard constraints, while a combination of Kriging and high-dimensional model representation is used for flexible constraints to improve efficiency.
STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
(2023)
Article
Energy & Fuels
Yuzhu Duan et al.
Summary: This paper proposes a distributed optimization algorithm for a hybrid microgrid network to minimize the total generation cost in a dynamic economic dispatch problem. The algorithm considers the supply-demand balance, capacity, and ramp-rate constraints of the generation facilities, as well as the environmental impact of pollutant emissions. It transforms the multi-objective optimization problem into a single objective optimization problem and proposes a fully distributed algorithm that does not require initialization. The experimental results demonstrate the effectiveness of the algorithm in satisfying the constraints of the supply-demand and capacity inequalities.
SUSTAINABLE ENERGY GRIDS & NETWORKS
(2023)
Article
Agriculture, Multidisciplinary
Jinchao Xu et al.
Summary: Traditional rigid sensors and imperfect calibration methods cannot meet the demand of food quality and safety monitoring in aquatic products supply chain. This paper presents a passive flexible ammonia sensor based on adaptive parameter adjustment artificial neural network (APA-ANN) for aquatic monitoring. The sensor can acquire ammonia information and transmit it wirelessly using radio frequency identification (RFID). With an energy harvesting module, the sensor can harvest RF energy and operate without a battery. Calibration and verification experiments were conducted for oysters and abalone in waterless and watery environments, respectively. The sensor output showed good fitting effect and the APA-ANN improved the accuracy under interference and cross-sensitivity. The optimized model achieved an accuracy higher than 89.5%, demonstrating the applicability of the sensor and providing an optimization method for sustainable monitoring in agriculture.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2023)
Article
Engineering, Multidisciplinary
Yang Bai et al.
Summary: In wireless sensor networks, using clustering method and topology optimization technique based on CM-CSA to deal with energy consumption imbalance, short life cycles, and unstable topology. Using enhanced Cauchy mutation crow search algorithm in clustering approach to improve the crow algorithm's convergence speed and prevent settling for the local optimum. The suggested CM-CSA algorithm decreases energy consumption and enhances connectivity performance compared to other algorithms.
Article
Computer Science, Artificial Intelligence
Pushpendra Singh et al.
Summary: This paper presents a new load-shedding strategy for ensuring adequate voltage stability margin after load shedding. The strategy selects a minimum number of buses for load shedding based on the incremental voltage of the load buses. It formulates an objective function that minimizes inequality constraints on load bus voltage and line flow, which is a weighted sum of the slope of the weakest bus's PV-curve, transmission losses, and normalized total load shed amount. To obtain optimal load shed, a modified Crow Search Algorithm (CSA) is used. The methodology is implemented and compared with other algorithms on standard IEEE 14 and 25-bus test systems.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Abdelouahab Necira et al.
Summary: This paper introduces an enhanced version of CSA called dynamic crow search algorithm (DCSA) to overcome the drawbacks of traditional CSA. The DCSA dynamically adjusts parameters to provide better global search capability and exploitation of final solutions, and improves swarm diversity for high convergence accuracy and fast convergence rate. Experimental results show the effectiveness of DCSA compared to basic CSA and other similar algorithms in terms of convergence rate, global search capability, and final solutions.
EVOLUTIONARY INTELLIGENCE
(2022)
Article
Energy & Fuels
Xizheng Zhang et al.
Summary: The hybrid modified GSA-PSO scheme is proposed to optimize the load dispatch of the microgrid containing electric vehicles, which can significantly improve the safety and economy of the grid, reduce the total cost and load variance.
Article
Mathematics, Interdisciplinary Applications
Dongqing Luan et al.
Summary: This paper studies the robust location-allocation for emergency temporary blood supply after disaster in China. A robust location model is proposed with minimum cost and time window constraints. Experimental results show that increased demand uncertainty and survival probability lead to higher costs. The optimal robust model is the MIRP-ellipsoid set model, which can reduce costs and improve survival probability with necessary time window constraints.
DISCRETE DYNAMICS IN NATURE AND SOCIETY
(2022)
Article
Energy & Fuels
Kai Zhang et al.
