scholarly journals Two-Layer Network Caching for Different Service Requirements

2021 ◽  
Vol 13 (4) ◽  
pp. 85
Author(s):  
Gianluca Reali ◽  
Mauro Femminella
Keyword(s):  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Greedy Heuristic ◽  
Data Networks ◽  
Complex Data ◽  
Speed Up ◽  
User Access ◽  
Numerical Performance ◽  
Complete Optimization

Network caching is a technique used to speed-up user access to frequently requested contents in complex data networks. This paper presents a two-layer overlay network caching system for content distribution. It is used to define some caching scenarios with increasing complexity, which refers to real situations, including mobile 5G connectivity. For each scenario our aim is to maximize the hit ratio, which leads to the formulation of NP-complete optimization problems. The heuristic solutions proposed are based on the theory of the maximization of monotone submodular functions under matroid constraints. After the determination of the approximation ratio of the greedy heuristic algorithms proposed, a numerical performance analysis is shown. This analysis includes a comparison with the Least-Frequently Used (LFU) eviction strategy adapted to the analyzed systems. Results show very good performance, under the hypotheses of either known or unknown popularity of contents.

Comparison of Meta-heuristic Algorithms on Benchmark Functions

2019 ◽  
Vol 2 (3) ◽  
pp. 508-517
Author(s):  
FerdaNur Arıcı ◽  
Ersin Kaya
Keyword(s):  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Differential Evolution Algorithm ◽  
Population Based ◽  
Time Interval ◽  
Bee Colony ◽  
Different Characteristics

Optimization is a process to search the most suitable solution for a problem within an acceptable time interval. The algorithms that solve the optimization problems are called as optimization algorithms. In the literature, there are many optimization algorithms with different characteristics. The optimization algorithms can exhibit different behaviors depending on the size, characteristics and complexity of the optimization problem. In this study, six well-known population based optimization algorithms (artificial algae algorithm - AAA, artificial bee colony algorithm - ABC, differential evolution algorithm - DE, genetic algorithm - GA, gravitational search algorithm - GSA and particle swarm optimization - PSO) were used. These six algorithms were performed on the CEC’17 test functions. According to the experimental results, the algorithms were compared and performances of the algorithms were evaluated.

Opposition-based learning Multi-Verse Optimizer with disruption operator for optimization problems

2021 ◽  
Author(s):  
Mohammad Shehab ◽  
Laith Abualigah
Keyword(s):  
Real World ◽  
Optimization Problems ◽  
Second Stage ◽  
Opposition Based Learning ◽  
High Fitness ◽  
Fitness Value ◽  
Speed Up ◽  
Disruption Operator ◽  
The Given ◽  
Real World Problems

Abstract Multi-Verse Optimizer (MVO) algorithm is one of the recent metaheuristic algorithms used to solve various problems in different fields. However, MVO suffers from a lack of diversity which may trapping of local minima, and premature convergence. This paper introduces two steps of improving the basic MVO algorithm. The first step using Opposition-based learning (OBL) in MVO, called OMVO. The OBL aids to speed up the searching and improving the learning technique for selecting a better generation of candidate solutions of basic MVO. The second stage, called OMVOD, combines the disturbance operator (DO) and OMVO to improve the consistency of the chosen solution by providing a chance to solve the given problem with a high fitness value and increase diversity. To test the performance of the proposed models, fifteen CEC 2015 benchmark functions problems, thirty CEC 2017 benchmark functions problems, and seven CEC 2011 real-world problems were used in both phases of the enhancement. The second step, known as OMVOD, incorporates the disruption operator (DO) and OMVO to improve the accuracy of the chosen solution by giving a chance to solve the given problem with a high fitness value while also increasing variety. Fifteen CEC 2015 benchmark functions problems, thirty CEC 2017 benchmark functions problems and seven CEC 2011 real-world problems were used in both phases of the upgrade to assess the accuracy of the proposed models.

