Design of multipurpose digital FIR double-band filter using hybrid firefly differential evolution algorithm

Dash, Judhisthir; Dam, Bivas; Swain, Ramakrushna

doi:10.1016/j.asoc.2017.06.025

Cited by 20 publications

(14 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, the search of global optimality is guided purely by a single swarm leader without consideration of multiple promising solutions in the neighbourhood, and it is more likely to be trapped in local optima. HFDE (Dash et al, 2017) incorporates FA to further improve the global search capabilities of IDE. In HFDE, although the FA movement is used to improve three top solutions identified by IDE, the search process largely relies on the mutation and crossover operations of IDE.…”

Section: Discussion On Different Search Methodsmentioning

confidence: 99%

“…zero) in the landscapes defined by the test functions. The FA variants implemented for performance comparison include Opposition and Dimensional based modified FA (ODFA) (Verma et al, 2016), CFA (Kazem et al, 2013), LSFA (Alweshah and Abdullah, 2015), SFA (Alweshah and Abdullah, 2015), NaFA (Wang et al, 2017), and HFDE (Dash et al, 2017). The classical search methods included for comparison are FA, SA, Bat Swarm Optimization (BSO), CS, PSO, Dragonfly Algorithm (DA) (Mirjalili, 2016a) and ALO (Mirjalili, 2015a).…”

Section: Evaluation Using Standard Benchmark Functionsmentioning

confidence: 99%

“…NaFA and HFDE also employ the abovementioned setting of FA. In addition, NaFA utilizes a neighbourhood size of 3 (as recommended in Wang et al (2017)) while HFDE uses a new coefficient of 1.8 for weighting factor calculation (as recommended in Dash et al (2017)). We have also conducted a number of trials to confirm the efficiency of these parameter settings of NaFA and HFDE for diverse optimization tasks.…”

Section: Evaluation Using Standard Benchmark Functionsmentioning

confidence: 99%

“…These two methods as well as LSFA also employ an extra number of function evaluations for identification or further improvement of the global best solution in each iteration (Verma et al, 2016;Alweshah and Abdullah, 2015). Comparing with our proposed model, HFDE (Dash et al, 2017) also employs a higher number of function evaluations to increase global exploration by integrating DE with FA.…”

Section: F10mentioning

confidence: 99%

See 3 more Smart Citations

Classifier ensemble reduction using a modified firefly algorithm: An empirical evaluation

Zhang

Srisukkham

Neoh

et al. 2018

Expert Systems with Applications

View full text Add to dashboard Cite

In this research, we propose a variant of the firefly algorithm (FA) for classifier ensemble reduction. It incorporates both accelerated attractiveness and evading strategies to overcome the premature convergence problem of the original FA model. The attractiveness strategy takes not only the neighbouring but also global best solutions into account, in order to guide the firefly swarm to reach the optimal regions with fast convergence while the evading action employs both neighbouring and global worst solutions to drive the search out of gloomy regions. The proposed algorithm is subsequently used to conduct discriminant base classifier selection for generating optimized ensemble classifiers without compromising classification accuracy. Evaluated with standard, shifted, and composite test functions, as well as the Black-Box Optimization Benchmarking test suite and several high dimensional UCI data sets, the empirical results indicate that, based on statistical tests, the proposed FA model outperforms other state-of-the-art FA variants and classical metaheuristic search methods in solving diverse complex unimodal and multimodal optimization and ensemble reduction problems. Moreover, the resulting ensemble classifiers show superior performance in comparison with those of the original, full-sized ensemble models.

show abstract

Section: Discussion On Different Search Methodsmentioning

confidence: 99%

Section: Evaluation Using Standard Benchmark Functionsmentioning

confidence: 99%

Section: Evaluation Using Standard Benchmark Functionsmentioning

confidence: 99%

Section: F10mentioning

confidence: 99%

See 2 more Smart Citations

Classifier ensemble reduction using a modified firefly algorithm: An empirical evaluation

Zhang

Srisukkham

Neoh

et al. 2018

Expert Systems with Applications

View full text Add to dashboard Cite

show abstract

“…In 2017, Jadon et al proposed a hybridization of artificial bee colony (ABC) and DE algorithms to develop a new meta-heuristic algorithm with better convergence speed and a better balance between exploration and exploitation abilities [40]. Dash et al introduced a hybrid meta-heuristic technique known as hybrid firefly differential evolution (HFDE) [41]. The firefly movement was used as an additional search of DE to avoid local optimization and improve convergence performance.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Grasshopper Optimization Algorithm and Differential Evolution for Multilevel Satellite Image Segmentation

et al. 2019

View full text Add to dashboard Cite

An efficient satellite image segmentation method based on a hybrid grasshopper optimization algorithm (GOA) and minimum cross entropy (MCE) is proposed in this paper. The proposal is known as GOA–jDE, and it merges GOA with self-adaptive differential evolution (jDE) to improve the search efficiency, preserving the population diversity especially in the later iterations. A series of experiments is conducted on various satellite images for evaluating the performance of the algorithm. Both low and high levels of the segmentation are taken into account, increasing the dimensionality of the problem. The proposed approach is compared with the standard color image thresholding methods, as well as the advanced satellite image thresholding techniques based on different criteria. Friedman test and Wilcoxon’s rank sum test are performed to assess the significant difference between the algorithms. The superiority of the proposed method is illustrated from different aspects, such as average fitness function value, peak signal to noise ratio (PSNR), structural similarity index (SSIM), feature similarity index (FSIM), standard deviation (STD), convergence performance, and computation time. Furthermore, natural images from the Berkeley segmentation dataset are also used to validate the strong robustness of the proposed method.

show abstract

A Novel Hybrid Genetic-Simulated Annealing Algorithm to Multi-band Filter Design Problem

Wielgus

2021

Lecture Notes in Networks and Systems

View full text Add to dashboard Cite

Design of multipurpose digital FIR double-band filter using hybrid firefly differential evolution algorithm

Cited by 20 publications

References 31 publications

Classifier ensemble reduction using a modified firefly algorithm: An empirical evaluation

Classifier ensemble reduction using a modified firefly algorithm: An empirical evaluation

Hybrid Grasshopper Optimization Algorithm and Differential Evolution for Multilevel Satellite Image Segmentation

A Novel Hybrid Genetic-Simulated Annealing Algorithm to Multi-band Filter Design Problem

Contact Info

Product

Resources

About