Economical Load Dispatch Using Modified Harmony Memory Search Optimization Technique

2018 ◽  
Author(s):  
Tanmoy Mulo ◽  
Prasid Syam ◽  
Amalendu Bikash Choudhury
Keyword(s):  
Optimization Technique ◽  
Memory Search ◽  
Search Optimization ◽  
Harmony Memory

scholarly journals A modified simplex based direct search optimization algorithm for adaptive transversal FIR filters

2021 ◽  
Vol 104 (2) ◽  
pp. 003685042110254
Author(s):  
Armaghan Mohsin ◽  
Yazan Alsmadi ◽  
Ali Arshad Uppal ◽  
Sardar Muhammad Gulfam
Keyword(s):  
Optimization Algorithm ◽  
Optimization Technique ◽  
Fast Convergence ◽  
Good Convergence ◽  
Optimum Point ◽  
Search Optimization ◽  
Higher Dimensional ◽  
Gradient Based ◽  
Improved Accuracy ◽  
Quadratic Cost Function

In this paper, a novel modified optimization algorithm is presented, which combines Nelder-Mead (NM) method with a gradient-based approach. The well-known Nelder Mead optimization technique is widely used but it suffers from convergence issues in higher dimensional complex problems. Unlike the NM, in this proposed technique we have focused on two issues of the NM approach, one is shape of the simplex which is reshaped at each iteration according to the objective function, so we used a fixed shape of the simplex and we regenerate the simplex at each iteration and the second issue is related to reflection and expansion steps of the NM technique in each iteration, NM used fixed value of [Formula: see text], that is, [Formula: see text]  = 1 for reflection and [Formula: see text]  = 2 for expansion and replace the worst point of the simplex with that new point in each iteration. In this way NM search the optimum point. In proposed algorithm the optimum value of the parameter [Formula: see text] is computed and then centroid of new simplex is originated at this optimum point and regenerate the simplex with this centroid in each iteration that optimum value of [Formula: see text] will ensure the fast convergence of the proposed technique. The proposed algorithm has been applied to the real time implementation of the transversal adaptive filter. The application used to demonstrate the performance of the proposed technique is a well-known convex optimization problem having quadratic cost function, and results show that the proposed technique shows fast convergence than the Nelder-Mead method for lower dimension problems and the proposed technique has also good convergence for higher dimensions, that is, for higher filter taps problem. The proposed technique has also been compared with stochastic techniques like LMS and NLMS (benchmark) techniques. The proposed technique shows good results against LMS. The comparison shows that the modified algorithm guarantees quite acceptable convergence with improved accuracy for higher dimensional identification problems.

scholarly journals An enhanced krill herd optimization technique used for classification problem

2021 ◽  
Vol 30 (2) ◽  
pp. 354-364
Author(s):  
Firas Al-Mashhadani ◽  
Ibrahim Al-Jadir ◽  
Qusay Alsaffar
Keyword(s):  
Machine Learning ◽  
Simulated Annealing ◽  
Optimization Technique ◽  
Optimization Method ◽  
Classification Problem ◽  
Test Results ◽  
Internal Evaluation ◽  
Evaluation Measures ◽  
Search Optimization ◽  
Krill Herd

In this paper, this method is intended to improve the optimization of the classification problem in machine learning. The EKH as a global search optimization method, it allocates the best representation of the solution (krill individual) whereas it uses the simulated annealing (SA) to modify the generated krill individuals (each individual represents a set of bits). The test results showed that the KH outperformed other methods using the external and internal evaluation measures.

A New Simple Micro-PSO for High Dimensional Optimization Problem

2012 ◽  
Vol 236-237 ◽  
pp. 1195-1200
Author(s):  
Wen Hua Han
Keyword(s):  
Optimization Problem ◽  
Optimization Technique ◽  
Pso Algorithm ◽  
Population Based ◽  
Global Optimum ◽  
Stochastic Search ◽  
High Dimensional ◽  
Swarm Optimization ◽  
Search Optimization ◽  
Dimensional Optimization

The particle swarm optimization (PSO) algorithm is a population-based intelligent stochastic search optimization technique, which has already been widely used to various of fields. In this paper, a simple micro-PSO is proposed for high dimensional optimization problem, which is resulted from being introduced escape boundary and perturbation for global optimum. The advantages of the simple micro-PSO are more simple and easily implemented than the previous micro-PSO. Experiments were conducted using Griewank, Rosenbrock, Ackley, Tablets functions. The experimental results demonstrate that the simple micro-PSO are higher optimization precision and faster convergence rate than PSO and robust for the dimension of the optimization problem.

