Journal ArticleDOI
GSA: A Gravitational Search Algorithm
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TLDR
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.About:
This article is published in Information Sciences.The article was published on 2009-06-01. It has received 5501 citations till now. The article focuses on the topics: Metaheuristic & Best-first search.read more
Citations
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A stability constrained adaptive alpha for gravitational search algorithm
TL;DR: This paper proposes a new variant of GSA, namely stability constrained adaptive alpha for GSA (SCAA), which has been evaluated by comparing with the original GSA and four alpha adjusting algorithms on 13 conventional functions and 15 complex CEC2015 functions and demonstrated that SCAA has significantly better searching performance than its peers do.
Journal ArticleDOI
A novel gravitational acceleration enhanced particle swarm optimization algorithm for wind–thermal economic emission dispatch problem considering wind power availability
TL;DR: In this article, a wind-thermal economic emission dispatch (WTEED) model considering the coordination of power allocation from thermal and wind power generators is established, and a newly developed optimization approach, known as gravitational acceleration enhanced particle swarm optimization algorithm (GAEPSO), has been adopted to solve the model in this work.
Journal ArticleDOI
Optimizing the Design of Airfoil and Optical Buffer Problems Using Spotted Hyena Optimizer
Gaurav Dhiman,Amandeep Kaur +1 more
TL;DR: The main concept of this work is to apply the recently developed SHO algorithm to two real-life design problems, namely optical buffer design and airfoil design, and reveal the supremacy of the SHO algorithms for solving the engineering design problems as compared to other competitor algorithms.
Journal ArticleDOI
Automated soil prediction using bag-of-features and chaotic spider monkey optimization algorithm
TL;DR: An automated system for categorization of the soil datasets into respective categories using images of the soils using Bag-of-words and chaotic spider monkey optimization based method which can further be used for the decision of crops.
Journal ArticleDOI
A novel hybrid system for feature selection based on an improved gravitational search algorithm and k-NN method
TL;DR: A novel hybrid system to improve classification accuracy with an appropriate feature subset in binary problems based on an improved gravitational search algorithm that is able to select the discriminating input features correctly and achieve high classification accuracy which is comparable to or better than well-known similar classifier systems.
References
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Journal ArticleDOI
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.
Proceedings ArticleDOI
Particle swarm optimization
TL;DR: A concept for the optimization of nonlinear functions using particle swarm methodology is introduced, and the evolution of several paradigms is outlined, and an implementation of one of the paradigm is discussed.
Book
Artificial Intelligence: A Modern Approach
Stuart Russell,Peter Norvig +1 more
TL;DR: In this article, the authors present a comprehensive introduction to the theory and practice of artificial intelligence for modern applications, including game playing, planning and acting, and reinforcement learning with neural networks.
Journal ArticleDOI
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.
Journal ArticleDOI
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.