126 research outputs found
Metaheuristics and Chaos Theory
Chaos theory is a novelty approach that has been widely used into various applications. One of the famous applications is the introduction of chaos theory into optimization. Note that chaos theory is highly sensitive to initial condition and has the feature of randomness. As chaos theory has the feature of randomness and dynamical properties, it is easy to accelerate the optimization algorithm convergence and enhance the capability of diversity. In this work, we integrated 10 chaotic maps into several metaheuristic algorithms in order to extensively investigate the effectiveness of chaos theory for improving the search capability. Extensive experiments have been carried out and the results have shown that chaotic optimization can be a very promising tool for solving optimization algorithms
Frequency Control of Microgrid with Renewable Generation using PID Controller based Krill Herd
The main of this paper is to provide optimal control of a state microgrid system. The proposed configuration composes of renewable generation systems such as solar photovoltaic system and wind turbine generator with a Diesel Engine Generator and Fuel-Cell. An Aqua electrolyzer and other energy storage systems such as battery and flywheel are also used in the proposed microgrid. A standard PID (Proportional Integral Derivative) controller scheme is introduced whose its parameters are determined using different optimizations algorithm such as Algorithm Genetic, Particle Swarm Optimization, and Krill Herd algorithm for minimizing frequency and power deviations, in order to enhance the operation of this system. The PID controller gains are optimized by resolving an objective function. The simulation results are shown, and given that the Krill Herd algorithm improves the performance of the system in comparison with GA and PSO based on PID. The efficiency of the system is improved
Current Studies and Applications of Krill Herd and Gravitational Search Algorithms in Healthcare
Nature-Inspired Computing or NIC for short is a relatively young field that
tries to discover fresh methods of computing by researching how natural
phenomena function to find solutions to complicated issues in many contexts. As
a consequence of this, ground-breaking research has been conducted in a variety
of domains, including synthetic immune functions, neural networks, the
intelligence of swarm, as well as computing of evolutionary. In the domains of
biology, physics, engineering, economics, and management, NIC techniques are
used. In real-world classification, optimization, forecasting, and clustering,
as well as engineering and science issues, meta-heuristics algorithms are
successful, efficient, and resilient. There are two active NIC patterns: the
gravitational search algorithm and the Krill herd algorithm. The study on using
the Krill Herd Algorithm (KH) and the Gravitational Search Algorithm (GSA) in
medicine and healthcare is given a worldwide and historical review in this
publication. Comprehensive surveys have been conducted on some other
nature-inspired algorithms, including KH and GSA. The various versions of the
KH and GSA algorithms and their applications in healthcare are thoroughly
reviewed in the present article. Nonetheless, no survey research on KH and GSA
in the healthcare field has been undertaken. As a result, this work conducts a
thorough review of KH and GSA to assist researchers in using them in diverse
domains or hybridizing them with other popular algorithms. It also provides an
in-depth examination of the KH and GSA in terms of application, modification,
and hybridization. It is important to note that the goal of the study is to
offer a viewpoint on GSA with KH, particularly for academics interested in
investigating the capabilities and performance of the algorithm in the
healthcare and medical domains.Comment: 35 page
SCSO: A novel sine-cosine based swarm optimization algorithm for numerical function optimization
Many swarm optimization algorithms have been presented in the literature and these algorithms are generally nature-inspired algorithms. In this paper a novel sine-cosine based particle swarm optimization (SCSO) is presented. In SCSO, firstly particles are generated randomly in the search space. Personal best value and velocity of the particles are calculated and by using step. Calculated velocity is used for updating particles. The proposed algorithm is basic algorithm and approximately 30 rows MATLAB codes are used to implement the proposed algorithm. This short code surprisingly has high optimization capability. In order to evaluate performance and prove success of this algorithm, 14 well known numerical functions was used and the results illustrate that the proposed algorithm is successful in numerical functions optimization
A Hybrid Chimp Optimization Algorithm and Generalized Normal Distribution Algorithm with Opposition-Based Learning Strategy for Solving Data Clustering Problems
This paper is concerned with data clustering to separate clusters based on
the connectivity principle for categorizing similar and dissimilar data into
different groups. Although classical clustering algorithms such as K-means are
efficient techniques, they often trap in local optima and have a slow
convergence rate in solving high-dimensional problems. To address these issues,
many successful meta-heuristic optimization algorithms and intelligence-based
methods have been introduced to attain the optimal solution in a reasonable
time. They are designed to escape from a local optimum problem by allowing
flexible movements or random behaviors. In this study, we attempt to
conceptualize a powerful approach using the three main components: Chimp
Optimization Algorithm (ChOA), Generalized Normal Distribution Algorithm
(GNDA), and Opposition-Based Learning (OBL) method. Firstly, two versions of
ChOA with two different independent groups' strategies and seven chaotic maps,
entitled ChOA(I) and ChOA(II), are presented to achieve the best possible
result for data clustering purposes. Secondly, a novel combination of ChOA and
GNDA algorithms with the OBL strategy is devised to solve the major
shortcomings of the original algorithms. Lastly, the proposed ChOAGNDA method
is a Selective Opposition (SO) algorithm based on ChOA and GNDA, which can be
used to tackle large and complex real-world optimization problems, particularly
data clustering applications. The results are evaluated against seven popular
meta-heuristic optimization algorithms and eight recent state-of-the-art
clustering techniques. Experimental results illustrate that the proposed work
significantly outperforms other existing methods in terms of the achievement in
minimizing the Sum of Intra-Cluster Distances (SICD), obtaining the lowest
Error Rate (ER), accelerating the convergence speed, and finding the optimal
cluster centers.Comment: 48 pages, 14 Tables, 12 Figure
Evolutionary Computation 2020
Intelligent optimization is based on the mechanism of computational intelligence to refine a suitable feature model, design an effective optimization algorithm, and then to obtain an optimal or satisfactory solution to a complex problem. Intelligent algorithms are key tools to ensure global optimization quality, fast optimization efficiency and robust optimization performance. Intelligent optimization algorithms have been studied by many researchers, leading to improvements in the performance of algorithms such as the evolutionary algorithm, whale optimization algorithm, differential evolution algorithm, and particle swarm optimization. Studies in this arena have also resulted in breakthroughs in solving complex problems including the green shop scheduling problem, the severe nonlinear problem in one-dimensional geodesic electromagnetic inversion, error and bug finding problem in software, the 0-1 backpack problem, traveler problem, and logistics distribution center siting problem. The editors are confident that this book can open a new avenue for further improvement and discoveries in the area of intelligent algorithms. The book is a valuable resource for researchers interested in understanding the principles and design of intelligent algorithms
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