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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Journal ArticleDOI
Animal migration optimization: an optimization algorithm inspired by animal migration behavior
TL;DR: Experimental results indicate that the proposed algorithm performs better than or at least comparable with state-of-the-art approaches from literature when considering the quality of the solution obtained.
Journal ArticleDOI
A survey on the Imperialist Competitive Algorithm metaheuristic
TL;DR: The present study is the first ever comprehensive review on ICA, which indicates a statistically significant increase in the amount of published research on this metaheuristic algorithm, especially research addressing discrete optimization problems.
Journal ArticleDOI
Probabilistic energy and operation management of a microgrid containing wind/ photovoltaic/fuel cell generation and energy storage devices based on point estimate method and self-adaptive gravitational search algorithm
TL;DR: In this paper, a probabilistic approach for the energy and operation management (EOM) of renewable microgrids under uncertain environment is proposed, which consists of 2m point estimate method for covering the existing uncertainties in the MGs and a self-adaptive optimization algorithm based on the gravitational search algorithm (GSA) to determine the optimal energy management of MGs.
Journal ArticleDOI
Farmland fertility: A new metaheuristic algorithm for solving continuous optimization problems
TL;DR: The farmland fertility in problems with smaller dimensions problems has been able to act as a strong metaheuristic algorithm and it has optimized problems nicely and the effectiveness of other algorithms decreases significantly with number of dimensions and the farmland fertility obtains better results than other algorithms.
Journal ArticleDOI
A novel metaheuristic optimizer inspired by behavior of jellyfish in ocean
Jui-Sheng Chou,Dinh-Nhat Truong +1 more
TL;DR: The JS algorithm was used to solve structural optimization problems, including 25- bar tower design and 582-bar tower design problems, where JS not only performed best but also required the fewest evaluations of objective functions.
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.