Model turbine heat rate by fast learning network with tuning based on ameliorated krill herd algorithm

Breast cancer diagnosis using the fast learning network algorithm

The use of machine learning (ML) and data mining algorithms in the diagnosis of breast cancer (BC) has recently received a lot of attention. The majority of these efforts, however, still require improvement since either they were not statistically evaluated or they were evaluated using insufficient assessment metrics, or both. One of the most recent and effective ML algorithms, fast learning network (FLN), may be seen as a reputable and efficient approach for classifying data; however, it has not been applied to the problem of BC diagnosis. Therefore, this study proposes the FLN algorithm in order to improve the accuracy of the BC diagnosis. The FLN algorithm has the capability to a) eliminate overfitting, b) solve the issues of both binary and multiclass classification, and c) perform like a kernel-based support vector machine with a structure of the neural network. In this study, two BC databases (Wisconsin Breast Cancer Database (WBCD) and Wisconsin Diagnostic Breast Cancer (WDBC)) were used to assess the performance of the FLN algorithm. The results of the experiment demonstrated the great performance of the suggested FLN method, which achieved an average of accuracy 98.37%, precision 95.94%, recall 99.40%, F-measure 97.64%, G-mean 97.65%, MCC 96.44%, and specificity 97.85% using the WBCD, as well as achieved an average of accuracy 96.88%, precision 94.84%, recall 96.81%, F-measure 95.80%, G-mean 95.81%, MCC 93.35%, and specificity 96.96% using the WDBC database. This suggests that the FLN algorithm is a reliable classifier for diagnosing BC and may be useful for resolving other application-related problems in the healthcare sector.

Hybrid Annealing Krill Herd and Quantum-Behaved Particle Swarm Optimization

The particle swarm optimization algorithm (PSO) is not good at dealing with discrete optimization problems, and for the krill herd algorithm (KH), the ability of local search is relatively poor. In this paper, we optimized PSO by quantum behavior and optimized KH by simulated annealing, so a new hybrid algorithm, named the annealing krill quantum particle swarm optimization (AKQPSO) algorithm, is proposed, and is based on the annealing krill herd algorithm (AKH) and quantum particle swarm optimization algorithm (QPSO). QPSO has better performance in exploitation and AKH has better performance in exploration, so AKQPSO proposed on this basis increases the diversity of population individuals, and shows better performance in both exploitation and exploration. In addition, the quantum behavior increased the diversity of the population, and the simulated annealing strategy made the algorithm avoid falling into the local optimal value, which made the algorithm obtain better performance. The test set used in this paper is a classic 100-Digit Challenge problem, which was proposed at 2019 IEEE Congress on Evolutionary Computation (CEC 2019), and AKQPSO has achieved better performance on benchmark problems.

A Hybrid Multi-Step Probability Selection Particle Swarm Optimization with Dynamic Chaotic Inertial Weight and Acceleration Coefficients for Numerical Function Optimization

As a meta-heuristic algoriTthm, particle swarm optimization (PSO) has the advantages of having a simple principle, few required parameters, easy realization and strong adaptability. However, it is easy to fall into a local optimum in the early stage of iteration. Aiming at this shortcoming, this paper presents a hybrid multi-step probability selection particle swarm optimization with sine chaotic inertial weight and symmetric tangent chaotic acceleration coefficients (MPSPSO-ST), which can strengthen the overall performance of PSO to a large extent. Firstly, we propose a hybrid multi-step probability selection update mechanism (MPSPSO), which skillfully uses a multi-step process and roulette wheel selection to improve the performance. In order to achieve a good balance between global search capability and local search capability to further enhance the performance of the method, we also design sine chaotic inertial weight and symmetric tangent chaotic acceleration coefficients inspired by chaos mechanism and trigonometric functions, which are integrated into the MPSPSO-ST algorithm. This strategy enables the diversity of the swarm to be preserved to discourage premature convergence. To evaluate the effectiveness of the MPSPSO-ST algorithm, we conducted extensive experiments with 20 classic benchmark functions. The experimental results show that the MPSPSO-ST algorithm has faster convergence speed, higher optimization accuracy and better robustness, which is competitive in solving numerical optimization problems and outperforms a lot of classical PSO variants and well-known optimization algorithms.

Improved Chaotic Particle Swarm Optimization Algorithm with More Symmetric Distribution for Numerical Function Optimization

As a global-optimized and naturally inspired algorithm, particle swarm optimization (PSO) is characterized by its high quality and easy application in practical optimization problems. However, PSO has some obvious drawbacks, such as early convergence and slow convergence speed. Therefore, we introduced some appropriate improvements to PSO and proposed a novel chaotic PSO variant with arctangent acceleration coefficient (CPSO-AT). A total of 10 numerical optimization functions were employed to test the performance of the proposed CPSO-AT algorithm. Extensive contrast experiments were conducted to verify the effectiveness of the proposed methodology. The experimental results showed that the proposed CPSO-AT algorithm converges quickly and has better stability in numerical optimization problems compared with other PSO variants and other kinds of well-known optimal algorithms.

Enhanced Chaotic Grey Wolf Optimizer for Real-World Optimization Problems

The gray wolf optimizer (GWO) is a new population-based optimizer that is inspired by the hunting procedure and leadership hierarchy in gray wolves. In this chapter, a new enhanced gray wolf optimizer (EGWO) is proposed for tackling several real-world optimization problems. In the EGWO algorithm, a new chaotic operation is embedded in GWO which helps search agents to chaotically move toward a randomly selected wolf. By this operator, the EGWO algorithm is capable of switching between chaotic and random exploration. In order to substantiate the efficiency of EGWO, 22 test cases from IEEE CEC 2011 on real-world problems are chosen. The performance of EGWO is compared with six standard optimizers. A statistical test, known as Wilcoxon rank-sum, is also conducted to prove the significance of the explored results. Moreover, the obtained results compared with those of six advanced algorithms from CEC 2011. The evaluations reveal that the proposed EGWO can obtain superior results compared to the well-known algorithms and its results are better than some advanced variants of optimizers.