Modified Bat Algorithm

Yılmaz, Selim; Küçüksille, Ecir Uğur; Cengiz, Yavuz

doi:10.5755/j01.eee.20.2.4762

Cited by 53 publications

(47 citation statements)

References 23 publications

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“…This mechanism enables bats to identify the type of the objects, the distance from the target, and the kind and speed of the prey. Bats have the capability to establish three-dimensional pictures around the hunting area using their advanced echolocation strategy [53,54].…”

Section: Bat Algorithm Methodsmentioning

confidence: 99%

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A Comparative Study on Recently-Introduced Nature-Based Global Optimization Methods in Complex Mechanical System Design

2017

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Advanced global optimization algorithms have been continuously introduced and improved to solve various complex design optimization problems for which the objective and constraint functions can only be evaluated through computation intensive numerical analyses or simulations with a large number of design variables. The often implicit, multimodal, and ill-shaped objective and constraint functions in high-dimensional and "black-box" forms demand the search to be carried out using low number of function evaluations with high search efficiency and good robustness. This work investigates the performance of six recently introduced, nature-inspired global optimization methods: Artificial Bee Colony (ABC), Firefly Algorithm (FFA), Cuckoo Search (CS), Bat Algorithm (BA), Flower Pollination Algorithm (FPA) and Grey Wolf Optimizer (GWO). These approaches are compared in terms of search efficiency and robustness in solving a set of representative benchmark problems in smooth-unimodal, non-smooth unimodal, smooth multimodal, and non-smooth multimodal function forms. In addition, four classic engineering optimization examples and a real-life complex mechanical system design optimization problem, floating offshore wind turbines design optimization, are used as additional test cases representing computationally-expensive black-box global optimization problems. Results from this comparative study show that the ability of these global optimization methods to obtain a good solution diminishes as the dimension of the problem, or number of design variables increases. Although none of these methods is universally capable, the study finds that GWO and ABC are more efficient on average than the other four in obtaining high quality solutions efficiently and consistently, solving 86% and 80% of the tested benchmark problems, respectively. The research contributes to future improvements of global optimization methods.

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Section: Bat Algorithm Methodsmentioning

confidence: 99%

“…Bats use a limited range of frequencies from 200 to 500 kHz. The steps of BA are described in [53][54][55].…”

Section: Bat Algorithm Methodsmentioning

confidence: 99%

A Comparative Study on Recently-Introduced Nature-Based Global Optimization Methods in Complex Mechanical System Design

2017

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“…[23] As shown in figure no 4 various testing strategies will be adopted for checking the accuracy and perfection of the system. Recommendation functions, GUI components, systems acceptance and accuracy will be tested and validated before adapting the particular algorithm for the recommendation system [17]. …”

Section: Testing Processmentioning

confidence: 99%

Nature Inspired Recommender Algorithms for Collaborative Web based Learning Environments

Saini¹,

Prakash²

2015

IJCA

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The design of recommender systems for various domains has been proposed based on the nature inspired algorithms. In this paper attempt is made to propose a Nature Inspired Algorithms based architecture for recommender system for web based learning environments. The paper also compares between the traditional recommender systems and the nature inspired algorithm recommender systems. Collaborative filtering is proposed for personalized recommendations; user and item attributes are used as filtration parameter. Attributes and rating of the user's similarity is used for collaborative filtering process. Hybrid collaborative filtering is proposed for user and item attribute that can alleviate the sparsity issue in the recommender systems. Traditional systems are studied in detail and all the possible limitations of the traditional systems are bought under attention.

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“…In following iterations, BA will be bad in exploration but good at exploitation and it is very likely that the algorithm will be trapped in local optima. [13] On the other hand, the process of updating solutions in equations (2), (3) and (4) increases the exploration capability of BA. In this case, BA will be very good at exploration and but bad in exploitation.…”

Section: Proposed Novel Adaptive Bat Algorithm 31 Problems In Bat Almentioning

confidence: 99%

“…Where ϵ indicates randomly generated number, is the frequency generated for solution i, represents the new velocity for solution i, represents the global best solutions in the population [13].…”

Section: Solution Frequency and Velocitymentioning

confidence: 99%

A Novel Adaptive Bat Algorithm to Control Explorations and Exploitations for Continuous Optimization Problems

Kabir¹,

Sakib²,

Chowdhury³

et al. 2014

IJCA

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Modified Bat Algorithm

Cited by 53 publications

References 23 publications

A Comparative Study on Recently-Introduced Nature-Based Global Optimization Methods in Complex Mechanical System Design

A Comparative Study on Recently-Introduced Nature-Based Global Optimization Methods in Complex Mechanical System Design

Nature Inspired Recommender Algorithms for Collaborative Web based Learning Environments

A Novel Adaptive Bat Algorithm to Control Explorations and Exploitations for Continuous Optimization Problems

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