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
Artificial ecosystem-based optimization: a novel nature-inspired meta-heuristic algorithm
TL;DR: The overall comparisons suggest that the optimization performance of AEO outperforms that of other state-of-the-art counterparts, especially for real-world engineering problems, and is more competitive than other reported methods in terms of both convergence rate and computational efforts.
Journal ArticleDOI
A novel algorithm for global optimization: Rat Swarm Optimizer
TL;DR: The experimental results reveal that the proposed RSO algorithm is highly effective in solving real world optimization problems as compared to other well-known optimization algorithms.
Book ChapterDOI
Gravitational Search Algorithm
Bo Xing,Wen-Jing Gao +1 more
TL;DR: In this chapter, a gravitational search algorithm (GSA) which is based on the low of gravity is presented, and the fundamentals and performance of GSA are introduced.
Journal ArticleDOI
Exchange market algorithm
Naser Ghorbani,Ebrahim Babaei +1 more
TL;DR: The results indicate the ability of the proposed algorithm in finding the global optimum point of the functions for each run of the program.
Journal ArticleDOI
A hybrid GSA-GA algorithm for constrained optimization problems
TL;DR: A new hybrid GSA-GA algorithm is presented for the constraint nonlinear optimization problems with mixed variables that is tuned up with the gravitational search algorithm and each solution is upgraded with the genetic operators such as selection, crossover, mutation.
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.