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Journal ArticleDOI

Chaotic hunger games search optimization algorithm for global optimization and engineering problems

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TLDR
Chaotic maps have the characteristics of ergodicity and non-repeatability, and they provide fast convergence by effectively scanning the search space in a metaheuristic optimization algorithm as mentioned in this paper.
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This article is published in Mathematics and Computers in Simulation.The article was published on 2022-02-01. It has received 29 citations till now. The article focuses on the topics: Chaotic & Benchmark (surveying).

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Citations
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An improved remora optimization algorithm with autonomous foraging mechanism for global optimization problems.

TL;DR: Test results confirm the effectiveness of the proposed improved ROA (IROA) by introducing a new mechanism named autonomous foraging mechanism (AFM), which is inspired from the fact that remora can also find food on its own.
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Nature-inspired optimization algorithms and their significance in multi-thresholding image segmentation: an inclusive review

TL;DR: In this article , a review of NIOA-based multi-level thresholding models is presented, highlighting and exploring the major challenges encountered during the development of image multi-thresholding models.
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Gradient-based optimizer for economic optimization of engineering problems

TL;DR: In this paper , the authors proposed a gradient-based optimization (GBO) algorithm to optimize fin and tube heat exchanger (FTHE) with various constraints and design variables, with the total cost as the objective function.
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A novel modified opposition‐based hunger games search algorithm to design fractional order proportional‐integral‐derivative controller for magnetic ball suspension system

TL;DR: In this article , a modified opposition-based hunger games search (mOBL•HGS) algorithm is used as an efficient tool to tune a fractional order proportional integral derivative (FOPID) controller in order to control a magnetic ball suspension system with greater flexibility.
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Grasshopper Optimization Algorithm With Crossover Operators for Feature Selection and Solving Engineering Problems

- 01 Jan 2022 - 
TL;DR: Wang et al. as discussed by the authors proposed a new method using crossover-salp swarm with grasshopper optimization algorithm (cSG), in which the crossover operators are used to maintain the population of the SSA then the improved SSA is used as a local search to boost the exploration phase of the GOA, which prevents the cSG from premature convergence, high computation time, and being trapped in local minimum.
References
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Journal ArticleDOI

Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces

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.
Journal ArticleDOI

Particle swarm optimization

TL;DR: A snapshot of particle swarming from the authors’ perspective, including variations in the algorithm, current and ongoing research, applications and open problems, is included.
Book

Genetic Algorithms

Book

Genetic Programming: On the Programming of Computers by Means of Natural Selection

TL;DR: This book discusses the evolution of architecture, primitive functions, terminals, sufficiency, and closure, and the role of representation and the lens effect in genetic programming.
Journal ArticleDOI

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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