Evaluation of the efficiency of the problem of choosing the extreme values of the parameters of the parametric family of regenerating processes

2021 ◽  
Author(s):  
D.V. Stroganov ◽  
V.M. Chernenky
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problem ◽  
Extreme Values ◽  
Simulation Models ◽  
Correct Choice ◽  
Simple Extension ◽  
Computational Costs ◽  
Unconstrained Optimization Problem ◽  
Search Optimization ◽  
Analytical Expressions

Thus, the formal formulation of the problem of evaluating the effectiveness of search optimization procedures on simulation models of regenerating processes under strict time constraints has been completed. A procedure for parametric tuning of the optimization algorithm has been developed, which sequentially refines the values of the functional specified by the model and redistributes the remaining model regeneration cycles between the investigated values of the controlled parameter. The problem of maximizing the probability of the correct choice is posed and solved, i.e. selection, according to the results of the simulation experiment on the model of the regenerating process, of the value of the controlled parameter that delivers the true maximum to the investigated functional. Based on the transition to the Lagrangian, the solution to the constrained optimization problem is reduced to an unconstrained optimization problem. Analytical expressions are obtained to assess the optimal distribution of regeneration cycles. It is shown that the simulation model with the included search engine optimization algorithm provides solutions that are quite effective in terms of computational costs. As a result, a method is proposed for a simple extension of the developed simulation models by including a search optimization algorithm, which makes it possible to move from modeling the system to optimizing its objective function on a given area of controlled parameters.

Penguins Search Optimization Algorithm for Community Detection in Complex Networks

2018 ◽  
Vol 9 (1) ◽  
pp. 1-14 ◽  
Author(s):  
Mohamed Guendouz ◽  
Abdelmalek Amine ◽  
Reda Mohamed Hamou
Keyword(s):  
Social Networks ◽  
Complex Networks ◽  
Community Detection ◽  
Optimization Algorithm ◽  
Optimization Problem ◽  
Search Optimization ◽  
Density Evaluation ◽  
Evaluation Measure ◽  
Modularity Density ◽  
Initialization Strategy

In the last decade, the problem of community detection in complex networks has attracted the attention of many researchers in many domains, several methods and algorithms have been proposed to deal with this problem, many of them consider it as an optimization problem and various bio-inspired algorithms have been applied to solve it. In this work, the authors propose a new method for community detection in complex networks using the Penguins Search Optimization Algorithm (PeSOA), the authors use the modularity density evaluation measure as a function to maximize and they propose also to enhance the algorithm by using a new initialization strategy. The proposed algorithm has been tested on four popular real-world networks; experimental results compared with other known algorithms show the effectiveness of using this method for community detection in social networks.

scholarly journals Chaotic-Search-Based Cultural Algorithm for Solving Unconstrained Optimization Problem

2011 ◽  
Vol 2011 ◽  
pp. 1-6 ◽  
Author(s):  
Jianjia He ◽  
Fuyuan Xu
Keyword(s):  
Optimization Problem ◽  
Function Optimization ◽  
Premature Convergence ◽  
Test Results ◽  
Cultural Algorithm ◽  
Unconstrained Optimization Problem ◽  
Search Optimization ◽  
Normative Knowledge ◽  
Chaos Search ◽  
Efficiency Test

For premature convergence and instability of cultural algorithm in solving function optimization problem, based on cultural algorithm and chaos search optimization, a chaos cultural algorithm (CCA) is proposed. The algorithm model consists of a chaos-based population space and a knowledge-storing belief space, uses normative knowledge and situational knowledge for chaos search and chaos perturbation, respectively, effectively avoids premature convergence of cultural algorithm, and overcomes chaos search optimization's sensitivity to initial values and poor efficiency. Test results show that this algorithm is strong in global search and has good performance in searching efficiency, precision, and stability, especially in solving high-dimensional optimization problem.

scholarly journals Image segmentation with a multilevel threshold using backtracking search optimization algorithm

2020 ◽  
Vol 25 (2) ◽  
pp. 102
Author(s):  
Hather Ibraheem Abed
Keyword(s):  
Image Segmentation ◽  
Optimization Algorithm ◽  
Optimization Problem ◽  
Threshold Method ◽  
Effective Technique ◽  
Backtracking Search ◽  
Search Optimization ◽  
Segmentation Methods ◽  
Backtracking Search Optimization Algorithm ◽  
Huge Challenge

Image segmentation is an important process in image processing. Though, there are many applications are affected by the segmentation methods and algorithms, unfortunately, not one technique, but the threshold is the popular one. Threshold technique can be categorized into two ways either simple threshold which has one threshold or multi- thresholds separated which has more than two thresholds . In this paper, image segmentation is used simple threshold method which is a simple and effective technique. Therefore, to calculate the value of threshold solution which is led to increase exponentially threshold that gives multi-thresholds image segmentation present a huge challenge. This paper is considered the multi-thresholds segmentation model for the optimization problem in order to overcome the problem of excessive calculation. The objective of this paper proposed an slgorithmto solve the optimization problem and realize multi-thresholds image segmentation. The proposed multi-thresholds segmentation algorithm should be segmented  the raw  image into pieces, and compared with other algorithms results. The experimental results that show multi-thresholds image segmentation based on backtracking search optimization algorithm are feasible and have a good segmentation.   http://dx.doi.org/10.25130/tjps.25.2020.036

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.

Comparative investigation of the moth-flame algorithm and whale optimization algorithm for optimal spur gear design

2021 ◽  
Vol 63 (3) ◽  
pp. 266-271
Author(s):  
Hammoudi Abderazek ◽  
Ferhat Hamza ◽  
Ali Riza Yildiz ◽  
Sadiq M. Sait
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problem ◽  
Spur Gear ◽  
Comparative Investigation ◽  
Whale Optimization Algorithm ◽  
Solution Quality ◽  
Gear Design ◽  
Geometric Conditions ◽  
Swarm Algorithms ◽  
Whale Optimization

Abstract In this study, two recent algorithms, the whale optimization algorithm and moth-flame optimization, are used to optimize spur gear design. The objective function is the minimization of the total weight of the spur gear pair. Moreover, the optimization problem is subjected to constraints on the main kinematic and geometric conditions as well as to the resistance of the material of the gear system. The comparison between moth-flame optimization (MFO), the whale optimization algorithm (WOA), and previous studies indicate that the final results obtained from both algorithms lead to a reduction in gear weight by 1.05 %. MFO and the WOA are compared with four additional swarm algorithms. The experimental results indicate that the algorithms introduced here, in particular MFO, outperform the four other methods when compared in terms of solution quality, robustness, and high success rate.

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