A Fractional-Order Chaotic Sparrow Search Algorithm for Enhancement of Long Distance Iris Image

At present, iris recognition has been widely used as a biometrics-based security enhancement technology. However, in some application scenarios where a long-distance camera is used, due to the limitations of equipment and environment, the collected iris images cannot achieve the ideal image quality for recognition. To solve this problem, we proposed a modified sparrow search algorithm (SSA) called chaotic pareto sparrow search algorithm (CPSSA) in this paper. First, fractional-order chaos is introduced to enhance the diversity of the population of sparrows. Second, we introduce the Pareto distribution to modify the positions of finders and scroungers in the SSA. These can not only ensure global convergence, but also effectively avoid the local optimum issue. Third, based on the traditional contrast limited adaptive histogram equalization (CLAHE) method, CPSSA is used to find the best clipping limit value to limit the contrast. The standard deviation, edge content, and entropy are introduced into the fitness function to evaluate the enhancement effect of the iris image. The clipping values vary with the pictures, which can produce a better enhancement effect. The simulation results based on the 12 benchmark functions show that the proposed CPSSA is superior to the traditional SSA, particle swarm optimization algorithm (PSO), and artificial bee colony algorithm (ABC). Finally, CPSSA is applied to enhance the long-distance iris images to demonstrate its robustness. Experiment results show that CPSSA is more efficient for practical engineering applications. It can significantly improve the image contrast, enrich the image details, and improve the accuracy of iris recognition.

Download Full-text

K-Means Clustering Algorithm Based on Chaotic Adaptive Artificial Bee Colony

Algorithms ◽

10.3390/a14020053 ◽

2021 ◽

Vol 14 (2) ◽

pp. 53

Author(s):

Qibing Jin ◽

Nan Lin ◽

Yuming Zhang

Keyword(s):

Artificial Bee Colony ◽

Clustering Algorithm ◽

Heuristic Algorithms ◽

Fitness Function ◽

Step Length ◽

Standard Test ◽

Local Optimum ◽

Bee Colony ◽

Clustering Problems ◽

Speed And Accuracy

K-Means Clustering is a popular technique in data analysis and data mining. To remedy the defects of relying on the initialization and converging towards the local minimum in the K-Means Clustering (KMC) algorithm, a chaotic adaptive artificial bee colony algorithm (CAABC) clustering algorithm is presented to optimally partition objects into K clusters in this study. This algorithm adopts the max–min distance product method for initialization. In addition, a new fitness function is adapted to the KMC algorithm. This paper also reports that the iteration abides by the adaptive search strategy, and Fuch chaotic disturbance is added to avoid converging on local optimum. The step length decreases linearly during the iteration. In order to overcome the shortcomings of the classic ABC algorithm, the simulated annealing criterion is introduced to the CAABC. Finally, the confluent algorithm is compared with other stochastic heuristic algorithms on the 20 standard test functions and 11 datasets. The results demonstrate that improvements in CAABA-K-means have an advantage on speed and accuracy of convergence over some conventional algorithms for solving clustering problems.

Download Full-text

A hybrid artificial bee colony algorithm for numerical function optimization

International Journal of Modern Physics C ◽

10.1142/s0129183115501090 ◽

2015 ◽

Vol 26 (10) ◽

pp. 1550109 ◽

Cited By ~ 7

Author(s):

Zakaria N. Alqattan ◽

Rosni Abdullah

Keyword(s):

