Journal ArticleDOI
Manta ray foraging optimization: An effective bio-inspired optimizer for engineering applications
TLDR
The comparison results on the benchmark functions suggest that MRFO is far superior to its competitors, and the real-world engineering applications show the merits of this algorithm in tackling challenging problems in terms of computational cost and solution precision.About:
This article is published in Engineering Applications of Artificial Intelligence.The article was published on 2020-01-01. It has received 519 citations till now. The article focuses on the topics: Metaheuristic & Optimization problem.read more
Citations
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New machine learning method for image-based diagnosis of COVID-19.
Mohamed Abd Elaziz,Mohamed Abd Elaziz,Khalid M. Hosny,Ahmad Salah,Mohamed M. Darwish,Songfeng Lu,Ahmed T. Sahlol +6 more
TL;DR: A new ML-method is proposed to classify the chest x-ray images into two classes, COVID-19 patient or non-CO VID-19 person, using new Fractional Multichannel Exponent Moments (FrMEMs).
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Artificial hummingbird algorithm: A new bio-inspired optimizer with its engineering applications
TL;DR: In this paper, a bio-inspired optimization algorithm called artificial hummingbird algorithm (AHA) is proposed to solve optimization problems, which simulates the special flight skills and intelligent foraging strategies of hummingbirds in nature.
Journal ArticleDOI
Artificial hummingbird algorithm: A new bio-inspired optimizer with its engineering applications
TL;DR: In this article , a bio-inspired optimization algorithm called artificial hummingbird algorithm (AHA) is proposed to solve optimization problems, which simulates the special flight skills and intelligent foraging strategies of hummingbirds in nature.
Journal ArticleDOI
Snake Optimizer: A novel meta-heuristic optimization algorithm
TL;DR: In this paper , a novel nature-inspired metaheuristic algorithm named as snake optimizer (SO) is proposed to tackle a various set of optimization tasks which imitates the special mating behavior of snakes.
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An intensify Harris Hawks optimizer for numerical and engineering optimization problems
TL;DR: The proposed hHHO-SCA optimization algorithm is much better than standard sine–cosine optimization algorithm, Harris Hawks Optimizer, Ant Lion Optimizer algorithm, Moth Flame Optimization algorithm, grey wolf optimizer algorithm and others recently described meta-heuristics, heuristics and hybrid type optimization search algorithm and endorses its effectiveness in multi-disciplinary design and engineering optimization problems.
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.
Book
Adaptation in natural and artificial systems
TL;DR: Names of founding work in the area of Adaptation and modiication, which aims to mimic biological optimization, and some (Non-GA) branches of AI.
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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.
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Ant system: optimization by a colony of cooperating agents
TL;DR: It is shown how the ant system (AS) can be applied to other optimization problems like the asymmetric traveling salesman, the quadratic assignment and the job-shop scheduling, and the salient characteristics-global data structure revision, distributed communication and probabilistic transitions of the AS.
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No free lunch theorems for optimization
TL;DR: A framework is developed to explore the connection between effective optimization algorithms and the problems they are solving and a number of "no free lunch" (NFL) theorems are presented which establish that for any algorithm, any elevated performance over one class of problems is offset by performance over another class.