A new three-term spectral conjugate gradient algorithm with higher numerical performance for solving large scale optimization problems based on Quasi-Newton equation

2021 ◽  
pp. 2150053
Author(s):  
Jie Guo ◽  
Zhong Wan
Keyword(s):  
Conjugate Gradient ◽  
Large Scale ◽  
Line Search ◽  
Optimization Problems ◽  
Gradient Algorithm ◽  
Large Scale Optimization ◽  
Newton Equation ◽  
Numerical Performance ◽  
Scale Optimization ◽  
Quasi Newton

A new spectral three-term conjugate gradient algorithm in virtue of the Quasi-Newton equation is developed for solving large-scale unconstrained optimization problems. It is proved that the search directions in this algorithm always satisfy a sufficiently descent condition independent of any line search. Global convergence is established for general objective functions if the strong Wolfe line search is used. Numerical experiments are employed to show its high numerical performance in solving large-scale optimization problems. Particularly, the developed algorithm is implemented to solve the 100 benchmark test problems from CUTE with different sizes from 1000 to 10,000, in comparison with some similar ones in the literature. The numerical results demonstrate that our algorithm outperforms the state-of-the-art ones in terms of less CPU time, less number of iteration or less number of function evaluation.

Discrete Social Spider Algorithm for Solving Traveling Salesman Problem

2021 ◽  
pp. 2150020
Author(s):  
Asieh Khosravanian ◽  
Mohammad Rahmanimanesh ◽  
Parviz Keshavarzi
Keyword(s):  
Genetic Algorithm ◽  
Discrete Optimization ◽  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Fitness Function ◽  
Traveling Salesman ◽  
The Other ◽  
Discrete Optimization Problem ◽  
Social Spider ◽  
Social Spider Algorithm

The Social Spider Algorithm (SSA) was introduced based on the information-sharing foraging strategy of spiders to solve the continuous optimization problems. SSA was shown to have better performance than the other state-of-the-art meta-heuristic algorithms in terms of best-achieved fitness values, scalability, reliability, and convergence speed. By preserving all strengths and outstanding performance of SSA, we propose a novel algorithm named Discrete Social Spider Algorithm (DSSA), for solving discrete optimization problems by making some modifications to the calculation of distance function, construction of follow position, the movement method, and the fitness function of the original SSA. DSSA is employed to solve the symmetric and asymmetric traveling salesman problems. To prove the effectiveness of DSSA, TSPLIB benchmarks are used, and the results have been compared to the results obtained by six different optimization methods: discrete bat algorithm (IBA), genetic algorithm (GA), an island-based distributed genetic algorithm (IDGA), evolutionary simulated annealing (ESA), discrete imperialist competitive algorithm (DICA) and a discrete firefly algorithm (DFA). The simulation results demonstrate that DSSA outperforms the other techniques. The experimental results show that our method is better than other evolutionary algorithms for solving the TSP problems. DSSA can also be used for any other discrete optimization problem, such as routing problems.

scholarly journals Comparative Study of Optimization Algorithms for The Optimal Reservoir Operation

2018 ◽  
Vol 246 ◽  
pp. 01003
Author(s):  
Xinyuan Liu ◽  
Yonghui Zhu ◽  
Lingyun Li ◽  
Lu Chen
Keyword(s):  
Reservoir Operation ◽  
Optimization Problems ◽  
Optimization Algorithms ◽  
Complex Problem ◽  
Optimization Techniques ◽  
Decision Variables ◽  
Handling Methods ◽  
Optimality Algorithm ◽  
Parameter Values

Apart from traditional optimization techniques, e.g. progressive optimality algorithm (POA), modern intelligence algorithms, like genetic algorithms, differential evolution have been widely used to solve optimization problems. This paper deals with comparative analysis of POA, GA and DE and their applications in a reservoir operation problem. The results show that both GA and DES are feasible to reservoir operation optimization, but they display different features. GA and DE have many parameters and are difficult in determination of these parameter values. For simple problems with mall number of decision variables, GA and DE are better than POA when adopting appropriate parameter values and constraint handling methods. But for complex problem with large number of variables, POA combined with simplex method are much superior to GA and DE in time-assuming and quality of optimal solutions. This study helps to select proper optimization algorithms and parameter values in reservoir operation.