Artificial Bee Colony ◽

Search Algorithm ◽

Function Optimization ◽

Search Process ◽

Local Optimum ◽

Particle Movement ◽

Numerical Function ◽

Abc Algorithm ◽

Bee Colony ◽

Optimization Search

Artificial Bee Colony (ABC) algorithm is one of the swarm intelligence algorithms; it has been introduced by Karaboga in 2005. It is a meta-heuristic optimization search algorithm inspired from the intelligent foraging behavior of the honey bees in nature. Its unique search process made it as one of the most competitive algorithm with some other search algorithms in the area of optimization, such as Genetic algorithm (GA) and Particle Swarm Optimization (PSO). However, the ABC performance of the local search process and the bee movement or the solution improvement equation still has some weaknesses. The ABC is good in avoiding trapping at the local optimum but it spends its time searching around unpromising random selected solutions. Inspired by the PSO, we propose a Hybrid Particle-movement ABC algorithm called HPABC, which adapts the particle movement process to improve the exploration of the original ABC algorithm. Numerical benchmark functions were used in order to experimentally test the HPABC algorithm. The results illustrate that the HPABC algorithm can outperform the ABC algorithm in most of the experiments (75% better in accuracy and over 3 times faster).

Download Full-text

Phase-controlled metasurface design via optimized genetic algorithm

Nanophotonics ◽

10.1515/nanoph-2020-0132 ◽

2020 ◽

Vol 9 (12) ◽

pp. 3931-3939 ◽

Cited By ~ 1

Author(s):

Yulong Fan ◽

Yunkun Xu ◽

Meng Qiu ◽

Wei Jin ◽

Lei Zhang ◽

...

Keyword(s):

Genetic Algorithm ◽

High Speed ◽

Fitness Function ◽

Low Noise ◽

Local Optimum ◽

Large Area ◽

Long Distance ◽

Random Mutation ◽

Light Sheet Microscopy ◽

Optical Modes

AbstractIn an optical Pancharatnam-Berry (PB) phase metasurface, each sub-wavelength dielectric structure of varied spatial orientation can be treated as a point source with the same amplitude yet varied relative phase. In this work, we introduce an optimized genetic algorithm (GA) method for the synthesis of one-dimensional (1D) PB phase-controlled dielectric metasurfaces by seeking for optimized phase profile solutions, which differs from previously reported amplitude-controlled GA method only applicable to generate transverse optical modes with plasmonic metasurfaces. The GA–optimized phase profiles can be readily used to construct dielectric metasurfaces with improved functionalities. The loop of phase-controlled GA consists of initialization, random mutation, screened evolution, and duplication. Here random mutation is realized by changing the phase of each unit cell, and this process should be efficient to obtain enough mutations to drive the whole GA process under supervision of appropriate mutation boundary. A well-chosen fitness function ensures the right direction of screened evolution, and the duplication process guarantees an equilibrated number of generated light patterns. Importantly, we optimize the GA loop by introducing a multi-step hierarchical mutation process to break local optimum limits. We demonstrate the validity of our optimized GA method by generating longitudinal optical modes (i. e., non-diffractive light sheets) with 1D PB phase dielectric metasurfaces having non-analytical counter-intuitive phase profiles. The produced large-area, long-distance light sheets could be used for realizing high-speed, low-noise light-sheet microscopy. Additionally, a simplified 3D light pattern generated by a 2D PB phase metasurface further reveals the potential of our optimized GA method for manipulating truly 3D light fields.

Download Full-text

Fractional Order Fuzzy PID Controller for Automatic Generation Control of Power Systems

ECTI Transactions on Electrical Engineering, Electronics, and Communications ◽

10.37936/ecti-eec.2021191.222284 ◽

2021 ◽

Vol 19 (1) ◽

pp. 71

Author(s):

Dillip Kumar Sahoo ◽

Rabindra Kumar Sahu ◽

Sidharth Panda

Keyword(s):

