A New Classification Method for Computational Intelligence

Chai, Yuanyuan; Jia, Limin; Zhang, Zundong

doi:10.1109/icicic.2008.55

Cited by 5 publications

(10 citation statements)

References 6 publications

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“…In this section, in order to investigate the proposed algorithm performance, the results of the fifteen datasets simulation related to the microarray data of different types of cancers, compared to the other common methods, are reported in several subsections as follows. The assessment results of the SBCSO algorithm, compared to the binary single-objective optimization algorithms, such as the BACO [13], BBA [25], BCSO [23], BDA [26], BGSA [35], BGA [42], BPSO [21], and BGWO [22], are reported in subsection 4.3. Finally, the assessment results of the SBCSO algorithm, compared to the binary multi-objective optimization algorithms, such as BMOCSO [46], BNSGA-II [45], BMODE [43], BMOPSO [44], and BMOBBA [47], are reported in subsection 4.5.…”

Section: Resultsmentioning

confidence: 99%

“…Binary dragonfly algorithm (BDA) [26] beta 1.5 Inertia weight (w) 0.9 to 0.2 Binary bat algorithm (BBA) [25], binary multi-objective bat optimization algorithm (BMOBBA) [47] α 0.9 γ 0.9…”

Section: Mutation Rate 1/dimensionmentioning

confidence: 99%

“…Optimization algorithms employing S-shape functions include BPSO [21], BGWO [22] and BCSO [23]. Also, the BGSA [24], BBA [25], BDA [26] algorithms take advantage of V-shape functions for various applications. Recently, a new type of transfer function is introduced, the form of which varies by time.…”

Section: Introductionmentioning

confidence: 99%

“…In the article [25], a time-varying mirrored S-shaped transfer function is used for BPSO algorithms to solve feature selection problems. The binary version of the binary whale optimization algorithm (BWOA) algorithm is also provided based on a time-varying transfer function to solve feature selection problems [26]. In this paper, a new time-varying V-shape transfer function is employed to convert the continuous space to the binary space in the CSO algorithm.…”

Section: Introductionmentioning

confidence: 99%

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A self-adaptive binary cat swarm optimization using new time-varying transfer function for gene selection in DNA microarray expression cancer data

2023

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Microarray technology is beneficial in terms of diagnosing various diseases, including cancer. Despite all DNA microarray benefits, the high number of genes versus the low number of samples has always been a crucial challenge for this technology. Accordingly, we need new optimization algorithms to select optimal genes for faster disease diagnosis. In this article, a new version of the binary cat optimization algorithm, named SBCSO, for gene selection in DNA microarray expression cancer data is presented. The main contributions in this paper are listed as follows: First, the opposition-based learning (OBL) mechanism is employed to improve the proposed algorithm's population members' diversity. Second, a time-varying V-shape transfer function is employed to balance the two phases of exploration and extraction in the proposed algorithm. Third, the MR and 𝛌 parameters in the proposed algorithm are adapted over time, and finally, single-objective and multi-objective approaches are proposed to solve the gene selection problems. The fifteen datasets pertinent to microarray data of various cancer types are employed to compare the proposed method with other well-known binary optimization algorithms. The experiments' results indicate that the proposed algorithm has a better capability to select the optimal genes for a faster disease diagnosis.

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Section: Resultsmentioning

confidence: 99%

“…Binary dragonfly algorithm (BDA) [26] beta 1.5 Inertia weight (w) 0.9 to 0.2 Binary bat algorithm (BBA) [25], binary multi-objective bat optimization algorithm (BMOBBA) [47] α 0.9 γ 0.9…”

Section: Mutation Rate 1/dimensionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A self-adaptive binary cat swarm optimization using new time-varying transfer function for gene selection in DNA microarray expression cancer data

2023

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“…gradient methods, least square, Newton method, etc. (Chu and Tsai, 2007). These methods required expert knowledge and powerful intuition into the array's physics to synthesize it.…”

Section: Introductionmentioning

confidence: 99%

Thinned array antenna synthesis using modified binary particle swarm optimization with minimization of sidelobes

Allu

Kumar²,

Prasad

2022

IJICC

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PurposeThe purpose of this paper is to propose radiating system by avoiding electromagnetic interference in unwanted directions and to radiate the energy in the required direction with an optimization technique.Design/methodology/approachPractically, multiple, incompatible variables require concurrent boost on a synthesis of systematic antenna assemblage. The authors have worked out the main statistic penalty function to ensure all the restrictions. Here, MBPSO (Modified Binary Particle Swarm Optimization) is developed and introduced thin planar synthesis restriction. The sigmoid function is used to update the particle position. Different analytical demonstrations have been carried out, and the exhibited methods are predominant than the algorithms.FindingsA 20 × 10 planar antenna array is synthesized using modified BPSO. The authors have suppressed the PSLL in two principal planes and as well as in the entire f plane. Numerical results state that MBPSO outperforms the other binary BPSO, BCSO, ACO, RGA, GA optimization techniques. MBPSO achieved a −51.84 dB PSLL level, whereas BPSO achieved −48.57 dB with the same 50% thinning.Originality/valuePlanar array antenna formation is one of the most complex syntheses because the array gets filled with more antenna elements. The machine-like complication and implementation of such an antenna arrangement with a broad opening would be expensive. It is not easy to control the required radiation patterns shape by using a uniform feeding network. To get better flexibility for sustaining the sidelobe levelheaded along with consistent amplitude distribution. So as far as prominence has been given to the evolutionary algorithm, find an ideal solution for objective array combinational problems.

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Explosion gravitation field algorithm with dust sampling for unconstrained optimization

Huang

Wang

et al. 2019

Applied Soft Computing

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A New Classification Method for Computational Intelligence

Cited by 5 publications

References 6 publications

A self-adaptive binary cat swarm optimization using new time-varying transfer function for gene selection in DNA microarray expression cancer data

A self-adaptive binary cat swarm optimization using new time-varying transfer function for gene selection in DNA microarray expression cancer data

Thinned array antenna synthesis using modified binary particle swarm optimization with minimization of sidelobes

Explosion gravitation field algorithm with dust sampling for unconstrained optimization

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