scholarly journals Experimental study on solution possibilities of multiextremal optimization problems through heuristic methods

2015 ◽  
Vol 15 (4) ◽  
pp. 82-93 ◽  
Author(s):  
Rudolf Neydorf ◽  
-Ivan- Chernogorov ◽  
Orhan Yarahmedov-- ◽  
Viktor Polyah--
Keyword(s):  
Experimental Study ◽  
Optimization Problems ◽  
Heuristic Methods ◽  
Multiextremal Optimization

Genetic Algorithm and Other Meta-Heuristics

2005 ◽  
pp. 144-173 ◽  
Author(s):  
Bernard K.S. Cheung
Keyword(s):  
Genetic Algorithm ◽  
Large Scale ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Schema Theory ◽  
Heuristic Methods ◽  
Optimal Solutions ◽  
Corporate Globalization ◽  
Global Optimal ◽  
Mutation And Selection

Genetic algorithms have been applied in solving various types of large-scale, NP-hard optimization problems. Many researchers have been investigating its global convergence properties using Schema Theory, Markov Chain, etc. A more realistic approach, however, is to estimate the probability of success in finding the global optimal solution within a prescribed number of generations under some function landscapes. Further investigation reveals that its inherent weaknesses that affect its performance can be remedied, while its efficiency can be significantly enhanced through the design of an adaptive scheme that integrates the crossover, mutation and selection operations. The advance of Information Technology and the extensive corporate globalization create great challenges for the solution of modern supply chain models that become more and more complex and size formidable. Meta-heuristic methods have to be employed to obtain near optimal solutions. Recently, a genetic algorithm has been reported to solve these problems satisfactorily and there are reasons for this.

scholarly journals Computational and experimental study of classification of a mixture of dissimilar components

Vestnik IGEU ◽  
2020 ◽  
pp. 56-63
Author(s):  
A.Ye. Barochkin ◽  
A.N. Belyakov ◽  
H. Otwinowski ◽  
T. Wylecial ◽  
E.V. Barochkin
Keyword(s):  
Experimental Study ◽  
Physical Properties ◽  
Optimization Problems ◽  
Systems Dynamics ◽  
Classification Problems ◽  
Relevant Issue ◽  
Separation Technology ◽  
The Difference ◽  
Main Component

The classification of particles by size is traditionally considered in relation to homogeneous materials, which must be divided into coarse and fine products. However, often there are the impurities in the material that differ in their physical properties from the base component. When classifying such mixtures, the difference in physical properties can be used to isolate, purify, or enrich the main component. The choice of the technology for such processing dissimilar components is possible based on simple and adequate models. The formulation and solution of classification problems for mixtures of dissimilar components on the basis of adequate models is the relevant issue for the power industry and related industries. Fundamental laws of dispersed systems dynamics are used to simulate the classification process; mathematical programming methods are used to identify models and improve separation technology. Experimental study of the separation of a mixture of dissimilar components in a two-stage classifying system has been carried out. Using the obtained experimental data, the model was identified, and its adequacy was shown. The presented experimental results and computational model can be used to formulate and solve optimization problems of fractionation of dispersed materials and to increase the efficiency of the process in classifying systems. The results obtained can be used in the energy, chemical and other industries to improve the efficiency of resource and energy-saving technologies for obtaining dispersed products with acceptable content of impurities.

scholarly journals Implementation of a parallel algorithm for searching the global extremum of a function on Intel Xeon Phi

2016 ◽  
pp. 101-110
Author(s):  
К.А. Баркалов ◽  
И.Г. Лебедев ◽  
В.В. Соврасов ◽  
А.В. Сысоев
Keyword(s):  
Parallel Algorithm ◽  
Optimization Problems ◽  
Computational Cost ◽  
Xeon Phi ◽  
Intel Xeon Phi ◽  
Global Extremum ◽  
Computing Systems ◽  
Multiextremal Optimization ◽  
Available Information ◽  
Intel Xeon

