scholarly journals Krill herd algorithm with chaotic time interval and elitism scheme

2019 ◽  
Vol 7 (2) ◽  
pp. 71-84 ◽  
Author(s):  
Shuxia Li ◽  
Yuzhe Tian
Keyword(s):  
Time Interval ◽  
Krill Herd Algorithm ◽  
Krill Herd ◽  
Elitism Scheme

A Chaotic Krill Herd Algorithm for Optimal Solution of the Economic Dispatch Problem

2017 ◽  
Vol 31 ◽  
pp. 155-168 ◽  
Author(s):  
Bachir Bentouati ◽  
Saliha Chettih ◽  
Ragab Abdel-Aziz El-Sehiemy
Keyword(s):  
Optimization Problem ◽  
Economic Dispatch ◽  
Optimal Solution ◽  
Operating Conditions ◽  
Krill Herd Algorithm ◽  
Krill Herd ◽  
Conflicting Objectives ◽  
Metaheuristic Search ◽  
Elitism Scheme ◽  
Planning And Operation

The aim of economic dispatch (ED) problem is to provide an efficient utilization of energy resources to produce economic and secure operating conditions for the planning and operation of a power system. ED is formed as a nonlinear optimization problem with conflicting objectives and subjected to both inequality and equality constraints. An efficient improvement of krill herd (KH) algorithm, a powerful metaheuristic method, has been introduced in this paper. The KH algorithm inspired by the Lagrangian and evolutionary behaviour of the krill people in nature, has been investigated to solve ED problem on 6, 13, 20 and 40 generating units. The proposed chaotic krill herd (CKH)) improvement is done by incorporating the chaos approach to KH algorithm for raising the global convergence speed and for enhancing its performance. The elitism scheme serves to save the best krill during the procedure when updating the krill. The results show clearly the superiority of CKH in searching for the best cost value results when compared with well-known metaheuristic search algorithms.

scholarly journals Simulated Annealing-Based Krill Herd Algorithm for Global Optimization

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Gai-Ge Wang ◽  
Lihong Guo ◽  
Amir Hossein Gandomi ◽  
Amir Hossein Alavi ◽  
Hong Duan
Keyword(s):  
Global Optimization ◽  
Simulated Annealing ◽  
Optimization Problems ◽  
Optimization Methods ◽  
Greedy Strategy ◽  
Krill Herd Algorithm ◽  
Krill Herd ◽  
Low Probability ◽  
Elitism Scheme ◽  
Reliability And Robustness

Recently, Gandomi and Alavi proposed a novel swarm intelligent method, called krill herd (KH), for global optimization. To enhance the performance of the KH method, in this paper, a new improved meta-heuristic simulated annealing-based krill herd (SKH) method is proposed for optimization tasks. A new krill selecting (KS) operator is used to refine krill behavior when updating krill’s position so as to enhance its reliability and robustness dealing with optimization problems. The introduced KS operator involves greedy strategy and accepting few not-so-good solutions with a low probability originally used in simulated annealing (SA). In addition, a kind of elitism scheme is used to save the best individuals in the population in the process of the krill updating. The merits of these improvements are verified by fourteen standard benchmarking functions and experimental results show that, in most cases, the performance of this improved meta-heuristic SKH method is superior to, or at least highly competitive with, the standard KH and other optimization methods.

scholarly journals Lévy-Flight Krill Herd Algorithm

2013 ◽  
Vol 2013 ◽  
pp. 1-14 ◽  
Author(s):  
Gaige Wang ◽  
Lihong Guo ◽  
Amir Hossein Gandomi ◽  
Lihua Cao ◽  
Amir Hossein Alavi ◽  
...  
Keyword(s):  
Optimization Problems ◽  
Computing Time ◽  
Optimization Methods ◽  
Population Based ◽  
Global Optimum ◽  
Lévy Flight ◽  
Levy Flight ◽  
Krill Herd Algorithm ◽  
Krill Herd ◽  
Elitism Scheme

