Embedded feature fusion for multi-label criteria selection via local search strategy and particle swarm optimization by Suhua Chen, Xu Fang, Feng Zhai, Li Wang, Lin Lv

“…To address this issue and enhance convergence speed, this article introduces a hybrid AI solution that combines a binary particle swarm optimization algorithm with a local search strategy specifically designed for multi-label feature selection. …”
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Particle Swarm Optimization Algorithm for Determining Global Optima of Investment Portfolio Weight Using Mean-Value-at-Risk Model in Banking Sector Stocks by Moh. Alfi Amal, Herlina Napitupulu, Sukono

“…This study aims to develop a computational algorithm based on the metaheuristic particle swarm optimization (PSO) model, which can be used to solve global optimization problems in portfolio investment. …”
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Optimization of spatio-temporal ozone (O3) pollution modeling using an ensemble machine model learning with a swarm-based metaheuristic algorithm by Seyed Vahid Razavi-Termeh, Abolghasem Sadeghi-Niaraki, Armin Sorooshian, Lingbo Liu, Shuming Bao, Soo-Mi Choi

“…This research fills a crucial gap in current modeling approaches by proposing a novel methodology that integrates an ensemble machine-learning algorithm with swarm-based metaheuristic optimization algorithm. …”
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An electricity price optimization model considering time-of-use and active distribution network efficiency improvements by Yan Li, Yaheng Su, Qixin Zhao, Bala Wuda, Kaibo Qu, Lei Tang

“…This model combines an improved Particle Swarm Optimization algorithm, Quantum-behaved Particle Swarm Optimization, and the Shuffle Frog Leaping Algorithm. …”
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Multi-Class Skin Cancer Classification Using a Hybrid Dynamic Salp Swarm Algorithm and Weighted Extreme Learning Machines with Transfer Learning by Ramya Panneerselvam, Sathiyabhama Balasubramaniam

“…The proposed work is implemented as a hybrid model with a dynamic salp swarm algorithm (DSSA) with a weighted extreme learning machine (DSSA-WELM) that addresses the imbalances in the dataset and performs the classification with higher accuracy. …”
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Benchmarking Metaheuristic Algorithms Against Optimization Techniques for Transportation Problem in Supply Chain Management by Felicia Lim Xin Ying, Suliadi Firdaus Sufahani

“…Metaheuristic algorithms, specifically Simulated Annealing (SA) and Particle Swarm Optimization (PSO), are implemented to explore alternative near-optimal solutions and assess the performance in terms of cost accuracy and computational efficiency. …”
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A Simplified Fish School Search Algorithm for Continuous Single-Objective Optimization by Elliackin Figueiredo, Clodomir Santana, Hugo Valadares Siqueira, Mariana Macedo, Attilio Converti, Anu Gokhale, Carmelo Bastos-Filho

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Application of the metaheuristic algorithms to quantify the GSI based on the RMR classification by Pouya Koureh Davoodi, Farnusch Hajizadeh, Mohammad Rezaei

“…According to this evaluation, the best-performing equation from each algorithm was selected as the optimum and further evaluated using both graphical and statistical analyses, including comparisons with conventional empirical relationships. …”
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Orthogonal Multi‐Swarm Greedy Selection Based Sine Cosine Algorithm for Optimal FACTS Placement in Uncertain Wind Integrated Scenario Based Power Systems by Sunilkumar P. Agrawal, Pradeep Jangir, Arpita, Sundaram B. Pandya, Anil Parmar, Mohammad Khishe, Bhargavi Indrajit Trivedi

“…This study proposes a novel hybrid metaheuristic approach, the Orthogonal Multi‐swarm Greedy Selection Sine Cosine Algorithm (OMGSCA), integrating orthogonal learning, multi‐swarm mechanisms, and greedy selection to enhance solution quality. …”
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Multi-Criteria Optimization of a Hybrid Renewable Energy System Using Particle Swarm Optimization for Optimal Sizing and Performance Evaluation by Shree Om Bade, Olusegun Stanley Tomomewo, Ajan Meenakshisundaram, Maharshi Dey, Moones Alamooti, Nabil Halwany