scholarly journals FSD-HSO Optimization Algorithm for Closed Fringes Interferogram Demodulation

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Ulises H. Rodriguez-Marmolejo ◽  
Miguel Mora-Gonzalez ◽  
Jesus Muñoz-Maciel ◽  
Tania A. Ramirez-delreal
Keyword(s):  
Relative Error ◽  
Optimization Technique ◽  
Harmony Search ◽  
Mathematical Optimization ◽  
Physical Nature ◽  
Algorithm Optimization ◽  
Interference Phenomenon ◽  
Sinusoidal Modulation ◽  
Search Optimization ◽  
Mathematical Optimization Model

Due to the physical nature of the interference phenomenon, extracting the phase of an interferogram is a known sinusoidal modulation problem. In order to solve this problem, a new hybrid mathematical optimization model for phase extraction is established. The combination of frequency guide sequential demodulation and harmony search optimization algorithms is used for demodulating closed fringes patterns in order to find the phase of interferogram applications. The proposed algorithm is tested in four sets of different synthetic interferograms, finding a range of average relative error in phase reconstructions of 0.14–0.39 rad. For reference, experimental results are compared with the genetic algorithm optimization technique, obtaining a reduction in the error up to 0.1448 rad. Finally, the proposed algorithm is compared with a very known demodulation algorithm, using a real interferogram, obtaining a relative error of 1.561 rad. Results are shown in patterns with complex fringes distribution.

scholarly journals Power Quality Improvement in HRES Grid Connected System with FOPID Based Atom Search Optimization Technique

Energies ◽  
2021 ◽  
Vol 14 (18) ◽  
pp. 5812
Author(s):  
Ch. Rami Reddy ◽  
B. Srikanth Goud ◽  
Flah Aymen ◽  
Gundala Srinivasa Rao ◽  
Edson C. Bortoni
Keyword(s):  
Power Systems ◽  
Power Quality ◽  
Reactive Power ◽  
Renewable Energy Sources ◽  
Optimization Technique ◽  
Harmonic Distortion ◽  
Reducing Power ◽  
Search Optimization ◽  
Power Quality Improvement ◽  
Power Quality Conditioner

An intelligent control strategy is proposed in this paper which suggests the Optimum Power Quality Enhancement (OPQE) of grid-connected hybrid power systems with solar photovoltaic, wind turbines, and battery storage. Unified Power Quality Conditioner with Active and Reactive power (UPQC-PQ) is designed with Atom Search Optimization (ASO) based Fractional-order Proportional Integral Derivative (FOPID) controller in the proposed Hybrid Renewable Energy Sources (HRES) system. The main aim is to regulate voltage while reducing power loss and reducing Total Harmonic Distortion (THD). UPQC-PQ is used to mitigate the Power Quality (PQ) problems such as sag, swell, interruptions, real power, reactive power and THD reductions related to voltage /current by using ASO based FOPID controller. The developed technique is demonstrated in various modes: simultaneous to improve PQ reinforcement and RES power injection, PRES > 0, PRES = 0. The results are then compared to those obtained using previous literature methods such as PI controller, GSA, BBO, GWO, ESA, RFA, and GA and found the proposed approach is efficient. The MATLAB/Simulink work framework is used to create the model.

scholarly journals Optimization of Complex System Reliability using Hybrid Grey Wolf Optimizer

2021 ◽  
Vol 4 (2) ◽  
pp. 241-256
Author(s):  
Ganga Negi ◽  
◽  
Anuj Kumar ◽  
Sangeeta Pant ◽  
Mangey Ram ◽  
...  
Keyword(s):  
Optimization Problems ◽  
Optimization Technique ◽  
Cuckoo Search ◽  
Optimization Methods ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Reliability Allocation ◽  
Search Optimization ◽  
Cuckoo Search Optimization ◽  
Comparison Of The Results

Reliability allocation to increase the total reliability has become a successful way to increase the efficiency of the complex industrial system designs. A lot of research in the past have tackled this problem to a great extent. This is evident from the different techniques developed so far to achieve the target. Stochastic metaheuristics like simulated annealing, Tabu search (TS), Particle Swarm Optimization (PSO), Cuckoo Search Optimization (CS), Genetic Algorithm (GA), Grey wolf optimization technique (GWO) etc. have been used in recent years. This paper proposes a framework for implementing a hybrid PSO-GWO algorithm for solving some reliability allocation and optimization problems. A comparison of the results obtained is done with the results of other well-known methods like PSO, GWO, etc. The supremacy/competitiveness of the proposed framework is demonstrated from the numerical experiments. These results with regard to the time taken for the computation and quality of solution outperform the previously obtained results by the other well-known optimization methods.

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