Nature-Inspired Algorithms: State-of-Art, Problems and Prospects

Agarwal, Parul; Mehta, Shikha

doi:10.5120/17593-8331

Cited by 57 publications

(46 citation statements)

References 51 publications

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“…Ants are unconscious, and have no sound. In addition, the ants cannot see or hear, but they are able to transmit information using a sense of smell (known as stigmergy [32]). When an ant walks, it leaves behind itself a chemical substance called pheromone which evaporates over the time, and the more pheromones, the shorter the pass (perhaps the shortest) can be reached [33].…”

Section: Ant Colony Optimization (Aco)mentioning

confidence: 99%

Impacts of pH, temperature, and pretreatment method on biohydrogen production from organic wastes by sewage microflora

Wongthanate¹,

Chinnacotpong²,

Khumpong³

2014

Int J Energy Environ Eng

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This paper focuses on higher order modeling and design of the free-piston Stirling engine (FPSE) based on Ant Colony Optimization (ACO). First, the governing thermodynamics and dynamical equations of the engine have been derived. Then, the design parameters of the engine are selected taking into account the finite heat transfer coefficient (resulting in a fifth-order model) and pressure drop (resulting in a sixth-order model) in the dynamical system and the corresponding differential equations are derived in detail. In the following, the mentioned methods and their performance in modeling the FPSE dynamics are investigated. The simulated results show that the effect of the pressure drop on the places of the closed-loop poles of the system is not significant, while the heat transfer coefficient has a considerable effect on the engine dynamics. Accordingly, a fifth-order model along with ACO algorithm is proposed to justify the FPSE behavior. To validate the presented modeling scheme, the prototype engine SUTECH-SR-1 was experimented. It is found that the values of parameters obtained from the proposed design method are close to those of the experiment. Besides, the presented higher order model predicts the engine behavior with an acceptable accuracy through which the validity of the design technique is affirmed. Initial pressure of working gas (Pa) P w Power generation (J/s) P Nonlinear pressure (Pa) R Ideal gas constant J kgVolume of expansion space m

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Section: Ant Colony Optimization (Aco)mentioning

confidence: 99%

Impacts of pH, temperature, and pretreatment method on biohydrogen production from organic wastes by sewage microflora

Wongthanate¹,

Chinnacotpong²,

Khumpong³

2014

Int J Energy Environ Eng

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“…The nature inspired optimization algorithms [5] have enabled lot of scope for the complex optimization problems. The complex problems have several factors such as number of variables, time complexity and space complexity etc, to affect the result.…”

Section: Fuzzy Partitioning With Acomentioning

confidence: 99%

A Novel Hybrid Image Analysis Using Aco and Fuzzy Logic for Textured and Non-Textured Images

K¹,

Sambasivaraon²,

Sammulal³

2018

IJCA

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Image analysis is the process of extracting quantitative and useful content from images by means of image processing approaches. In Image analysis, the classification process plays vital role in arranging the pixels into groups, called clusters that are similar in characteristics. In this paper a novel hybrid image classification method is proposed, which is used to analyze both textured and non-textured images. The image classification generally makes an attempt to group all the pixels that are strongly related to the objects in the image of interest. Classification rules are extracted from the input image using the unsupervised swarm intelligence technique called ant colony optimization (ACO), which are then used to group pixels. These classification rules are obtained from the training set of the image and further pruning process is applied to eliminate those rules that contribute less in the classification process on the test set of the image. Fuzzy partitioning technique along with ACO is used to generate simple rules to produce better results.

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“…Due to complexity and nonlinearity of many engineering optimization problems, classic optimization approaches may tend to fail. Nature and bio inspired algorithms seem superior to their classic counterparts and have found specific places between the newfangled algorithms [1]. The effectiveness of these algorithms had been proven in many fields such as industry, hardware design, urban design, routing, image processing, project scheduling, Controller design, robots path planning, data clustering and etc.…”

Section: Introductionmentioning

confidence: 99%

Modified Black Hole Algorithm with Genetic Operators

Yaghoobi¹,

Mojallali²

2016

IJCIS

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In this paper, a modified version of nature-inspired optimization algorithm called Black Hole has been proposed. The proposed algorithm is population based and consists of genetic algorithm operators in order to improve optimization results. The proposed method enhances Black Hole algorithm performance by searching space with more diversity. The modified Black Hole algorithm has been applied to a well-known benchmark. The experimental results show that the modified Black Hole algorithm outperforms compared to some prominent optimization algorithms.

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Nature-Inspired Algorithms: State-of-Art, Problems and Prospects

Cited by 57 publications

References 51 publications

Impacts of pH, temperature, and pretreatment method on biohydrogen production from organic wastes by sewage microflora

Impacts of pH, temperature, and pretreatment method on biohydrogen production from organic wastes by sewage microflora

A Novel Hybrid Image Analysis Using Aco and Fuzzy Logic for Textured and Non-Textured Images

Modified Black Hole Algorithm with Genetic Operators

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