scholarly journals An integrated platform for intuitive mathematical programming modeling using LaTeX

2018 ◽  
Vol 4 ◽  
pp. e161 ◽  
Author(s):  
Charalampos P. Triantafyllidis ◽  
Lazaros G. Papageorgiou
Keyword(s):  
Mathematical Programming ◽  
Error Detection ◽  
Development Process ◽  
Optimization Problems ◽  
Scientific Work ◽  
Input Language ◽  
Speed Up ◽  
Cross Platform ◽  
Algebraic Modeling Language ◽  
Friendly Graphical User Interface

This paper presents a novel prototype platform that uses the same LaTeX mark-up language, commonly used to typeset mathematical content, as an input language for modeling optimization problems of various classes. The platform converts the LaTeX model into a formal Algebraic Modeling Language (AML) representation based on Pyomo through a parsing engine written in Python and solves by either via NEOS server or locally installed solvers, using a friendly Graphical User Interface (GUI). The distinct advantages of our approach can be summarized in (i) simplification and speed-up of the model design and development process (ii) non-commercial character (iii) cross-platform support (iv) easier typo and logic error detection in the description of the models and (v) minimization of working knowledge of programming and AMLs to perform mathematical programming modeling. Overall, this is a presentation of a complete workable scheme on using LaTeX for mathematical programming modeling which assists in furthering our ability to reproduce and replicate scientific work.

scholarly journals Integrated Offline and Online Decision Making under Uncertainty

2021 ◽  
Vol 70 ◽  
pp. 77-117
Author(s):  
Allegra De Filippo ◽  
Michele Lombardi ◽  
Michela Milano
Keyword(s):  
Optimization Problems ◽  
Greedy Heuristic ◽  
Management Problem ◽  
Quality Improvements ◽  
Solution Quality ◽  
Convex Optimization Problem ◽  
Routing Problem ◽  
Multi Stage ◽  
The Cost ◽  
Parametric Convex Optimization

This paper considers multi-stage optimization problems under uncertainty that involve distinct offline and online phases. In particular it addresses the issue of integrating these phases to show how the two are often interrelated in real-world applications. Our methods are applicable under two (fairly general) conditions: 1) the uncertainty is exogenous; 2) it is possible to define a greedy heuristic for the online phase that can be modeled as a parametric convex optimization problem. We start with a baseline composed by a two-stage offline approach paired with the online greedy heuristic. We then propose multiple methods to tighten the offline/online integration, leading to significant quality improvements, at the cost of an increased computation effort either in the offline or the online phase. Overall, our methods provide multiple options to balance the solution quality/time trade-off, suiting a variety of practical application scenarios. To test our methods, we ground our approaches on two real cases studies with both offline and online decisions: an energy management problem with uncertain renewable generation and demand, and a vehicle routing problem with uncertain travel times. The application domains feature respectively continuous and discrete decisions. An extensive analysis of the experimental results shows that indeed offline/online integration may lead to substantial benefits.

scholarly journals Hybrid Genetic Algorithm and Simulated Annealing for Function Optimization

2017 ◽  
Vol 1 (2) ◽  
pp. 82 ◽  
Author(s):  
Tirana Noor Fatyanosa ◽  
Andreas Nugroho Sihananto ◽  
Gusti Ahmad Fanshuri Alfarisy ◽  
M Shochibul Burhan ◽  
Wayan Firdaus Mahmudy
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Optimization Problems ◽  
Linear Optimization ◽  
Hybrid Genetic Algorithm ◽  
Optimal Solution ◽  
Function Optimization ◽  
Non Linear ◽  
Speed Up ◽  
Linear Problems

The optimization problems on real-world usually have non-linear characteristics. Solving non-linear problems is time-consuming, thus heuristic approaches usually are being used to speed up the solution’s searching. Among of the heuristic-based algorithms, Genetic Algorithm (GA) and Simulated Annealing (SA) are two among most popular. The GA is powerful to get a nearly optimal solution on the broad searching area while SA is useful to looking for a solution in the narrow searching area. This study is comparing performance between GA, SA, and three types of Hybrid GA-SA to solve some non-linear optimization cases. The study shows that Hybrid GA-SA can enhance GA and SA to provide a better result

