Journal ArticleDOI
GSA: A Gravitational Search Algorithm
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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 chaotic strategy-based quadratic Opposition-Based Learning adaptive variable-speed whale optimization algorithm
TL;DR: In this article, a chaotic strategy-based quadratic opposition-based learning adaptive variable speed whale optimization algorithm is proposed to solve the problems that the current algorithm's convergence accuracy and convergence speed are insufficient.
Journal ArticleDOI
Modeling a shape memory alloy actuator using an evolvable recursive black-box and hybrid heuristic algorithms inspired based on the annual migration of salmons in nature
Alireza Fathi,Ahmad Mozaffari +1 more
TL;DR: Results reveal the promising potential of the evolvable frameworks for modeling the behavior of SMA as a complex real world engineering system.
Journal ArticleDOI
A chaotic strategy-based quadratic Opposition-Based Learning adaptive variable-speed whale optimization algorithm
TL;DR: In this paper , a chaotic strategy-based quadratic opposition-based learning adaptive variable speed whale optimization algorithm is proposed to solve the problems that the current algorithm's convergence accuracy and convergence speed are insufficient.
Journal ArticleDOI
An Inter Type-2 FCR Algorithm Based T–S Fuzzy Model for Short-Term Wind Power Interval Prediction
Zou Wen,Chaoshun Li,Pengfei Chen +2 more
TL;DR: A novel fuzzy interval prediction model (FIPM) based on the lower upper bound estimation method and a novel inter type-2 (IT-2) fuzzy model is designed to construct the lower and upper bounds of the prediction interval (PI).
Journal ArticleDOI
Binary Genetic Swarm Optimization: A Combination of GA and PSO for Feature Selection
TL;DR: The proposed method, named BGSO, combines GA and PSO results by an algorithm called average weighted combination method to produce an intermediate solution and proves the applicability and usefulness of the method in the domain of FS.
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.