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Optimizing Biomass Pyrolysis: A Comparative Analysis of GA, PSO, and SCE Algorithms

Renewable energy, especially biomass pyrolysis, are receiving increasing attention due to their economic and environmental benefits. To advance biomass pyrolysi

China , Nanjing , Jiangsu , Hongfang-wang , Junhui-gong , Tech-university , College-of-safety-science , Genetic-algorithm , Hill-climbing , Particle-swarm-optimization , Shuffled-complex-evolution , Emergency-managementscience

"A Novel Hybrid MPPT Approach for Solar PV Systems Using Particle-Swarm" by Dilip Kumar, Yogesh Kumar Chauhan et al.

In this paper, a novel hybrid Maximum Power Point Tracking (MPPT) algorithm using Particle-Swarm-Optimization-trained machine learning and Flying Squirrel Search Optimization (PSO_ML-FSSO) has been proposed to obtain the optimal efficiency for solar PV systems. The proposed algorithm was compared with other well-known methods viz. Perturb & Observer (P&O), Incremental Conductance (INC), Particle Swarm Optimization (PSO), Cuckoo Search Optimization (CSO), Flower Pollen Algorithm (FPA), Gray Wolf Optimization (GWO), Neural-Network-trained Machine Learning (NN_ML), Genetic Algorithm (GA), and PSO-trained Machine Learning. The proposed algorithm was modelled in the MATLAB/Simulink environment under different operating conditions, for example, with step changes in temperature, solar irradiance, and partial shading. The proposed algorithm improved the efficiency up to 0.72% and reduced the settling time up to 76.4%. The findings of the research highlight that PSO_ML-FSSO is a potential approach that outperforms all other well-known algorithms tested herein for solar PV systems.

Perturb-observer-po-incremental-conductance , Maximum-power-point-tracking , Flying-squirrel-search-optimization , Incremental-conductance , Particle-swarm-optimization , Cuckoo-search-optimization , Flower-pollen-algorithm , Gray-wolf-optimization , Neural-network-trained-machine-learning , Genetic-algorithm ,

Global Swarm Intelligence Market Analysis Report 2024: Ant Colony Optimization Leads Market with 45.0% Share in 2023 - Forecast to 2030 for the $300+ Million Industry

/PRNewswire/ -- The "Swarm Intelligence Market Size, Share & Trends Analysis Report By Model (Ant Colony Optimization, Particle Swarm Optimization, Others), By...

United-states , Dublin , Ireland , America , Fritz-schafer , Laura-wood , Robert-bosch-gmb , Axon-enterprise-inc , Application-movement-analysis , Company-market-positioning , Research-scope , Company-categorization

How Digital Transformation Is Upgrading Businesses Across The Globe

This article analyzes how digital transformation is modernizing companies globally and changing the labour market altogether.

Chitra-sabapathy-ranganathan , Harris-hawks-optimization , Particle-swarm-optimization ,

Novel MPPT technique based on Coronavirus algorithm

Novel MPPT technique based on Coronavirus algorithm
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Pakistan , Global-maxima , Incremental-conductance , Dragonfly-optimization , Cuckoo-search , Fruit-fly-optimization , Particle-swarm-optimization , Ant-colony-optimization , Coronavirus-optimization ,

Network Encryption Market 2032 |Moving Towards a Brighter

Network Encryption Market 2032 |Moving Towards a Brighter
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United-states , America , Market-research , Allied-market-research , Intelligence-market , Particle-swarm-optimization , Ant-colony-optimization , Human-swarming , North-america , Global-opportunity-analysis , Industry-forecast

Forests | Free Full-Text | Demand-Led Optimization of Urban Park Services

As the demand for cultural and recreational services grows, the mismatch between the supply and demand of park services significantly affects residents’ well-being. Optimizing the spatial layout of park services is a focal point of urban park and green space research. Taking Hangzhou, Zhejiang Province, as a case study, this research analyzes the spatial patterns and balance of park service supply and demand. Utilizing the Grey Wolf Optimization Model optimized by the K-Nearest Neighbor Model (GWO-KNN), this study proposes construction objectives for optimizing park services. The results indicate the following: (1) significant differences exist in the park service demands of residents in different residential environments; (2) there is a noticeable spatial disparity in park service supply among various residential areas with an overall positive correlation between park service supply levels and resident demands, yet an imbalance exists; (3) this study categorizes spatial types into low-service coordination, high-service coordination, low-service imbalance, and high-service imbalance; (4) the GWO-KNN Model is applied with optimization objectives being the innovative aspect of this study. Strategies for each park category are proposed: emphasizing suburban park construction by utilizing surrounding green resources and adding diverse facilities; introducing facilities friendly to vulnerable groups to meet the needs of diverse populations; enhancing the complementary advantages between “new” and “old” cities by moderately increasing park sizes and improving cultural and facility development levels; optimizing spatial structure with limited land resources to construct an urban park network system. This study aims to provide theoretical and technical support for optimizing urban park and green space systems.

Xixi , Zhejiang , China , Shanghai , Xiaoshan , Anhui , Zhongyan , Yuhang , Hangzhou , Shangcheng , Guangdong , Lingshan

Broadband achromatic metalens design based on predictive neural network and particle swarm optimization-genetic algorithm

Broadband achromatic metalens design based on predictive neural network and particle swarm optimization-genetic algorithm
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China , Anhui , Natural-science-foundation-of-anhui-province-no , National-natural-science-foundation-of-china , Nvidia , Particle-swarm-optimization , Genetic-algorithm , National-natural-science-foundation , Natural-science-foundation , Anhui-province , Higher-education-institutions

Swarm Intelligence Market to Reach $725.4 Million, Globally, by 2032 at 38.6% CAGR: Allied Market Research

Portland, OR, Sept. 25, 2023 (GLOBE NEWSWIRE) -- Allied Market Research published a report, titled, "Swarm Intelligence Market by Model (Particle Swarm Optimization, Ant Colony Optimization, and Others), Capability

Portland , Oregon , United-states , Delaware , America , Robert-bosch-gmbh , Pawan-kumar , Business-intelligence-solutions , Convergentai-inc , Swarm-technology , Library-premium , Market-research

"An Optimized Bio-inspired Localization Routing Technique for Sustainab" by Premkumar Chithaluru, Fadi Al-Turjman et al.

The industrial Internet of Things (IIoTs) network life is shortened due to sensor node (SN) energy limitations and computational capability. As a result, optimum node location estimation and efficient energy usage are two critical IIoT requirements. This work reduces energy consumption by performing node localization and cluster-based routing using an improved evolutionary algorithm called Cat Swarm Optimization (CSO). First, the CSO method is used to optimize the bio-inspired node's location. Second, to conserve SN energy in the IIoT network, a cluster-based routing technique is used. The objective function is defined as minimizing the average distance between the cluster and its SNs while selecting the most energy-efficient Cluster Head (CH). In terms of fitness value, the Improved CSO (ICSO) algorithm outperforms the Particle Swarm Optimization (PSO) algorithm. In this paper, real-time test-bed analysis was used to investigate the performance of both node localization and energy-efficient clustering. When it comes to achieving sustainable IIoT and green cities, the findings show that ICSO outperforms in terms of convergence rate and network lifetime.

Cat-swarm-optimization , Cluster-head , Particle-swarm-optimization , Bio-inspired , Reen-cities , Cso , Iot , Ocalization , So , Ustainable ,