Journal ArticleDOI
GSA: A Gravitational Search Algorithm
Reads0
Chats0
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
More filters
Journal ArticleDOI
Spherical evolution for solving continuous optimization problems
Deyu Tang,Deyu Tang +1 more
TL;DR: A new search style, namely a spherical searchstyle, inspired by the traditional hypercube search style is proposed, and a spherical evolution algorithm is proposed based on the search pattern and spherical search style.
Journal ArticleDOI
Hybrid Gravitational Search Algorithm-Particle Swarm Optimization with Time Varying Acceleration Coefficients for large scale CHPED problem
TL;DR: The obtained results by the suggested algorithm in terms of quality solution, computational performance, and convergence characteristic are compared with various algorithms to show the ability of the proposed approach and its robustness in finding a better fuel cost with a less expensive solution.
Journal ArticleDOI
Comparison of particle swarm optimization variants with fuzzy dynamic parameter adaptation for modular granular neural networks for human recognition
Journal ArticleDOI
A maximum power point tracking method for PV system with improved gravitational search algorithm
TL;DR: This study contributes to increase output efficiency of PV system by improving the tracking time and accuracy of existing MPPT methods Specifically, a MPPT method with improved gravitational search algorithm (IGSA-MPPT) was proposed, which has better performance in trackingTime and accuracy than other comparison algorithms.
Journal ArticleDOI
A memory-based gravitational search algorithm for solving economic dispatch problem in micro-grid
TL;DR: The proposed Memory-Based Gravitational Search Algorithm for solving the economic load dispatch in a micro-grid has been utilized to optimize power generation from multiple generation sources such as Photovoltaic systems, combined heat power (CHP) systems, and diesel generators and illustrates that the proposed method has higher performance in solving the optimal power generation problem compared to other methods.
References
More filters
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.