A modified particle swarm optimization-based adaptive maximum power point tracking approach for proton exchange membrane fuel cells by Bhukya Laxman, Ramesh Gugulothu, Surender Reddy Salkuti

“…In this paper, an MPSO algorithm-based MPPT tracking approach without a PID controller is proposed to achieve the maximum power point (MPP) of a PEMFC. …”
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Fuzzy clustering based on Forest optimization algorithm by Arash Chaghari, Mohammad-Reza Feizi-Derakhshi, Mohammad-Ali Balafar

“…By analyzing and comparing the results of the proposed method with the results of algorithms GGAFCM (fuzzy clustering based on genetic algorithm) and PSOFCM (fuzzy clustering based on particle swarm optimization algorithm), it has been shown that the accuracy of the proposed approach is significantly increased.…”
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QPSO-Based Adaptive DNA Computing Algorithm by Mehmet Karakose, Ugur Cigdem

“…This new approach aims to perform DNA computing algorithm with adaptive parameters towards the desired goal using quantum-behaved particle swarm optimization (QPSO). …”
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Black Hole Algorithm for Software Requirements Prioritization by Norah Ibrahim Alfassam, M. Abdullah-Al-Wadud, Mubarak Rashed Alrashoud

“…Software engineers have introduced several methods for solving these problems. The Black Hole Algorithm (BHA) is a population-based approach. It is among one of the many modern approaches and has been successfully applied to solve optimization problems. …”
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Optimasi Algoritma Support Vector Machine Berbasis Kernel Radial Basis Function (RBF) Menggunakan Metode Particle Swarm Optimization Untuk Analisis Sentimen by Cucun Very Angkoso, Khozainul Asror, Ari Kusumaningsih, Andi Kurniawan Nugroho

“…Furthermore, based on the results of SVM testing using parameter optimization of the PSO algorithm, an average performance increase of 11.33% was obtained for each application that had been analyzed. …”
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Sequentially Modified Gravitational Search Algorithm for Image Enhancement by Ferzan Katırcıoğlu, Uğur Güvenç

“…The achieved results are compared with the Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and GSA among the heuristic optimization algorithms. …”
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Artificial Bee Colony Algorithm with Time-Varying Strategy by Quande Qin, Shi Cheng, Qingyu Zhang, Li Li, Yuhui Shi

“…Artificial bee colony (ABC) is one of the newest additions to the class of swarm intelligence. ABC algorithm has been shown to be competitive with some other population-based algorithms. …”
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Optimization of Electrical Discharge Machining Process by Metaheuristic Algorithms by Nurezayana Zainal, Mohanavali Sithambranathan, Umar Farooq Khattak, Azlan Mohd Zain, Salama A. Mostafa, Ashanira Mat Deris

“…This paper emphasizes optimizing the EDM process using three metaheuristic algorithms: Glowworm Swarm Optimization (GSO), Grey Wolf Optimizer (GWO), and Whale Optimization Algorithm (WOA). …”
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Modified Meerkat Clan Algorithm for Association Rules Mining by Mohamad Ab. Saleh, Ahmed T. Sadiq Sadiq

“…In this paper, Modified Meerkat Clan for Association Rules Mining (MCC-ARM) has been proposed. Basically, the proposed algorithm depends on Meerkat Clan Algorithm (MCA). …”
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Hybrid self-inertia weight adaptive particle swarm optimisation with local search using C4.5 decision tree classifier for feature selection problems by Arfan Ali Nagra, Fei Han, Qing Hua Ling, Muhammad Abubaker, Farooq Ahmad, Sumet Mehta, Abeo Timothy Apasiba

“…A number of methodologies have been presented for feature selection problems using metaheuristic algorithms. …”
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Enhancing Power Quality in Grid-Integrated Hybrid Renewable Energy System using ANFIS-FBSO by Singh Manpreet, Singh Lakhwinder

“…In this paper, an enhanced adaptive neuro-fuzzy inference system (ANFIS)-based firebug swarm optimisation (FBSO) algorithm has been integrated with a unified power quality conditioner (UPQC) to mitigate power quality (PQ) issues in hybrid renewable energy systems (HRESs). …”
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Robust reinforcement learning algorithm based on pigeon-inspired optimization by Mingying ZHANG, Bing HUA, Yuguang ZHANG, Haidong LI, Mohong ZHENG