Summary: The paper introduces a novel production optimization method that models the life-cycle production optimization problem as a finite-horizon Markov decision process. By training deep reinforcement learning agents, it achieves maximizing net present value and realtime adjustment of well control schemes.
JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING
(2022)
Article
Computer Science, Hardware & Architecture
Hussam N. Fakhouri et al.
Summary: Swarm intelligence is a subset of artificial intelligence that utilizes the collective behavior of particles to solve complex optimization problems. The success history intelligent optimizer (SHIO) is a novel stochastic swarm intelligence algorithm that offers a new exploration and exploitation strategy based on the three best solutions found in the search space. SHIO demonstrates efficient exploration and utilization of search space locations while avoiding local optimism and converging to the global best, outperforming compared algorithms in unimodal and multimodal benchmarks.
JOURNAL OF SUPERCOMPUTING
(2022)
Article
Computer Science, Information Systems
Zhihan Lv et al.
Summary: This work aims to explore the cross-layer optimization of digital twins in Industrial Internet of Things (IIoT) and applies it to the hazardous gas leakage boundary tracking in industries. By proposing an algorithm based on parallel optimization framework and establishing a distributed edge computing network, an effective industrial hazardous gas tracking algorithm has been developed. The results show that the algorithm can accurately track the gas boundary and reduce network energy consumption.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Article
Computer Science, Artificial Intelligence
Thaer Thaher et al.
Summary: This paper proposes an efficient feature selection approach based on a Boolean variant of Particle Swarm Optimization (BPSO) boosted with Evolutionary Population Dynamics (EPD). The experimental results demonstrate the superiority of the proposed EPD-based feature selection approaches, especially the BPSO-TEPD variant when compared with conventional BPSO and other five EPD-based variants.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Mohammed A. Awadallah et al.
Summary: This paper introduces a method to systematically structure the population in evolutionary computation to manage the evolution process, and optimizes the crow search algorithm using the cellular automata model. The proposed CCSA method demonstrates its effectiveness by structuring the population in a 2D grid and guiding individuals based on neighboring relationships and the best neighbor. Comparative evaluations against state-of-the-art methods on standard benchmark functions and real-world problems show that CCSA outperforms other methods.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Software Engineering
Thaer Thaher et al.
Summary: This paper proposes an efficient wrapper feature selection method based on Grey Wolf Optimizer (GWO), which shows promising performance in dealing with high-dimensional problems.
JOURNAL OF UNIVERSAL COMPUTER SCIENCE
(2022)
Article
Automation & Control Systems
Bin Li et al.
Summary: This article proposes two distributionally robust model predictive control algorithms for handling discrete linear systems with unbounded noise. The algorithms consider chance constraints on both state and control, and employ deterministic convex reformulations inspired by distributionally robust optimization. Computational complexity analysis demonstrates the effectiveness of the proposed algorithms, and proofs of recursive feasibility and convergence are provided. Simulation results are presented to show their effectiveness.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2022)
Article
Engineering, Marine
Yuanzhou Zheng et al.
Summary: This paper proposes a ship position prediction model combining genetic algorithm, ant colony algorithm, and BP neural network, which improves the accuracy and efficiency of position prediction by deep preprocessing AIS data and optimizing neural network parameters.
JOURNAL OF MARINE SCIENCE AND ENGINEERING
(2022)
Article
Mathematics
Ali Asghar Heidari et al.
Summary: This paper proposes a new wavelet PM2.5 prediction system based on an improved variant of the salp swarm algorithm, kernel extreme learning machine, wavelet decomposition, and Boruta-XGBoost feature selection. The experimental results show that the proposed model has superior performance in predicting PM2.5 concentration.
Review
Computer Science, Information Systems
Aminu Aminu Muazu et al.
Summary: The metaheuristic algorithm is an important area of research for solving optimization problems. Nature-inspired metaheuristic algorithms have gained popularity in the past few decades and have made significant contributions to tackling complex problems and achieving optimal results. However, not all metaheuristics are effectively adopted in combinatorial t-way testing. This research paper provides a comprehensive review of 110 outstanding nature-inspired metaheuristic algorithms and their current states of use, aiming to inspire future research in combinatorial t-way testing and provide directions for optimization.
