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
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Mayfly in Harmony: A New Hybrid Meta-Heuristic Feature Selection Algorithm
Trinav Bhattacharyya,Bitanu Chatterjee,Pawan Kumar Singh,Jin Hee Yoon,Zong Woo Geem,Ram Sarkar +5 more
TL;DR: This paper has presented a new feature selection algorithm called Mayfly-Harmony Search (MA-HS) based on two meta-heuristics namely Mayfly Algorithm and Harmony Search, which has been applied on 18 UCI datasets and compared with 12 other state-of-the-art meta- heuristic FS methods.
Journal ArticleDOI
Multi-objective load dispatch for microgrid with electric vehicles using modified gravitational search and particle swarm optimization algorithm
TL;DR: In this article, a hybrid modified GSA-PSO scheme is proposed to optimize the load dispatch of the microgrid containing electric vehicles, where the global memory capacity of the PSO is introduced into the GSA to improve the global search performance.
Journal ArticleDOI
A new metaheuristic optimization method: the algorithm of the innovative gunner (AIG)
Paweł Pijarski,Piotr Kacejko +1 more
TL;DR: The algorithm of the innovative gunner (AIG), inspired by the choice of artillery parameters that sends a shot precisely to a target, is presented, characterized by high efficiency and speed in solving various optimization problems having nothing to do with ballistics.
Journal ArticleDOI
Modified hybrid bat algorithm with genetic crossover operation and smart inertia weight for multilevel image segmentation
Xiaofeng Yue,Hongbo Zhang +1 more
TL;DR: The proposed hybrid bat algorithm with genetic crossover operation and smart inertia weight (SGA-BA) is proposed to choose the optimal thresholds, and between-class variance and Kapur’s entropy are used as objective functions.
Journal ArticleDOI
Social Network Search for Global Optimization
TL;DR: In this article, a novel metaheuristic algorithm called Social Network Search (SNS) is developed for solving optimization problems, which simulates the attempts of users in social networks to gain more popularity by modeling the moods of users expressing their opinions.
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.