Optimal design of linear phase multi-band stop filters using improved cuckoo search particle swarm optimization

Dash, Judhisthir; Dam, Bivas; Swain, Ramakrushna

doi:10.1016/j.asoc.2016.10.024

Cited by 31 publications

(6 citation statements)

References 24 publications

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“…Hybrid PSO CS Compressed image classification Ye et al [123] 2015 Hybrid CSA with PSO Optimization of parameters of SVM Li and Yin [124] 2015 PSCS Global optimization Chen et al [125] 2015 PSOCS Artificial Neural Networks Guo et al [126] 2016 PSOCS Preventive maintenance period optimization model Chi et al [127] 2017 CSPSO Optimization problems Dash et al [128] 2017 ICSPSO Linear phase multi-band stop filters 4.8.6. Hybridization of PSO using Artificial Bee Colony (ABC)…”

Section: Author/smentioning

confidence: 99%

“…The global search capability of PSO has been exploited ensuring information exchange among the cuckoos in the search process. Dash et al [128] introduced improved cuckoo search particle swarm optimization (ICSPSO) where the optimization strategy of Differential Evolution (DE) Algorithm is incorporated for searching effectively around the group best solution vectors at each iteration, ensuring the global search capability of hybrid CSPSO and implemented this in designing linear phase multi-band stop filters.…”

Section: Table 2 a Collection Of Hybridized De-pso Algorithmsmentioning

confidence: 99%

See 1 more Smart Citation

Particle Swarm Optimization: A Survey of Historical and Recent Developments with Hybridization Perspectives

Sengupta

Basak

Peters

2018

MAKE

387

184

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Particle Swarm Optimization (PSO) is a metaheuristic global optimization paradigm that has gained prominence in the last two decades due to its ease of application in unsupervised, complex multidimensional problems which cannot be solved using traditional deterministic algorithms. The canonical particle swarm optimizer is based on the flocking behavior and social cooperation of birds and fish schools and draws heavily from the evolutionary behavior of these organisms. This paper serves to provide a thorough survey of the PSO algorithm with special emphasis on the development, deployment and improvements of its most basic as well as some of the very recent state-of-the-art implementations. Concepts and directions on choosing the inertia weight, constriction factor, cognition and social weights and perspectives on convergence, parallelization, elitism, niching and discrete optimization as well as neighborhood topologies are outlined. Hybridization attempts with other evolutionary and swarm paradigms in selected applications are covered and an up-to-date review is put forward for the interested reader.

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Section: Author/smentioning

confidence: 99%

Section: Table 2 a Collection Of Hybridized De-pso Algorithmsmentioning

confidence: 99%

Particle Swarm Optimization: A Survey of Historical and Recent Developments with Hybridization Perspectives

Sengupta

Basak

Peters

2018

MAKE

387

184

View full text Add to dashboard Cite

show abstract

“…Optimal parameter settings were also explored in their work. Dash et al [13] developed two hybrid evolutionary models, known as CSPSO and ICSPSO, respectively. The search strategies of PSO and Cuckoo Search (CS) were integrated to increase local exploitation and global exploration of the original PSO algorithm.…”

Section: Skin Cancer Detectionmentioning

confidence: 99%

Intelligent skin cancer detection using enhanced particle swarm optimization

Tan

Zhang

Neoh

et al. 2018

Knowledge-Based Systems

105

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In this research, we undertake intelligent skin cancer diagnosis based on dermoscopic images using a variant of the Particle Swarm Optimization (PSO) algorithm for feature optimization. Since the identification of the most significant discriminative characteristics of the benign and malignant skin lesions plays an important role in robust skin cancer detection, the proposed PSO algorithm is employed for feature optimization. It incorporates not only subswarms, local and global food and enemy signals, attraction and flee operations, and mutation-based local exploitation, but also diverse matrix representations to mitigate premature convergence of the original PSO algorithm. Specifically, two remote swarm leaders, which show similar fitness but low position proximity, are used to lead the subswarm-based search and to enable the exploration of more distinctive search regions. Modified velocity updating strategies are also proposed to enable the particles to follow multiple swarm leaders and avoid the local and global worst individuals, partially (i.e. in randomly selected sub-dimensions) and fully (in every dimension), with an attempt to search for global optima. Probability distribution and dynamic matrix representations are used to diversify the search process. Evaluated with multiple skin lesion and UCI databases and diverse unimodal and multimodal benchmark functions, the proposed PSO variant shows a superior performance over those of other advanced and classical search methods for identifying discriminative features that facilitate benign and malignant lesion classification as well as for solving diverse optimization problems with different landscapes. The Wilcoxon rank sum test is adopted to further ascertain superiority of the proposed algorithm over other methods statistically.

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“…CS is successful for finding optimum values of multimodal functions. A hybrid algorithm is proposed finding for optimal design of multiband stop filters [46].…”

Section: Cuckoo Searchmentioning

confidence: 99%

Introductory Chapter: Swarm Intelligence and Particle Swarm Optimization

Erdoğmuş¹

2018

Particle Swarm Optimization With Applications

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Optimal design of linear phase multi-band stop filters using improved cuckoo search particle swarm optimization

Cited by 31 publications

References 24 publications

Particle Swarm Optimization: A Survey of Historical and Recent Developments with Hybridization Perspectives

Particle Swarm Optimization: A Survey of Historical and Recent Developments with Hybridization Perspectives

Intelligent skin cancer detection using enhanced particle swarm optimization

Introductory Chapter: Swarm Intelligence and Particle Swarm Optimization

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