“…Reinforcement learning(RL) is an artificial intelligence algorithm with the advantages of clear calculation logic and easy expansion of the model.Through interacting with the environment and maximizing value functions on the premise of obtaining little or no prior information, RL can optimize the performance of strategies and effectively reduce the complexity caused by physical models .The RL algorithm based on strategy gradient has been successfully applied in many fields such as intelligent image recognition, robot control and path planning for automatic driving.However, the highly sampling-dependent characteristics of RL determine that the training process needs a large number of samples to converge, and the accuracy of decision making is easily affected by slight interference that does not match with the simulation environment.Especially when RL is applied to the control field, it is difficult to prove the stability of the algorithm because the convergence of the algorithm cannot be guaranteed.Considering that swarm intelligence algorithm can solve complex problems through group cooperation and has the characteristics of self-organization and strong stability, it is an effective way to be used for improving the stability of RL model.The pigeon-inspired optimization algorithm in swarm intelligence was combined to improve RL based on strategy gradient.A RL algorithm based on pigeon-inspired optimization was proposed to solve the strategy gradient in order to maximize long-term future rewards.Adaptive function of pigeon-inspired optimization algorithm and RL were combined to estimate the advantages and disadvantages of strategies, avoid solving into an infinite loop, and improve the stability of the algorithm.A nonlinear two-wheel inverted pendulum robot control system was selected for simulation verification.The simulation results show that the RL algorithm based on pigeon-inspired optimization can improve the robustness of the system, reduce the computational cost, and reduce the algorithm’s dependence on the sample database.…”
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Water Quality Prediction Based on Hybrid Deep Learning Algorithm by Bhagavathi Perumal, Niveditha Rajarethinam, Anusuya Devi Velusamy, Venkatesa Prabhu Sundramurthy

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Binary Secretary Bird Optimization Algorithm for the Set Covering Problem by Broderick Crawford, Felipe Cisternas-Caneo, Ricardo Soto, Claudio Patricio Toledo Mac-lean, José Lara Arce, Fabián Solís-Piñones, Gino Astorga, Giovanni Giachetti

“…The use of metaheuristics to solve the SCP includes different algorithms. In particular, binarization techniques have been explored to adapt metaheuristics designed for continuous optimization problems to the binary domain of the SCP. …”
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GSPSO-LRF-ELM: Grid Search and Particle Swarm Optimization-Based Local Receptive Field-Enabled Extreme Learning Machine for Surface Defects Detection and Classification on the Magn... by Jun Xie, Jin Zhang, Fengmei Liang, Yunyun Yang, Xinying Xu, Junjie Dong

“…Machine vision-based surface defect detection and classification have always been the hot research topics in Artificial Intelligence. …”
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A New S-Box Design by Applying Bat Algorithm Based Technique by Maiya Din, Saibal K. Pal, S.K. Muttoo, Sushila Madan

“…In the proposed research work, Bat Algorithm based swarm technique is proposed to design strong S-boxes.  …”
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Exploring Evolutionary Algorithms for Multi-Objective Optimization in Seismic Structural Design by Seda Göktepe Körpeoğlu, Süleyman Mesut Yılmaz

“…Evolutionary computation, nature-inspired, and meta-heuristic algorithms have been studied more in recent years for the optimization of these devices. …”
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Evolutionary Algorithms Applied to Antennas and Propagation: A Review of State of the Art by Sotirios K. Goudos, Christos Kalialakis, Raj Mittra

“…In this paper, our primary focus is on Genetic Algorithms (GAs), Particle Swarm Optimization (PSO), and Differential Evolution (DE), though we also briefly review other recently introduced nature-inspired algorithms. …”
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Hybridization of meta-heuristic algorithm for load balancing in cloud computing environment by U.K. Jena, P.K. Das, M.R. Kabat

“…The robustness of the algorithm has been validated by comparing the results of the QMPSO obtained from the simulation process with the existing load balancing and scheduling algorithm. …”
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Genetic Algorithm Optimization for Determining Fuzzy Measures from Fuzzy Data by Chen Li, Gong Zeng-tai, Duan Gang

“…Though there have existed some methodologies for solving this problem, such as genetic algorithms, gradient descent algorithms, neural networks, and particle swarm algorithm, it is hard to say which one is more appropriate and more feasible. …”
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