Power Systems ◽

Fractional Order ◽

Optimization Algorithm ◽

Search Algorithm ◽

Thermal Power ◽

Automatic Generation ◽

Test System ◽

Pattern Search ◽

Fuzzy Pid ◽

Bee Colony

In this study, a Hybrid Adaptive Differential Evolution and Pattern Search (hADE-PS) tuned Fractional Order Fuzzy PID (FOFPID) structure is suggested for AGC of power systems. At first, a non-reheat type two-area thermal system is considered and the improvement of the proposed approach over Bacteria Foraging Optimization Algorithm (BFOA), Teaching Learning Based Optimization (TLBO), Jaya Algorithm (JA), Genetic Algorithm (GA) and Hybrid BFOA and Particle Swarm Optimization Algorithm (hBFOA-PSO) for the identical test systems has been demonstrated. The analysis was then extended to interconnected thermal power system of reheat type and two-area six-unit system. The results are compared with Firefly Algorithm (FA), Symbiotic Organism Search Algorithm (SOSA) and Artificial Bee colony (ABC) for second test system and TLBO, Hybrid Stochastic Fractal Search and Local Unimodal Sampling (hSFS-LUS), ADE and hADE-PS tuned PID for third test system. Finally, robustness of the suggested controller is examined under varied conditions.

Download Full-text

Fractional-order Learning Algorithm for PID Neural Network Decoupling Control Based on Sparrow Search Algorithm

10.21203/rs.3.rs-1004130/v1 ◽

2021 ◽

Author(s):

Min Wu ◽

Jie Ding ◽

Tingting Yuan ◽

Min Xiao

Keyword(s):

Neural Network ◽

Fractional Order ◽

Learning Algorithm ◽

Search Algorithm ◽

Decoupling Control ◽

Local Optimum ◽

Control Effect ◽

Control Scheme ◽

Tracking Time ◽

System Variables

Abstract Research on control of multi-variable system with strong coupling has been a significant issue in industry. To accurately eliminate the coupling between system variables and improve the control effect, decoupling control techniques are investigated. In this paper, a decoupling control scheme based on fractional-order proportion integration differentiation neural network and sparrow search algorithm (SSA-FPIDNN) is proposed, where sparrow search algorithm is employed to derive the optimal initial weights, preventing the weights from falling into the local optimum, while the fractional-order algorithm is used to correct its connection weights to improve control accuracy. Compared with traditional PIDNN, the proposed SSA-FPIDNN has better decoupling control performance, and the tracking time can be reduced significantly. Numerical simulation and engineering examples verified its effectiveness.

Download Full-text

A Hybrid Monkey Search Algorithm for Clustering Analysis

The Scientific World JOURNAL ◽

10.1155/2014/938239 ◽

2014 ◽

Vol 2014 ◽

pp. 1-16 ◽

Cited By ~ 7

Author(s):

Xin Chen ◽

Yongquan Zhou ◽

Qifang Luo

Keyword(s):

Clustering Analysis ◽

Clustering Algorithm ◽

Search Algorithm ◽

Real Life ◽

Local Optimum ◽

Data Mining Technique ◽

Mining Technique ◽

Bee Colony ◽

Optimum Solution ◽

Monkey Search

Clustering is a popular data analysis and data mining technique. Thek-means clustering algorithm is one of the most commonly used methods. However, it highly depends on the initial solution and is easy to fall into local optimum solution. In view of the disadvantages of thek-means method, this paper proposed a hybrid monkey algorithm based on search operator of artificial bee colony algorithm for clustering analysis and experiment on synthetic and real life datasets to show that the algorithm has a good performance than that of the basic monkey algorithm for clustering analysis.

Download Full-text

Comparison of Meta-heuristic Algorithms on Benchmark Functions

Academic Perspective Procedia ◽

10.33793/acperpro.02.03.41 ◽

2019 ◽

Vol 2 (3) ◽

pp. 508-517

Author(s):

FerdaNur Arıcı ◽

Ersin Kaya

Keyword(s):

Heuristic Algorithms ◽

Optimization Problems ◽

Search Algorithm ◽

Optimization Algorithms ◽

Gravitational Search Algorithm ◽

Differential Evolution Algorithm ◽

Population Based ◽

Time Interval ◽

Bee Colony ◽

Different Characteristics

Optimization is a process to search the most suitable solution for a problem within an acceptable time interval. The algorithms that solve the optimization problems are called as optimization algorithms. In the literature, there are many optimization algorithms with different characteristics. The optimization algorithms can exhibit different behaviors depending on the size, characteristics and complexity of the optimization problem. In this study, six well-known population based optimization algorithms (artificial algae algorithm - AAA, artificial bee colony algorithm - ABC, differential evolution algorithm - DE, genetic algorithm - GA, gravitational search algorithm - GSA and particle swarm optimization - PSO) were used. These six algorithms were performed on the CEC&rsquo;17 test functions. According to the experimental results, the algorithms were compared and performances of the algorithms were evaluated.

