PCR-RFLP Primer Design Using Particle Swarm Optimization Combined with Chaotic Logistic Map

2012 ◽  
Author(s):  
Li-Yeh Chuang ◽  
Yu-Da Lin ◽  
Hsueh-Wei Chang ◽  
Cheng-Hong Yang
Keyword(s):  
Particle Swarm Optimization ◽  
Logistic Map ◽  
Particle Swarm ◽  
Primer Design ◽  
Swarm Optimization ◽  
Pcr Rflp ◽  
Chaotic Logistic Map

scholarly journals An Investigation into the Performance of Particle Swarm Optimization with Various Chaotic Maps

2014 ◽  
Vol 2014 ◽  
pp. 1-17 ◽  
Author(s):  
Akugbe Martins Arasomwan ◽  
Aderemi Oluyinka Adewumi
Keyword(s):  
Particle Swarm Optimization ◽  
Chaotic Maps ◽  
Chaotic Map ◽  
Logistic Map ◽  
Particle Swarm ◽  
Test Problems ◽  
Swarm Optimization ◽  
Inertia Weight ◽  
Mathematical Problems ◽  
Logistic Chaotic Map

This paper experimentally investigates the effect of nine chaotic maps on the performance of two Particle Swarm Optimization (PSO) variants, namely, Random Inertia Weight PSO (RIW-PSO) and Linear Decreasing Inertia Weight PSO (LDIW-PSO) algorithms. The applications of logistic chaotic map by researchers to these variants have led to Chaotic Random Inertia Weight PSO (CRIW-PSO) and Chaotic Linear Decreasing Inertia Weight PSO (CDIW-PSO) with improved optimizing capability due to better global search mobility. However, there are many other chaotic maps in literature which could perhaps enhance the performances of RIW-PSO and LDIW-PSO more than logistic map. Some benchmark mathematical problems well-studied in literature were used to verify the performances of RIW-PSO and LDIW-PSO variants using the nine chaotic maps in comparison with logistic chaotic map. Results show that the performances of these two variants were improved more by many of the chaotic maps than by logistic map in many of the test problems. The best performance, in terms of function evaluations, was obtained by the two variants using Intermittency chaotic map. Results in this paper provide a platform for informative decision making when selecting chaotic maps to be used in the inertia weight formula of LDIW-PSO and RIW-PSO.

scholarly journals Chaotic-based particle swarm optimization algorithm for optimal PID tuning in automatic voltage regulator systems

2021 ◽  
pp. 50-59
Author(s):  
N. Anwar ◽  
A. Hanif ◽  
M.U. Ali ◽  
A. Zafar
Keyword(s):  
Particle Swarm Optimization ◽  
Electrical Power ◽  
Logistic Map ◽  
Particle Swarm ◽  
System Stability ◽  
Voltage Regulator ◽  
Swarm Optimization ◽  
Automatic Voltage Regulator ◽  
Terminal Voltage ◽  
Chaotic Particle Swarm Optimization

Introduction. In an electrical power system, the output of the synchronous generators varies due to disturbances or sudden load changes. These variations in output severely affect power system stability and power quality. The synchronous generator is equipped with an automatic voltage regulator to maintain its terminal voltage at rated voltage. Several control techniques utilized to improve the response of the automatic voltage regulator system, however, proportional integral derivative (PID) controller is the most frequently used controller but its parameters require optimization. Novelty. In this paper, the chaotic sequence based on the logistic map is hybridized with particle swarm optimization to find the optimal parameters of the PID for the automatic voltage regulator system. The logistic map chaotic sequence-based initialization and global best selection enable the algorithm to escape from local minima stagnation and improve its convergence rate resulting in best optimal parameters. Purpose. The main objective of the proposed approach is to improve the transient response of the automatic voltage regulator system by minimizing the maximum overshoot, settling time, rise time, and peak time values of the terminal voltage, and eliminating the steady-state error. Methods. In the process of parameter tuning, the Chaotic particle swarm optimization technique was run several times through the proposed hybrid objective function, which accommodates the advantages of the two most commonly used objective functions with a minimum number of iterations, and an optimal PID gain value was found. The proposed algorithm is compared with current metaheuristic algorithms including conventional particle swarm optimization, improved kidney algorithm, and others. Results. For performance evaluation, the characteristics of the integral of time multiplied squared error and Zwe-Lee Gaing objective functions are combined. Furthermore, the time-domain analysis, frequency-domain analysis, and robustness analysis are carried out to show the better performance of the proposed algorithm. The result shows that automatic voltage regulator tuned with the chaotic particle swarm optimization based PID yield improvement in overshoot, settling time, and function value of 14.41 %, 37.91 %, 1.73 % over recently proposed IKA, and 43.55 %, 44.5 %, 16.67 % over conventional particle swarm optimization algorithms. The improvement in transient response further improves the automatic voltage regulator system stability for electrical power systems.

scholarly journals An Optimal Solution for Smooth and Non-Smooth Cost Functions-Based Economic Dispatch Problem

Energies ◽  
2020 ◽  
Vol 13 (14) ◽  
pp. 3721 ◽  
Author(s):  
Chun-Yao Lee ◽  
Maickel Tuegeh
Keyword(s):  
Particle Swarm Optimization ◽  
Economic Dispatch ◽  
Logistic Map ◽  
Particle Swarm ◽  
Stable Solution ◽  
Optimal Solution ◽  
Cost Functions ◽  
Swarm Optimization ◽  
Chaotic Search ◽  
Modified Particle Swarm Optimization

