Extractive Summary: An Optimization Approach Using Bat Algorithm

2019 ◽  
pp. 175-186 ◽  
Author(s):  
Anshuman Pattanaik ◽  
Santwana Sagnika ◽  
Madhabananda Das ◽  
Bhabani Sankar Prasad Mishra
Keyword(s):  
Bat Algorithm ◽  
Optimization Approach

Optimization Techniques for the Multilevel Thresholding of the Medical Images

2019 ◽  
pp. 166-184 ◽  
Author(s):  
Taranjit Kaur ◽  
Barjinder Singh Saini ◽  
Savita Gupta
Keyword(s):  
Clinical Information ◽  
Bat Algorithm ◽  
Magnetic Resonance Images ◽  
Optimization Techniques ◽  
Optimization Approach ◽  
Multilevel Thresholding ◽  
Web Database ◽  
Entropy Maximization ◽  
Optimal Thresholds ◽  
Simulated Images

Multilevel thresholding is segmenting the image into several distinct regions. Medical data like magnetic resonance images (MRI) contain important clinical information that is crucial for diagnosis. Hence, automatic segregation of tissue constituents is of key interest to clinician. In the chapter, standard entropies (i.e., Kapur and Tsallis) are explored for thresholding of brain MR images. The optimal thresholds are obtained by the maximization of these entropies using the particle swarm optimization (PSO) and the BAT optimization approach. The techniques are implemented for the segregation of various tissue constituents (i.e., cerebral spinal fluid [CSF], white matter [WM], and gray matter [GM]) from simulated images obtained from the brain web database. The efficacy of the thresholding technique is evaluated by the Dice coefficient (Dice). The results demonstrate that Tsallis' entropy is superior to the Kapur's entropy for the segmentation CSF and WM. Moreover, entropy maximization using BAT algorithm attains a higher Dice in contrast to PSO.

scholarly journals A Novel Hybrid Bat Algorithm with Harmony Search for Global Numerical Optimization

2013 ◽  
Vol 2013 ◽  
pp. 1-21 ◽  
Author(s):  
Gaige Wang ◽  
Lihong Guo
Keyword(s):  
Numerical Optimization ◽  
Optimization Problems ◽  
Harmony Search ◽  
Bat Algorithm ◽  
Optimization Methods ◽  
Population Based ◽  
Optimization Approach ◽  
Global Numerical Optimization ◽  
Hybrid Metaheuristic ◽  
Real World Applications

A novel robust hybrid metaheuristic optimization approach, which can be considered as an improvement of the recently developed bat algorithm, is proposed to solve global numerical optimization problems. The improvement includes the addition of pitch adjustment operation in HS serving as a mutation operator during the process of the bat updating with the aim of speeding up convergence, thus making the approach more feasible for a wider range of real-world applications. The detailed implementation procedure for this improved metaheuristic method is also described. Fourteen standard benchmark functions are applied to verify the effects of these improvements, and it is demonstrated that, in most situations, the performance of this hybrid metaheuristic method (HS/BA) is superior to, or at least highly competitive with, the standard BA and other population-based optimization methods, such as ACO, BA, BBO, DE, ES, GA, HS, PSO, and SGA. The effect of the HS/BA parameters is also analyzed.

A New Stochastic Optimization Approach — Dolphin Swarm Optimization Algorithm

2016 ◽  
Vol 15 (02) ◽  
pp. 1650011 ◽  
Author(s):  
Wang Yong ◽  
Wang Tao ◽  
Zhang Cheng-Zhi ◽  
Huang Hua-Juan
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Artificial Bee Colony ◽  
Bat Algorithm ◽  
Global Optimum ◽  
The Other ◽  
Optimization Approach ◽  
Swarm Optimization ◽  
Bee Colony ◽  
Fast Convergence Rate

A novel nature-inspired swarm intelligence (SI) optimization is proposed called dolphin swarm optimization algorithm (DSOA), which is based on mimicking the mechanism of dolphins in detecting, chasing after, and preying on swarms of sardines to perform optimization. In order to test the performance, the DSOA is evaluated against the corresponding results of three existing well-known SI optimization algorithms, namely, particle swarm optimization (PSO), bat algorithm (BA), and artificial bee colony (ABC), in the terms of the ability to find the global optimum of a range of the popular benchmark functions. The experimental results show that the proposed optimization seems superior to the other three algorithms, and the proposed algorithm has the performance of fast convergence rate, and high local optimal avoidance.

Interactive multiobjective DEA target setting using lexicographic DDF

2020 ◽  
Vol 54 (6) ◽  
pp. 1703-1722 ◽  
Author(s):  
Narges Soltani ◽  
Sebastián Lozano
Keyword(s):  
Efficient Frontier ◽  
Directional Distance Function ◽  
Fixed Number ◽  
Optimization Approach ◽  
Target Setting ◽  
Interactive Process ◽  
Decision Making Unit ◽  
Setting Approach ◽  
Promising Area ◽  
Current Region

In this paper, a new interactive multiobjective target setting approach based on lexicographic directional distance function (DDF) method is proposed. Lexicographic DDF computes efficient targets along a specified directional vector. The interactive multiobjective optimization approach consists in several iteration cycles in each of which the Decision Making Unit (DMU) is presented a fixed number of efficient targets computed corresponding to different directional vectors. If the DMU finds one of them promising, the directional vectors tried in the next iteration are generated close to the promising one, thus focusing the exploration of the efficient frontier on the promising area. In any iteration the DMU may choose to finish the exploration of the current region and restart the process to probe a new region. The interactive process ends when the DMU finds its most preferred solution (MPS).

The Current Carbon Sink Structure of China's Staple Crop Production and Its Optimization Approach

2016 ◽  
Vol 18 (1) ◽  
pp. 114
Author(s):  
She Wei ◽  
Huang Huang ◽  
Guan Chunyun ◽  
Chen Fu ◽  
Chen Guanghui
Keyword(s):  
Crop Production ◽  
Carbon Sink ◽  
Optimization Approach ◽  
Staple Crop

A Combinatorial Optimization Approach to Solve the Synchronous Optical Network (SONET) Problem

2014 ◽  
Vol 6 (2) ◽  
pp. 9
Author(s):  
Mamoon Alameen ◽  
Mohammad Abdul-Niby ◽  
Tarek Selmi ◽  
Sadeq Damrah
Keyword(s):  
Combinatorial Optimization ◽  
Optical Network ◽  
Optimization Approach
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