Download Full-text

Automatic Mining of Quantitative Association Rules with Gravitational Search Algorithm

International Journal of Software Engineering and Knowledge Engineering ◽

10.1142/s0218194017500127 ◽

2017 ◽

Vol 27 (03) ◽

pp. 343-372 ◽

Cited By ~ 10

Author(s):

Umit Can ◽

Bilal Alatas

Keyword(s):

Association Rules ◽

Optimization Problems ◽

Search Algorithm ◽

Fitness Function ◽

Optimization Algorithms ◽

Gravitational Search Algorithm ◽

Search Space ◽

Search Method ◽

Quantitative Association Rules ◽

Gravitational Search

The classical optimization algorithms are not efficient in solving complex search and optimization problems. Thus, some heuristic optimization algorithms have been proposed. In this paper, exploration of association rules within numerical databases with Gravitational Search Algorithm (GSA) has been firstly performed. GSA has been designed as search method for quantitative association rules from the databases which can be regarded as search space. Furthermore, determining the minimum values of confidence and support for every database which is a hard job has been eliminated by GSA. Apart from this, the fitness function used for GSA is very flexible. According to the interested problem, some parameters can be removed from or added to the fitness function. The range values of the attributes have been automatically adjusted during the time of mining of the rules. That is why there is not any requirements for the pre-processing of the data. Attributes interaction problem has also been eliminated with the designed GSA. GSA has been tested with four real databases and promising results have been obtained. GSA seems an effective search method for complex numerical sequential patterns mining, numerical classification rules mining, and clustering rules mining tasks of data mining.

Download Full-text

A New “Good and Bad Groups-Based Optimizer” for Solving Various Optimization Problems

Applied Sciences ◽

10.3390/app11104382 ◽

2021 ◽

Vol 11 (10) ◽

pp. 4382

Author(s):

Ali Sadeghi ◽

Sajjad Amiri Doumari ◽

Mohammad Dehghani ◽

Zeinab Montazeri ◽

Pavel Trojovský ◽

...

Keyword(s):

Optimization Algorithm ◽

Optimization Problems ◽

Search Algorithm ◽

Fitness Function ◽

Optimization Algorithms ◽

Gravitational Search Algorithm ◽

Gray Wolf ◽

Good Group ◽

Good Ability ◽

Whale Optimization

Optimization is the science that presents a solution among the available solutions considering an optimization problem’s limitations. Optimization algorithms have been introduced as efficient tools for solving optimization problems. These algorithms are designed based on various natural phenomena, behavior, the lifestyle of living beings, physical laws, rules of games, etc. In this paper, a new optimization algorithm called the good and bad groups-based optimizer (GBGBO) is introduced to solve various optimization problems. In GBGBO, population members update under the influence of two groups named the good group and the bad group. The good group consists of a certain number of the population members with better fitness function than other members and the bad group consists of a number of the population members with worse fitness function than other members of the population. GBGBO is mathematically modeled and its performance in solving optimization problems was tested on a set of twenty-three different objective functions. In addition, for further analysis, the results obtained from the proposed algorithm were compared with eight optimization algorithms: genetic algorithm (GA), particle swarm optimization (PSO), gravitational search algorithm (GSA), teaching–learning-based optimization (TLBO), gray wolf optimizer (GWO), and the whale optimization algorithm (WOA), tunicate swarm algorithm (TSA), and marine predators algorithm (MPA). The results show that the proposed GBGBO algorithm has a good ability to solve various optimization problems and is more competitive than other similar algorithms.

Download Full-text