An enhanced binary Rat Swarm Optimizer based on local-best concepts of PSO and collaborative crossover operators for feature selection حسب Abu Zitar, Raed

"…In this paper, a recent swarm intelligence metaheuristic method called RSO which is inspired by the social and hunting behavior of a group of rats is enhanced and explored for FS problems. The binary enhanced RSO is built based on three successive modifications: i) an S-shape transfer function is used to develop binary RSO algorithms; ii) the local search paradigm of particle swarm optimization is used with the iterative loop of RSO to boost its local exploitation; iii) three crossover mechanisms are used and controlled by a switch probability to improve the diversity. …"

Optimized Load-Scheduling Algorithm for CubeSat's Electric Power System Management Considering Communication Link حسب Bayan Hussein (16904856)

Comparative analysis of metaheuristic load balancing algorithms for efficient load balancing in cloud computing حسب Jincheng Zhou (1887307)

"…This paper provides a comparative analysis of various metaheuristic load balancing algorithms for cloud computing based on performance factors i.e., Makespan time, degree of imbalance, response time, data center processing time, flow time, and resource utilization. …"

A new genetic algorithm approach for unit commitment حسب Mantawy, A.H.

"…In the proposed algorithm, coding the solution of the unit commitment problem is based on mixing binary and decimal representations. …"
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Meta Reinforcement Learning for UAV-Assisted Energy Harvesting IoT Devices in Disaster-Affected Areas حسب Marwan Dhuheir (19170898)

"…We conducted extensive simulations and compared our approach with two state-of-the-art models using traditional RL algorithms represented by a deep Q-network algorithm, a Particle Swarm Optimization (PSO) algorithm, and one greedy solution. …"

Intelligent route to design efficient CO₂ reduction electrocatalysts using ANFIS optimized by GA and PSO حسب Majedeh Gheytanzadeh (17541927)

"…The primary purpose of this study is to establish a new model through machine learning methods; namely, adaptive neuro-fuzzy inference system (ANFIS) combined with particle swarm optimization (PSO) and genetic algorithm (GA) for the prediction of *CO (the key intermediate) adsorption energy as the efficiency metric. …"