Opposition-Based Hybrid Strategy for Particle Swarm Optimization in Noisy Environments

The solution of high dimensional function has always been a hot topic. In this paper, a novel algorithm based on Kernel Fuzzy C-means and dolphin swarm algorithm are proposed to solve highdimensional functions more accurately. First, to improve the global convergence ability of dolphin swarm algorithm, Kernel Fuzzy C-means is introduced into the algorithm, named as Kernel Fuzzy C-means dolphin swarm algorithm (KFCDSA); Second, the five typical high-dimensional functions are applied to test the performance of the combination of KFCDSA. Finally, some indicators are used to evaluate the performance of different meta-heuristic algorithms. The results show that: the performance of the proposed algorithm exceeds that of the dolphin swarm algorithm and some advanced metaheuristic algorithms considered for comparison based on five different evaluating indicators. Through the test results, it can be concluded that introducing Kernel Fuzzy C-means into dolphin swarm algorithm is an effective improvement and provides a possibility for obtaining global optimal solutions for high-dimensional functions. INDEX TERMS Dolphin swarm algorithm, Kernel fuzzy C-means, the hybrid algorithm, high-dimensional function, solve.

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“…The development of some meta-heuristic algorithms is inspired by the particle swarm algorithm [30]- [31]. DSA [11] is no exception.…”

Section: Dolphin Swarm Algorithm a Hunting Process Of Dolphin Swarmmentioning

confidence: 99%

“…Step2: The membership matrix is updated by EQUA-TION (31), and the partition matrix U (t) is obtained.…”

Section: The Hybrid Algorithm Based On Kfc and Dsamentioning

confidence: 99%

An Improved Dolphin Swarm Algorithm Based on Kernel Fuzzy C-Means in the Application of Solving the Optimal Problems of Large-Scale Function

Qiao

Yang

2020

IEEE Access

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“…NSM based initialization tends to increase the convergence speed of particles over simple initialization. An oppositionbased hybrid initialization strategy is proposed by Kang et al [39]. Their reconnected approach becomes highly effective in the noisy environment on the many objective optimization problems Centroidal Voronoi Tessellations (CVT) generator was suggested by Mark and Ventura [40] to generate numbers at the locations identified by the CVT process.…”

Section: Related Workmentioning

confidence: 99%

Particle Swarm Optimization With Probability Sequence for Global Optimization

et al. 2020

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Particle Swarm Optimization (PSO) has been frequently employed to solve diversified optimization problems. Choosing initial placement for population plays an important role in meta-heuristic methods since they can significantly converge. In this study, probability distribution has been introduced to enhance the diversity of swarm and convergence speed. Population initialization method based on uniform distribution is normally used when there is no preceding knowledge available regarding the candidate solution. In this paper, a new approach to initialize population is proposed using probability sequence Weibull marked as (WI-PSO) that applies the probability distribution to generate numbers at random locations for swarm initialization. The proposed method (WI-PSO) is tested on sixteen well-known unimodal and multi-modal benchmark test functions broadly adopted by the research community and its encouraging performance is investigated and compared with the Exponential distribution based PSO (E-PSO), Beta distribution based PSO (BT-PSO), Gamma distribution based PSO (GA-PSO) and Log-normal distribution based PSO (LN-PSO). Artificial Neural Networks (ANNs) have become the most powerful tool for classification of complex benchmark problems. We have experimented the proposed method (WI-PSO) for weight optimization of a feed-forward neural network to ensure its purity and have compared with conventional back-propagation algorithm (BPA), E-PSO, BT-PSO, GA-PSO and LN-PSO. Due to flexible behaviour in the degree of freedom, the experimental results infer the perfection and dominance of the Weibull based population initialization. The result exhibits the anticipation of influence exerted by the proposed technique on all sixteen objective functions and eight real-world benchmark data sets.

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“…algorithm is another intelligence algorithm aiming to improving the performance of the WNN [28,29]. Basically, PSO imitates bird flocking for optimizing continuous nonlinear functions [30].…”

Section: Particle Swarm Optimization-based Wavelet Neural Network (Psmentioning

confidence: 99%

Wavelet Neural Network for Modeling Chlorophyll a Concentration Affected by Artificial Upwelling

Huang

Zheng

Wang

et al. 2019

Mathematical Problems in Engineering

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Through bringing nutrient-rich subsurface water to the surface, the artificial upwelling technology is applied to increase the primary marine productivity which could be assessed by Chlorophyll a concentration. Chlorophyll a concentration may vary with different water physical properties. Therefore, it is necessary to study the relationship between Chlorophyll a concentration and other water physical parameters. To ensure the accuracy of predicting the concentration of Chlorophyll a, we develop several models based on wavelet neural network (WNN). In this study, we build up a three-layer basic wavelet neural network followed by three improved wavelet neural networks, which are namely genetic algorithm-based wavelet neural network (GA-WNN), particle swarm optimization-based wavelet neural network (PSO-WNN), and genetic algorithm & particle swarm optimization-based wavelet neural network (GAPSO-WNN). The experimental data were collected from Qiandao Lake, China. The performances of the proposed models are compared based on four evaluation parameters, i.e., R-square, root mean square error (RMSE), mean of error (ME), and distance (D). The modeling results show that the wavelet neural network can achieve a certain extent of accuracy in modeling the relationships between Chlorophyll a concentration and the five input parameters (salinity, depth, temperature, pH, and dissolved oxygen).

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Opposition-Based Hybrid Strategy for Particle Swarm Optimization in Noisy Environments

Cited by 52 publications

References 43 publications

An Improved Dolphin Swarm Algorithm Based on Kernel Fuzzy C-Means in the Application of Solving the Optimal Problems of Large-Scale Function

An Improved Dolphin Swarm Algorithm Based on Kernel Fuzzy C-Means in the Application of Solving the Optimal Problems of Large-Scale Function

Particle Swarm Optimization With Probability Sequence for Global Optimization

Wavelet Neural Network for Modeling Chlorophyll a Concentration Affected by Artificial Upwelling

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