Journal ArticleDOI
A chaotic strategy-based quadratic Opposition-Based Learning adaptive variable-speed whale optimization algorithm
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In this paper , a chaotic strategy-based quadratic opposition-based learning adaptive variable speed whale optimization algorithm is proposed to solve the problems that the current algorithm's convergence accuracy and convergence speed are insufficient.About:
This article is published in Mathematics and Computers in Simulation.The article was published on 2022-03-01. It has received 43 citations till now. The article focuses on the topics: Chaotic & Quadratic equation.read more
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Journal ArticleDOI
Chaotic Search-and-Rescue-Optimization-Based Multi-Hop Data Transmission Protocol for Underwater Wireless Sensor Networks
D. Anuradha,Neelakandan Subramani,Osamah Ibrahim Khalaf,Youseef Alotaibi,Saleh A. Alghamdi,Manjula Rajagopal +5 more
TL;DR: A novel chaotic search-and-rescue-optimization-based multi-hop data transmission (CSRO-MHDT) protocol for UWSNs that resulted in higher values of number of packets received (NPR) under all rounds and a chaotic search and rescue optimization algorithm for route optimization, which was developed in-house.
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Model recovery for multi-input signal-output nonlinear systems based on the compressed sensing recovery theory
TL;DR: In this article , a data filtering-based orthogonal matching pursuit algorithm is presented for estimating the system parameters and the orders, which can obtain highly accurate estimates from a small number of measurements by finding the highest absolute inner product.
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Least squares parameter estimation and multi-innovation least squares methods for linear fitting problems from noisy data
TL;DR: The results of the least squares and multi-innovation least squares algorithms for linear regressive systems with white noises can be extended to other systems with colored noises as mentioned in this paper , and the results of least square and multinomial least square algorithms can be generalized to other problems with different noises.
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Modeling nonlinear systems using the tensor network B‐spline and the multi‐innovation identification theory
TL;DR: The TNBS can fit nonlinear systems with strong nonlinearity by the meaning of setting a proper degree and knots number and the recursive algorithm by combining the l2$$ {l}_2 $$ ‐norm is proposed to the NARX system with Gaussian noise.
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Overall recursive least squares and overall stochastic gradient algorithms and their convergence for feedback nonlinear controlled autoregressive systems
TL;DR: In this paper , an overall recursive least squares algorithm is developed to handle the difficulty of the bilinear-in-parameter identification model and an overall stochastic gradient algorithm is deduced and the forgetting factor is introduced to improve the convergence rate.
References
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Optimization by Simulated Annealing
TL;DR: There is a deep and useful connection between statistical mechanics and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters), and a detailed analogy with annealing in solids provides a framework for optimization of very large and complex systems.
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Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces
Rainer Storn,Kenneth Price +1 more
TL;DR: In this article, a new heuristic approach for minimizing possibly nonlinear and non-differentiable continuous space functions is presented, which requires few control variables, is robust, easy to use, and lends itself very well to parallel computation.
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Grey Wolf Optimizer
TL;DR: The results of the classical engineering design problems and real application prove that the proposed GWO algorithm is applicable to challenging problems with unknown search spaces.
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The Whale Optimization Algorithm
Seyedali Mirjalili,Andrew Lewis +1 more
TL;DR: Optimization results prove that the WOA algorithm is very competitive compared to the state-of-art meta-heuristic algorithms as well as conventional methods.
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GSA: A Gravitational Search Algorithm
TL;DR: A new optimization algorithm based on the law of gravity and mass interactions is introduced and the obtained results confirm the high performance of the proposed method in solving various nonlinear functions.