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Clustering routing protocol based on improved PSO algorithm in WSN by Xiaonian WU, Chuyun ZHANG, Runlian ZHANG, Yaping SUN

“…Aiming at the problem that the location distribution of cluster head nodes filtered by wireless sensor network clustering routing protocol was unbalanced and the data transmission path of forwarding nodes was unreasonable,which would increase the energy consumption of nodes and shorten the network life cycle,a clustering routing protocol based on improved particle swarm optimization algorithm was proposed.In the process of cluster head election,a new fitness function was established by defining the energy factor and position equalization factor of the node,the better candidate cluster head node was evaluated and selected,the position update speed of the candidate cluster head nodes was adjusted by the optimized update learning factor,the local search and speeded up the convergence of the global search was expanded.According to the distance between the forwarding node and the base station,the single-hop or multi-hop transmission mode was adopted,and a multi-hop method was designed based on the minimum spanning tree to select an optimal multi-hop path for the data transmission of the forwarding node.Simulation results show that the clustering routing protocol based on improved particle swarm optimization algorithm can elect cluster head nodes and forwarding nodes with more balanced energy and location,which shortened the communication distance of the network.The energy consumption of nodes is lower and more balanced,effectively extending the network life cycle.…”
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Multi-objective particle swarm optimization algorithm-based method for optimal placement and sizing of distributed generations and shunt capacitors in a radial distribution network by Pinank Patel, Nagaraj Patil, Ahmed Mohsen, Aditya Kashyap, Nofal Adrees Hasan, Karthikeyan A, Dhirendra Nath Thatoi, Deepak Gupta, Alireza kamranfar

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Multi-objective cloud workflow scheduling algorithm based on grid variance by Xiaoan BAO, Yundi CAO, Na ZHANG, Junyan QIAN, Jianwen CAO

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A Labor Division Artificial Gorilla Troops Algorithm for Engineering Optimization by Chenhuizi Liu, Bowen Wu, Liangkuan Zhu

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Credit card fraud Detection using Feature select method and improved machine learning algorithm by Mohammed AL-Hammadi

“…By using a binary genetic algorithm and cross-entropy, our feature selection approach identifies key attributes and evaluates their relevance to the target variable. …”
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Fast Non-Dominated Sorting Tuna Swarm Optimization Algorithm (FNS-TSO): Time-Energy-Impact Multi-Objective Optimization of Underwater Manipulator Trajectories by Xingyao Wang, Yanjun Liu, Gang Xue, Fagang Bai, Shuting Huang

“…To verify FNS-TSO’s effectiveness, we conducted comparative evaluations using standard test functions against three established algorithms: Multi-Objective Particle Swarm Optimization (MOPSO), Multi-Objective Jellyfish Search Optimization (MOJSO), and the Non-Dominated-Sorting Genetic Algorithm (NSGA-II). …”
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Risk assessment and hybrid algorithm transportation path optimization model for road transport of dangerous goods by Qiankun Jiang, Haiyan Wang

“…After calculating the weights of each indicator, this study improves the parameters of the particle swarm algorithm using the aggregation and foraging behavior of artificial fish, and uses the improved algorithm to solve the optimal solution for the cost of dangerous goods road transportation. …”
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Optimized Intelligent Localization Through Mathematical Modeling and Crow Search Algorithms by Tamer Ramadan Badawy, Nesreen I. Ziedan

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Innovative Hybrid Algorithm for Solving Vehicle Routing Problem with Time Window by A.M. Rahimi, B. Yadegari, M. Aboutalebi Esfahani

“…This paper presents a hybrid cat-swarming algorithm that utilizes genetic operators to effectively address the VRPTW problem. …”
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On efficiency of methods and algorithms for solving optimization problems considering objective function specifics by E. N. Ostroukh, Yu. O. Chernyshev, L. N. Evich, P. A. Panasenko

“…The efficiency of hybrid algorithms that combine the following approaches is evaluated: genetic and immune; methods of swarm intelligence and genetic and immune; immune and swarm with gravity and gradient.Discussion and Conclusions. …”
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