To improve the performance of the krill herd (KH) algorithm, in this paper, a Lévy-flight krill herd (LKH) algorithm is proposed for solving optimization tasks within limited computing time. The improvement includes the addition of a new local Lévy-flight (LLF) operator during the process when updating krill in order to improve its efficiency and reliability coping with global numerical optimization problems. The LLF operator encourages the exploitation and makes the krill individuals search the space carefully at the end of the search. The elitism scheme is also applied to keep the best krill during the process when updating the krill. Fourteen standard benchmark functions are used to verify the effects of these improvements and it is illustrated that, in most cases, the performance of this novel metaheuristic LKH method is superior to, or at least highly competitive with, the standard KH and other population-based optimization methods. Especially, this new method can accelerate the global convergence speed to the true global optimum while preserving the main feature of the basic KH.

scholarly journals Hybrid Krill Herd-ANN Model for Prediction Strength and Stiffness of Bolted Connections

Buildings ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 229
Author(s):  
Iman Faridmehr ◽  
Mehdi Nikoo ◽  
Mohammad Hajmohammadian Baghban ◽  
Raffaele Pucinotti
Keyword(s):  
Correlation Coefficient ◽  
Predictive Accuracy ◽  
Supply And Demand ◽  
Steel Structures ◽  
Ann Model ◽  
Krill Herd Algorithm ◽  
Seat Angle ◽  
Taylor Diagram ◽  
Krill Herd ◽  
Strength And Stiffness

The behavior of beam-to-column connections significantly influences the stability, strength, and stiffness of steel structures. This is particularly important in extreme non-elastic responses, i.e., earthquakes, and sudden column removal, as the fluctuation in strength and stiffness affects both supply and demand. Accordingly, it is essential to accurately estimate the strength and stiffness of connections in the analysis of and design procedures for steel structures. Beginning with the state-of-the-art, the capacity of three available component-based mechanical models to estimate the complex mechanical properties of top- and seat-angle connections with double-web angles (TSACWs), with variable parameters, were investigated. Subsequently, a novel hybrid krill herd algorithm-artificial neural network (KHA-ANN) model was proposed to acquire an informational model from the available experimental dataset. Using several statistical metrics, including the corresponding coefficient of variation (CoV), correlation coefficient (R), and the correlation coefficient provided by the Taylor diagram, this study revealed that the krill herd-ANN model achieved the most reliable predictive accuracy for the strength and stiffness of top- and seat-angle connections with double web angles.

scholarly journals HKF-SVR Optimized by Krill Herd Algorithm for Coaxial Bearings Performance Degradation Prediction

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 660 ◽  
Author(s):  
Fang Liu ◽  
Liubin Li ◽  
Yongbin Liu ◽  
Zheng Cao ◽  
Hui Yang ◽  
...  
Keyword(s):  
Kernel Method ◽  
Back Propagation ◽  
Vibration Signal ◽  
Back Propagation Neural Network ◽  
Industrial Applications ◽  
Performance Degradation ◽  
Support Vector ◽  
Krill Herd Algorithm ◽  
Krill Herd ◽  
Learning Machine

In real industrial applications, bearings in pairs or even more are often mounted on the same shaft. So the collected vibration signal is actually a mixed signal from multiple bearings. In this study, a method based on Hybrid Kernel Function-Support Vector Regression (HKF–SVR) whose parameters are optimized by Krill Herd (KH) algorithm was introduced for bearing performance degradation prediction in this situation. First, multi-domain statistical features are extracted from the bearing vibration signals and then fused into sensitive features using Kernel Joint Approximate Diagonalization of Eigen-matrices (KJADE) algorithm which is developed recently by our group. Due to the nonlinear mapping capability of the kernel method and the blind source separation ability of the JADE algorithm, the KJADE could extract latent source features that accurately reflecting the performance degradation from the mixed vibration signal. Then, the between-class and within-class scatters (SS) of the health-stage data sample and the current monitored data sample is calculated as the performance degradation index. Second, the parameters of the HKF–SVR are optimized by the KH (Krill Herd) algorithm to obtain the optimal performance degradation prediction model. Finally, the performance degradation trend of the bearing is predicted using the optimized HKF–SVR. Compared with the traditional methods of Back Propagation Neural Network (BPNN), Extreme Learning Machine (ELM) and traditional SVR, the results show that the proposed method has a better performance. The proposed method has a good application prospect in life prediction of coaxial bearings.

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