A modified particle swarm optimization and incorporated chaotic search to solve economic dispatch problems for smooth and non-smooth cost functions, considering prohibited operating zones and valve-point effects is proposed in this paper. An inertia weight modification of particle swarm optimization is introduced to enhance algorithm performance and generate optimal solutions with stable solution accuracy and offers faster convergence characteristic. Moreover, an incorporation of chaotic search, called logistic map, is used to increase the global searching capability. To demonstrate the effectiveness and feasibility of the proposed algorithm compared to the several existing methods in the literature, five systems with different criteria are verified. The results show the excellent performance of the proposed method to solve economic dispatch problems.

An Improved Chaotic Binary Bat Algorithm for QoS Multicast Routing

2016 ◽  
Vol 25 (04) ◽  
pp. 1650025 ◽  
Author(s):  
Yassine Meraihi ◽  
Dalila Acheli ◽  
Amar Ramdane-Cherif
Keyword(s):  
Particle Swarm Optimization ◽  
Communication Networks ◽  
Heuristic Algorithms ◽  
Multicast Routing ◽  
Logistic Map ◽  
Particle Swarm ◽  
Bat Algorithm ◽  
Multicast Tree ◽  
Swarm Optimization ◽  
Routing Problem

The quality of service (QoS) multicast routing problem is one of the main issues for transmission in communication networks. It is known to be an NP-hard problem, so many heuristic algorithms have been employed to solve the multicast routing problem and find the optimal multicast tree which satisfies the requirements of multiple QoS constraints such as delay, delay jitter, bandwidth and packet loss rate. In this paper, we propose an improved chaotic binary bat algorithm to solve the QoS multicast routing problem. We introduce two modification methods into the binary bat algorithm. First, we use the two most representative chaotic maps, namely the logistic map and the tent map, to determine the parameter [Formula: see text] of the pulse frequency [Formula: see text]. Second, we use a dynamic formulation to update the parameter α of the loudness [Formula: see text]. The aim of these modifications is to enhance the performance and the robustness of the binary bat algorithm and ensure the diversity of the solutions. The simulation results reveal the superiority, effectiveness and efficiency of our proposed algorithms compared with some well-known algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Jumping Particle Swarm Optimization (JPSO), and Binary Bat Algorithm (BBA).

scholarly journals Hybrid particle swarm optimization with spiral-shaped mechanism for solving high-dimension problems

2020 ◽  
Vol 1 (1) ◽  
pp. 1-6
Author(s):  
Humberto Martins Mendonça Duarte ◽  
Rafael Lima de Carvalho
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Logistic Map ◽  
Particle Swarm ◽  
Dynamic Correction ◽  
Swarm Optimization ◽  
Local Optima ◽  
Inertia Weight ◽  
Hybrid Particle Swarm Optimization ◽  
Global Optima

Particle swarm optimization (PSO) is a well-known metaheuristic, whose performance for solving global optimization problems has been thoroughly explored. It has been established that without proper manipulation of the inertia weight parameter, the search for a global optima may fail. In order to handle this problem, we investigate the experimental performance of a PSO-based metaheuristic known as HPSO-SSM, which uses a logistic map sequence to control the inertia weight to enhance the diversity in the search process, and a spiral-shaped mechanism as a local search operator, as well as two dynamic correction factors to the position formula. Thus, we present an application of this variant for solving high-dimensional optimization problems, and evaluate its effectiveness against 24 benchmark functions. A comparison between both methods showed that the proposed variant can escape from local optima, and demonstrates faster convergence for almost every evaluated function.

scholarly journals Finger print Feature Extraction Using Hybrid Approach: QPSO and Bees Algorithms Based on 3D Logistic Map

2017 ◽  
Vol 9 (2) ◽  
Author(s):  
Noor Hayder Abdul Ameer
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm Optimization Algorithm ◽  
Quantum Particle ◽  
Logistic Map ◽  
Hybrid Approach ◽  
Particle Swarm ◽  
Bees Algorithm ◽  
Initial Population ◽  
Swarm Optimization ◽  
Quantum Particle Swarm Optimization

     Particle swarm optimization algorithm is easy to access premature convergence in the solution process, and also fall into the local optimal solution. The Bees algorithm is an inference optimization based on the foraging behavior of honey bees. It has been assured that this algorithm is able to search for global optimum, but there is one defect, it’s the fact that the global best solution is not used in a direct manner, but the Bees algorithm stores it of each iteration. We propose a new hybrid approach in order to address these problems, it is between Quantum particle swarm optimization and Bees algorithm based on 3D logistic map. On one hand, the 3D logistic map is employed for initializing uniform distributed particles so as to enhance the initial population quality, which is a very efficient yet simple method for improving the quality of initial population. On the other hand, the essence of this approach is the use Quantum particle swarm optimization for optimum fitness value of population in Bees algorithm. After determining the starting point in the new algorithm, the form of distribution is of circles with random angles and random diagonal where 3D logistic map generate the random numbers. The algorithm was applied for extracting the characteristics of the fingerprint, and the results when compared to the traditional particle swarm optimization algorithm were excellent.

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