Fuzzy adaptive catfish particle swarm optimization

Chuang, Li‐Yeh; Tsai, Sheng-Wei; Yang, Cheng‐Hong

doi:10.5430/air.v1n2p149

Cited by 7 publications

(6 citation statements)

References 15 publications

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“…It can be seen from the numerical result of Table 2 that the CCCS algorithm has a superior search performance to the If (c≥limit) (09) from small to large in accordance with ; (10) Delete the solution of the end 10% (11) Generate m catfish new nest Placed in the tail of , = × 10%; (12) Output the catfish new solution ; (13) End (14) Select candidate solution ; (15) If ( > ) (16) The new solution is used instead of the candidate solution; (17) Count(present iterations)=1; (18) Else (19) Count(present iterations)=0; (20) End (21) Discarding the worst solution according to the probability ; (22) A new solution is used to replace the discarded solution with a preference random walk; (23) Keep the best solution; (24) End other three algorithms under all the performance index and different functions.…”

Section: Methodsmentioning

confidence: 99%

“…In other words, the catfish nests can guide nests trapped in a local optimum onto a new region of the search space and thus to potentially better nest solutions. In literature [21], the catfish effect is applied to the particle swarm optimization, which improves the algorithm's ability to solve. In literature [16], the catfish effect is fused into the artificial bee colony algorithm to enhance the probability of obtaining the global optimal solution.…”

Section: Updating the Catfish New Nestmentioning

confidence: 99%

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Semisupervised SVM Based on Cuckoo Search Algorithm and Its Application

Xia

Niu

et al. 2018

Mathematical Problems in Engineering

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Semisupervised support vector machine (S3VM) algorithm mainly depends on the predicted accuracy of unlabeled samples, if lots of misclassified unlabeled samples are added to the training will make the training model performance degrade. Thus, the cuckoo search algorithm (CS) is used to optimize the S3VM which also enhances the model performance of S3VM. Considering that the cuckoo search algorithm is limited to the local optimum problem, a new cuckoo search algorithm based on chaotic catfish effect optimization is proposed. First, use the chaotic mechanism with high randomness to initialize the nest for range expansion. Second, chaotic catfish nest is introduced into the effective competition coordination mechanism after falling into the local optimum, so that the candidate's nest can jump out of the local optimal solution and accelerate the convergence ability. In the experiment, results show that the improved cuckoo search algorithm is effective and better than the particle swarm optimization (PSO) algorithm and the cuckoo search algorithm on the benchmark functions. In the end, the improved cuckoo search algorithm is used to optimize semisupervised SVM which is applied into oil layer recognition. Results show that this optimization model is superior to the semisupervised SVM in terms of recognition rate and time.

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Section: Methodsmentioning

confidence: 99%

Section: Updating the Catfish New Nestmentioning

confidence: 99%

Semisupervised SVM Based on Cuckoo Search Algorithm and Its Application

Xia

Niu

et al. 2018

Mathematical Problems in Engineering

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“…The catfish disturb the living environment of the sardines to activate their survival ability. The catfish effect is derived from this phenomenon and has been successfully incorporated into PSO [25,26].…”

Section: Catfish Effect Mechanismmentioning

confidence: 99%

“…However, although OL strategy can improve the optimizing efficiency, the algorithm still suffers from the loss of diversity. To overcome this problem, the catfish effect mechanism [25,26] is introduced to enhance the population diversity by replacing the worst individuals with their own opposition information. Specifically, as the iterations progress, the worst individuals are coming closer and closer to the best individual, and once the algorithm traps into the local convergence, catfish effect mechanism can drive the worst subpopulation to explore the new region.…”

Section: Introductionmentioning

confidence: 99%

Biological Flower Pollination Algorithm with Orthogonal Learning Strategy and Catfish Effect Mechanism for Global Optimization Problems

Cui

2018

Mathematical Problems in Engineering

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The flower pollination algorithm (FPA) is a novel optimization technique derived from the pollination behavior of flowers. However, the shortcomings of the FPA, such as a tendency towards premature convergence and poor exploitation ability, confine its application in engineering problems. To further strengthen FPA optimization performance, an orthogonal learning (OL) strategy based on orthogonal experiment design (OED) is embedded into the local pollination operator. OED can predict the optimal factor level combination by constructing a smaller but representative test set based on an orthogonal array. Using this characteristic of OED, the OL strategy can extract a promising solution from various sources of experience information, which leads the population to a potentially reasonable search direction. Moreover, the catfish effect mechanism is introduced to focus on the worst individuals during the iteration process. This mechanism explores new valuable information and maintains superior population diversity. The experimental results on benchmark functions show that our proposed algorithm significantly enhances the performance of the basic FPA and offers stronger competitiveness than several state-of-the-art algorithms.

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“…Equation (10) is used to randomly perturb the globalbest particle in the suboptimal region to avoid convergence. The performance of the PPSO algorithm is further improved using catfish particles [46] into the swarm [14]. The particles are arranged in descending order of their fitness values after few generations (catIter).…”

Section: Variant Of Pso Algorithmmentioning

confidence: 99%

Selection and Configuration of Sorption Isotherm Models in Soils Using Artificial Bees Guided by the Particle Swarm

Bharat

2017

Advances in Artificial Intelligence

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A precise estimation of isotherm model parameters and selection of isotherms from the measured data are essential for the fate and transport of toxic contaminants in the environment. Nonlinear least-square techniques are widely used for fitting the isotherm model on the experimental data. However, such conventional techniques pose several limitations in the parameter estimation and the choice of appropriate isotherm model as shown in this paper. It is demonstrated in the present work that the classical deterministic techniques are sensitive to the initial guess and thus the performance is impeded by the presence of local optima. A novel solver based on modified artificial bee-colony (MABC) algorithm is proposed in this work for the selection and configuration of appropriate sorption isotherms. The performance of the proposed solver is compared with the other three solvers based on swarm intelligence for model parameter estimation using measured data from 21 soils. Performance comparison of developed solvers on the measured data reveals that the proposed solver demonstrates excellent convergence capabilities due to the superior explorationexploitation abilities. The estimated solutions by the proposed solver are almost identical to the mean fitness values obtained over 20 independent runs. The advantages of the proposed solver are presented.

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Fuzzy adaptive catfish particle swarm optimization

Cited by 7 publications

References 15 publications

Semisupervised SVM Based on Cuckoo Search Algorithm and Its Application

Semisupervised SVM Based on Cuckoo Search Algorithm and Its Application

Biological Flower Pollination Algorithm with Orthogonal Learning Strategy and Catfish Effect Mechanism for Global Optimization Problems

Selection and Configuration of Sorption Isotherm Models in Soils Using Artificial Bees Guided by the Particle Swarm

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