Swarm Intelligence applied in synthesis of hunting strategies in a three-dimensional environment

Castro, Emiliano Gonçalves de; Tsuzuki, Marcos de Sales Guerra

doi:10.1016/j.eswa.2007.02.031

Cited by 8 publications

(4 citation statements)

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“…where c max is the maximum value for the crystallization factor c k to use the Bates distribution. If c k is larger than c max , the proposed algorithm uses the Gaussian distribution and the value of the exponent is negative in (3). The Gaussian distribution has limits between −∞ and +∞; however, as its standard deviation is very small, it produces small numbers with high probability.…”

Section: Yes Nomentioning

confidence: 99%

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Using Feedback Strategies in Simulated Annealing with Crystallization Heuristic and Applications

et al. 2021

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This paper represents how typical advanced engineering design can be structured using a set of parameters and objective functions corresponding to the nature of the problem. The set of parameters can be in different types, including integer, real, cyclic, combinatorial, interval, etc. Similarly, the objective function can be presented in various types including integer (discrete), float, and interval. The simulated annealing with crystallization heuristic can deal with all these combinations of parameters and objective functions when the crystallization heuristic presents a sensibility for real parameters. Herein, simulated annealing with the crystallization heuristic is enhanced by combining Bates and Gaussian distributions and by incorporating feedback strategies to emphasize exploration or refinement, or a combination of the two. The problems that are studied include solving an electrical impedance tomography problem with float parameters and a partially evaluated objective function represented by an interval requiring the solution of 32 sparse linear systems defined by the finite element method, as well as an airplane design problem with several parameters and constraints used to reduce the explored domain. The combination of the proposed feedback strategies and simulated annealing with the crystallization heuristic is compared with existing simulated annealing algorithms and their benchmark results are shown. The enhanced simulated annealing approach proposed herein showed better results for the majority of the studied cases.

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Section: Yes Nomentioning

confidence: 99%

“…GA has two conventional operators: crossover and mutation. Particle Swarm Optimization (PSO) [3] models the learning task, where creatures can learn from themselves or from others. Simulated annealing (SA) [4] is a stochastic metaheuristic which models metal annealing.…”

Section: Introductionmentioning

confidence: 99%

Using Feedback Strategies in Simulated Annealing with Crystallization Heuristic and Applications

et al. 2021

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“…This work presents a tool to be able to lead to a multi-element agent system, which provides the group means of the various strategies to obtain the desired results, work as well as the coordinated and cooperative manner. Castro and Tsuzuki [4] raised a question, which is the basis where the enemies' group must be taken to capture threedimensional continuous around the prey. In this system, each particle i in x i position and with a v i velocity is updated at each iteration t, in accordance to the following:…”

Section: Swarm Intelligencementioning

confidence: 99%

Applications of intelligent computation

Zhang

2010

2010 2nd International Conference on Advanced Computer Control

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This paper consists of a survey of various engineering, computational biology, medicine, etc applications based on the intelligent computation, and also a summary of the recent techniques such as still swarm intelligence, artificial immune systems, business intelligence, etc. Intelligent computation can be powerful tools in a wide variety of practical applications. Keywords-intelligent computation; swarm intelligence; artificial immune systems; business intelligence978-1-4244-5848-6/10/$26.00 ©2010 IEEE

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“…Many evolutionary algorithms have been used for feature selection, which include genetic algorithms and swarm algorithms [16]. Swarm algorithms include, in turn, Ant Colony Optimization (ACO) [5,17,18], Particle Swarm Optimization (PSO) [19], Bat Algorithm (BAT) [2], and Artificial Bee Colony [1,[20][21][22].…”

Section: Related Concepts and Workmentioning

confidence: 99%

Data feature selection based on Artificial Bee Colony algorithm

2013

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Classification of data in large repositories requires efficient techniques for analysis since a large amount of features is created for better representation of such images. Optimization methods can be used in the process of feature selection to determine the most relevant subset of features from the data set while maintaining adequate accuracy rate represented by the original set of features. Several bioinspired algorithms, that is, based on the behavior of living beings of nature, have been proposed in the literature with the objective of solving optimization problems. This paper aims at investigating, implementing, and analyzing a feature selection method using the Artificial Bee Colony approach to classification of different data sets. Various UCI data sets have been used to demonstrate the effectiveness of the proposed method against other relevant approaches available in the literature.

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Swarm Intelligence applied in synthesis of hunting strategies in a three-dimensional environment

Cited by 8 publications

References 1 publication

Using Feedback Strategies in Simulated Annealing with Crystallization Heuristic and Applications

Using Feedback Strategies in Simulated Annealing with Crystallization Heuristic and Applications

Applications of intelligent computation

Data feature selection based on Artificial Bee Colony algorithm

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