Предложен параллельный алгоритм решения задач многоэкстремальной оптимизации. Описывается реализация алгоритма на современных вычислительных системах с использованием сопроцессора Xeon Phi. Обсуждаются два подхода к распараллеливанию алгоритма, учитывающие информацию о трудоемкости вычисления значений оптимизируемой функции. Приводятся результаты вычислительных экспериментов, полученные на суперкомпьютере "Лобачевский". Показано, что реализация для Xeon Phi опережает версию для CPU. Результаты подтверждают ускорение алгоритма с использованием Xeon Phi по сравнению с алгоритмом, реализованным только на CPU. A parallel algorithm for solving multiextremal optimization problems is proposed. An implementation of the algorithm on modern computing systems using Intel Xeon Phi coprocessors is examined. Two approaches to algorithm parallelization are discussed with consideration of the available information on the computational cost for computing a given objective function. A number of numerical results obtained on a Lobachevsky supercomputer are analyzed. It is shown that the implementation of the algorithm using Xeon Phi is more efficient than that using CPU only. Computational experiments confirm this conclusion.

scholarly journals Particle Swarm Optimization Algorithm with Multiple Phases for Solving Continuous Optimization Problems

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Jing Li ◽  
Yifei Sun ◽  
Sicheng Hou
Keyword(s):  
Experimental Study ◽  
Particle Swarm Optimization ◽  
Numerical Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Continuous Optimization ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Fixed Phase ◽  
Continuous Optimization Problems

An algorithm with different parameter settings often performs differently on the same problem. The parameter settings are difficult to determine before the optimization process. The variants of particle swarm optimization (PSO) algorithms are studied as exemplars of swarm intelligence algorithms. Based on the concept of building block thesis, a PSO algorithm with multiple phases was proposed to analyze the relation between search strategies and the solved problems. Two variants of the PSO algorithm, which were termed as the PSO with fixed phase (PSOFP) algorithm and PSO with dynamic phase (PSODP) algorithm, were compared with six variants of the standard PSO algorithm in the experimental study. The benchmark functions for single-objective numerical optimization, which includes 12 functions in 50 and 100 dimensions, are used in the experimental study, respectively. The experimental results have verified the generalization ability of the proposed PSO variants.

scholarly journals Hyper-Heuristic Based on ACO and Local Search for Dynamic Optimization Problems

Algorithms ◽  
2021 ◽  
Vol 15 (1) ◽  
pp. 9
Author(s):  
Felipe Martins Müller ◽  
Iaê Santos Bonilha
Keyword(s):  
Local Search ◽  
Dynamic Optimization ◽  
Optimization Problems ◽  
Travelling Salesman Problem ◽  
Dynamic Test ◽  
Test Cases ◽  
Heuristic Methods ◽  
Dynamic Optimization Problems ◽  
Ant System ◽  
Travelling Salesman

Hyper-heuristics comprise a set of approaches that are motivated (at least in part) by the objective of intelligently combining heuristic methods to solve hard optimization problems. Ant colony optimization (ACO) algorithms have been proven to deal with Dynamic Optimization Problems (DOPs) properly. Despite the good results obtained by the integration of local search operators with ACO, little has been done to tackle DOPs. In this research, one of the most reliable ACO schemes, the MAX-MIN Ant System (MMAS), has been integrated with advanced and effective local search operators, resulting in an innovative hyper-heuristic. The local search operators are the Lin–Kernighan (LK) and the Unstringing and Stringing (US) heuristics, and they were intelligently chosen to improve the solution obtained by ACO. The proposed method aims to combine the adaptation capabilities of ACO for DOPs and the good performance of the local search operators chosen in an adaptive way and based on their performance, creating in this way a hyper-heuristic. The travelling salesman problem (TSP) was the base problem to generate both symmetric and asymmetric dynamic test cases. Experiments have shown that the MMAS provides good initial solutions to the local search operators and the hyper-heuristic creates a robust and effective method for the vast majority of test cases.

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