Robust trajectory tracking control of an underwater robotic manipulator using an IPSO-PID framework under hydrodynamic and ocean-current disturbances. [PDF]
Front Robot AIYao J, Yang J, Li C.
europepmc +1 more source
Correction: Discrimination of missing data types in metabolomics data based on particle swarm optimization algorithm and XGBoost model. [PDF]
Sci RepYuan Y +5 more
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Exploring multi-objective optimization scheduling strategies for power dispatch robots based on an improved particle swarm optimization algorithm framework. [PDF]
Sci RepYan H, Zhang M, Zhu Y, Mo X, Yang Z.
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Retrospection dropout bare-bones particle swarm optimization for feature-based brain tumor classification in MRI images. [PDF]
Front OncolChen W, Shi T, Guo J, Fu L.
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Flamingo Search Algorithm: A New Swarm Intelligence Optimization Algorithm [PDF]
IEEE Access, 2021 This paper presents a new swarm intelligence optimization algorithm: Flamingo Search Algorithm (FSA), which is inspired by the migratory and foraging behavior of flamingos. A mathematical model of flamingo behavior is built so that FSA has the global exploration and local exploitation capabilities required for an optimization algorithm.
Jianhua Liu, Zhiheng Wang
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A survey of swarm intelligence for portfolio optimization: Algorithms and applications
Swarm and Evolutionary Computation, 2018 Abstract In portfolio optimization (PO), often, a risk measure is an objective to be minimized or an efficient frontier representing the best tradeoff between return and risk is sought. In order to overcome computational difficulties of this NP-hard problem, a growing number of researchers have adopted swarm intelligence (SI) methodologies to deal ...
Can B Kalayci
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An Adaptive Location-Aware Swarm Intelligence Optimization Algorithm
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 2021Optimization is an important and decisive task in science. Many optimization problems in science are naturally too complicated and difficult to be modeled and solved by the conventional optimization methods such as mathematical programming problem solvers. Meta-heuristic algorithms that are inspired by nature have started a new era in computing theory
Shenghao Jiang +4 more
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Conceptual and numerical comparisons of swarm intelligence optimization algorithms
Soft Computing, 2015Swarm intelligence (SI) optimization algorithms are fast and robust global optimization methods, and have attracted significant attention due to their ability to solve complex optimization problems. The underlying idea behind all SI algorithms is similar, and various SI algorithms differ only in their details.
Dan Simon
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From Swarm Intelligence to Metaheuristics: Nature-Inspired Optimization Algorithms [PDF]
Computer, 2016 Nature has provided rich models for computational problem solving, including optimizations based on the swarm intelligence exhibited by fireflies, bats, and ants. These models can stimulate computer scientists to think nontraditionally in creating tools to address application design challenges.
Xin-She Yang, Simon Fong, Suash Deb
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Optimization of artificial CNN based on swarm intelligence algorithm
Journal of Intelligent & Fuzzy Systems, 2020Aiming at the precocious convergence, low search accuracy and easy divergence of most particle swarm optimizations with velocity terms, a particle swarm optimization (IWPSO) with random inertia weights and quantization is proposed. First, the inertia weights are obeyed to be distributed randomly, and the learning factors are adjusted asynchronously to ...
Qian Li, Shuyuan Li
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