A Novel Double-Strand DNA Genetic Algorithm for Multi-Objective Optimization

Zang, Wenke; Zhang, Weining; Wang, Zehua; Jiang, Dong; Liu, Xiyu; Sun, Minghe

doi:10.1109/access.2019.2894726

Cited by 5 publications

(3 citation statements)

References 36 publications

(53 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…GAs work through its fundamental operators of selection, crossover, and mutation for reproduction of the new population of candidate solution at each step increment in generations. The generic workflow of GAs operations is illustrated in Figure 3, while further necessary details of processing blocks can be seen in [38,39]. Many constrained and unconstrained nonlinear optimization problems are effectively addressed with competency of GAs such as optimization in filter designing [40], life prediction of supercapacitors [41], salesman problem [42], multiaccess edge computing [43], and multiobjective optimization [44].…”

Section: Design Methodologymentioning

confidence: 99%

Evolutionary Heuristic Computing Paradigm for 2D-DOA Estimation along Circular Array

Shah

Raja

Al‐Turjman³

et al. 2022

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

Direction of arrival (DOA) estimation problem has growing interest for the researcher investigating in system identification models arising in the field of digital signal processing, mobile communication, controls, and beamforming. In the presented work, evolutionary heuristic computing paradigm is presented for 2D-DOA estimation of plane waves impinging on uniform circular array. Performance metric of mean squared error is utilized as construction of a fitness function for the system, and the optimization strength of three methodologies, genetic algorithms (GAs), Pattern Search (PS), and integration of GAs with PS (GA-PS) is exploited for 2D-DOA estimation based on elevation as well as azimuth angles. Consistent precision, convergence, stability, and robustness of integrated heuristics of GA-PS are endorsed through outcomes of statistical observations.

show abstract

Section: Design Methodologymentioning

confidence: 99%

Evolutionary Heuristic Computing Paradigm for 2D-DOA Estimation along Circular Array

Shah

Raja

Al‐Turjman³

et al. 2022

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

show abstract

“…The performance of the multi-objective clustering algorithm depends critically on the clustering objectives. In this paper, we choose the FCM objective function J FCM [11] and fuzzy separation S FCM [24] as the two clustering objectives. The definition of J FCM is given in Eq.(1).…”

Section: Objective Functions Of Multi-objective Clusteringmentioning

confidence: 99%

“…Each antibody s i in S needs to be reproduced with q i times. First, the variant crowding distance (VCD) [24] of each active antibody is calculated. Then, the active antibody with higher VCD has a larger q i for enhancing local search around the active antibody.…”

Section: B Grid-based Selection Of Active Antibodiesmentioning

confidence: 99%

A Kernel-Based Intuitionistic Fuzzy C-Means Clustering Using Improved Multi-Objective Immune Algorithm

et al. 2019

Self Cite

View full text Add to dashboard Cite

Clustering algorithms have attracted a lot of attentions recently in real-world applications. However, the traditional clustering algorithms still have plenty of defects which are not yet resolved. In this paper, a kernel-based intuitionistic fuzzy C-means clustering using improved multi-objective artificial immune algorithm (KIFCM-IMOIA) is proposed. In our algorithm, the kernel trick and the intuitionistic fuzzy entropy (IFE) are introduced into the objective functions, which improves the robustness to noises. In addition, an improved multi-objective optimization immune algorithm (IMOIA), which simultaneously optimizes the intra-cluster compactness and inter-cluster separation, is proposed to prevent the algorithm from falling into local optimum. The proposed IMOIA uses a novel active antibody selection strategy, a hybrid differential evolution strategy, and an adaptive mutation operator to maintain better distribution of the solutions with better convergence. Finally, we performed experiments using 14 UCI datasets and compared our algorithm with six clustering methods on three performance metrics. The experimental results show that our algorithm performs better than other algorithms. INDEX TERMS Intuitionistic fuzzy C-means, kernel function, artificial immune algorithm, multi-objective optimization.

show abstract