DEVELOPMENT OF MODELS FOR DIAGNOSTIC DIABETES BASED ON FUZZY LOGIC METODS

2021 ◽  
pp. 90-95
Author(s):  
Евгений Николаевич Коровин ◽  
Екатерина Ивановна Новикова ◽  
Олег Валерьевич Родионов
Keyword(s):  
Mathematical Modeling ◽  
Decision Making ◽  
Information Technology ◽  
Fuzzy Logic ◽  
Human Body ◽  
Patient Information ◽  
Decision Making Process ◽  
Speed Up ◽  
Definition Of

В статье рассматриваются разработки методов интеллектуальной поддержки процесса диагностики сахарного диабета, а также определение его типа. В последние годы количество людей, страдающих данным заболеванием, неуклонно растет, а без своевременной диагностики эта патология может нанести огромный вред организму человека. Сахарный диабет 1 типа опасен тем, что в основном возникает у людей молодого возраста. Оперативное обнаружение диабета, а также определение его типа, поможет не только избежать возможных осложнений, но и в некоторых случаях предотвратить смерть пациента. Информационные технологии все чаще используются в различных сферах деятельности для разработки новых или совершенствования существующих методов обработки данных, особенно это можно заметить в сфере медицины. В настоящее время врач самостоятельно ставит диагноз, основываясь на результатах различных анализов, однако, для ускорения процесса принятия решения, можно воспользоваться методами математического моделирования, а именно: моделями диагностики диабета на основе нечеткой логики. Для наибольшего удобства данный способ распознавания заболевания впоследствии можно реализовать в информационно-программное обеспечение, которое сможет еще больше увеличить эффективность и скорость распознавания патологии The article discusses the issues of the incidence of diabetes in the population, in particular, the definition of its type. In recent years, the number of people suffering from this disease has been steadily growing, and without timely diagnosis, this pathology can cause enormous harm to the human body. Prompt detection of diabetes, as well as determination of its type, will help not only avoid possible complications, but also in some cases prevent the death of the patient. Information technology is increasingly being used in various fields of activity to develop new or improve existing methods of data processing, especially in the field of medicine. Currently, the doctor independently makes a diagnosis based on the results of various analyzes, however, to speed up the decision-making process, you can use the methods of mathematical modeling, namely, models of diabetes diagnostics based on fuzzy logic. For the greatest convenience, this method of disease recognition can subsequently be implemented in information software, which can further increase the efficiency and speed of pathology recognition

Multiobjective optimization design of hybrid composite laminates for vibration using sequential permutation table method

2021 ◽  
pp. 095440622110504
Author(s):  
Zhao Jing ◽  
Qin Sun ◽  
Yongjie Zhang ◽  
Ke Liang
Keyword(s):  
Multiobjective Optimization ◽  
Fundamental Frequency ◽  
Hybrid Composite ◽  
Composite Laminates ◽  
Optimization Design ◽  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Design Flexibility ◽  
The Cost ◽  
Ply Orientation

Due to the large variable design space in optimization problems of composite laminates, it remains one of the challenging tasks to develop efficient optimization design methods to improve the design flexibility and efficiency. This work presents a sequential permutation table (SPT) method for the multiobjective optimization design of two-material hybrid composite laminates with simply supported boundary conditions, which maximizes the fundamental frequency and minimizes the cost/weight. Based on the vibration analysis of hybrid composite laminates, the approximate linear regularity of the square of fundamental frequency is derived, and two best ply orientations for the two materials are identified, respectively. By assigning one best ply orientation with maximum fundamental frequency at respective stacking positions, and using another best ply orientation to replace plies in the stacking sequence from the mid-plane to the outermost can lead to the optimum. Two multiobjective optimization problems are employed to verify the SPT method, results are compared with those obtained by heuristic algorithms. The obtained better solutions demonstrate the effectiveness and efficiency of the SPT method and its potentials for optimal design of hybrid composite laminates.

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