Article
Computer Science, Information Systems
Chen Li
Summary: This paper proposes an edge location method for multidimensional images based on edge symmetry, which verifies real edges by analyzing the symmetry axis position and symmetry of candidate image regions, and synthesizes them using a multidimensional pulse coupled neural network model to achieve accurate edge location results.
SECURITY AND COMMUNICATION NETWORKS
(2021)
Article
Computer Science, Artificial Intelligence
Malik Braik et al.
Summary: The study introduces a novel nature-inspired search optimization algorithm called Capuchin Search Algorithm (CapSA), which is designed based on the foraging behaviors of capuchin monkeys in forests to solve global optimization problems efficiently.
NEURAL COMPUTING & APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Yutao Yang et al.
Summary: The research proposes a population-based optimization technique called Hunger Games Search (HGS), designed based on the hunger-driven activities and behavioral choices of animals, with a simple structure, special stability features, and competitive performance to efficiently address constrained and unconstrained problems.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Ibrahim Mirac Eliguzel et al.
Summary: This paper demonstrates the application of the improved Crow Search Algorithm (I-CSA) on the P-median problem, which allows for faster convergence and better solutions through local searches and elitism. The application is structured in three phases, with parameters optimized and tested using well-known datasets, and results compared to other algorithms to showcase its effectiveness.
ARTIFICIAL INTELLIGENCE REVIEW
(2021)
Article
Automation & Control Systems
Niam Abdulmunim Al-Thanoon et al.
Summary: Feature selection plays a crucial role in improving classification algorithm results. The OBL-BCSA algorithm, utilizing an opposition-based learning strategy, excels in selecting relevant features with high classification performance.
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
(2021)
Article
Engineering, Multidisciplinary
Laith Abualigah et al.
Summary: The Arithmetic Optimization Algorithm (AOA) is a new meta-heuristic method that makes use of the distribution behavior of arithmetic operators, demonstrating promising results in solving challenging optimization problems across various search spaces.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
(2021)
Article
Computer Science, Artificial Intelligence
Abhilasha Chaudhuri et al.
Summary: Crow Search Algorithm (CSA) is a widely used meta-heuristic algorithm that introduces the concept of time varying flight length to solve the issue of being trapped in local minima. The proposed approach is applied to feature selection problems and outperforms other competitors in experimental results.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Engineering, Civil
Bin Cao et al.
Summary: Researched diversified recommendation problems in the Internet of Vehicles and constructed a multi-objective recommendation model, considering not only recommendation precision but also recommendation novelty and coverage.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2021)
Article
Engineering, Chemical
Mohammad H. Nadimi-Shahraki et al.
Summary: The article introduces a migration-based moth-flame optimization (M-MFO) algorithm to address local optima issues in traditional moth-flame optimization. M-MFO provides sufficient exploration ability and population diversity maintenance through the use of migration strategy and guiding archive.
Article
Mathematics, Interdisciplinary Applications
GuoChun Wang et al.
Summary: The MEWOA algorithm enhances efficiency by using elite strategy and spiral motion, outperforming other algorithms in global optimization.
Article
Computer Science, Interdisciplinary Applications
Laith Abualigah et al.
Summary: This paper introduces a novel population-based optimization method, AO, inspired by the behaviors of eagles during hunting. Through a series of experiments, the superior performance of AO in finding optimal solutions for various problems is demonstrated and compared with other meta-heuristic methods.
COMPUTERS & INDUSTRIAL ENGINEERING
(2021)
Article
Construction & Building Technology
Yukai Ke et al.
Summary: In this study, an Improved Crow Search Algorithm (ICSA) combined with EnergyPlus simulation tool was used to optimize office buildings in four cities in Australia, resulting in over 11.8% reduction in energy consumption through energy-saving measures. Comparisons with benchmark algorithms showed that ICSA provides more suitable solutions with shorter calculation time.
JOURNAL OF BUILDING ENGINEERING
(2021)
Article
Computer Science, Information Systems
Li Cao et al.
Summary: The study introduces an improved crow search algorithm to optimize the extreme learning machine, enhancing global search capability and gradually reducing search trajectory amplitude to avoid being attracted by local extremum, ultimately optimizing hidden layer neurons and connection weights for accurate prediction results.
Article
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Afshin Faramarzi et al.
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(2020)
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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
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