scholarly journals Optimization of Resource Control for Transitions in Complex Systems

2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
Florin Pop
Keyword(s):  
Complex Systems ◽  
Distributed System ◽  
Resource Selection ◽  
Large Scale ◽  
Optimization Problem ◽  
Fitness Function ◽  
Industrial Process ◽  
Optimal Solution ◽  
Resource Control ◽  
Open Issue

In complex systems like Large-Scale Distributed Systems (LSDSs) the optimization of resource control is an open issue. The large number of resources and multicriteria optimization requirements make the optimization problem a complex one. The importance of resource control increases with the need of use for industrial process and manufacturing, being a key solution for QoS assuring. This paper presents different solutions for multiobjective decentralized control models for tasks assignment in LSDS. The transaction in real-time complex system is modeled in simulation by tasks which will be scheduled and executed in a distributed system, so a set of specifications and requirements are known. The paper presents a critical analysis of existing solutions and focuses on a genetic-based algorithm for optimization. The contribution of the algorithm is the fitness function that includes multiobjective criteria for optimization in different way. Several experimental scenarios, modeled using simulation, were considered to offer a support for analysis of near-optimal solution for resource selection.

A Progressive Approximation Approach for the Exact Solution of Sparse Large-Scale Binary Interdiction Games

2021 ◽  
Author(s):  
Claudio Contardo ◽  
Jorge A. Sefair
Keyword(s):  
Exact Solution ◽  
Approximation Algorithm ◽  
Facility Location ◽  
Shortest Path ◽  
Large Scale ◽  
Optimization Problem ◽  
Optimal Solution ◽  
Solution Space ◽  
Decision Variables ◽  
Algorithmic Framework

We present a progressive approximation algorithm for the exact solution of several classes of interdiction games in which two noncooperative players (namely an attacker and a follower) interact sequentially. The follower must solve an optimization problem that has been previously perturbed by means of a series of attacking actions led by the attacker. These attacking actions aim at augmenting the cost of the decision variables of the follower’s optimization problem. The objective, from the attacker’s viewpoint, is that of choosing an attacking strategy that reduces as much as possible the quality of the optimal solution attainable by the follower. The progressive approximation mechanism consists of the iterative solution of an interdiction problem in which the attacker actions are restricted to a subset of the whole solution space and a pricing subproblem invoked with the objective of proving the optimality of the attacking strategy. This scheme is especially useful when the optimal solutions to the follower’s subproblem intersect with the decision space of the attacker only in a small number of decision variables. In such cases, the progressive approximation method can solve interdiction games otherwise intractable for classical methods. We illustrate the efficiency of our approach on the shortest path, 0-1 knapsack and facility location interdiction games. Summary of Contribution: In this article, we present a progressive approximation algorithm for the exact solution of several classes of interdiction games in which two noncooperative players (namely an attacker and a follower) interact sequentially. We exploit the discrete nature of this interdiction game to design an effective algorithmic framework that improves the performance of general-purpose solvers. Our algorithm combines elements from mathematical programming and computer science, including a metaheuristic algorithm, a binary search procedure, a cutting-planes algorithm, and supervalid inequalities. Although we illustrate our results on three specific problems (shortest path, 0-1 knapsack, and facility location), our algorithmic framework can be extended to a broader class of interdiction problems.

Intelligent Optimization of Urban Real-Time Traffic Route Based on DNA Non-Dominated Sorting Genetic Algorithm

2015 ◽  
Vol 713-715 ◽  
pp. 1746-1749
Author(s):  
Min Zhu ◽  
Bo Su ◽  
Gang Min Ning
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problem ◽  
Complexity Analysis ◽  
Fitness Function ◽  
Optimal Solution ◽  
Urban Traffic ◽  
Route Optimization ◽  
Optimization Strategy ◽  
Traffic Condition ◽  
Real Time Traffic

The urban traffic condition is changed timely, so the traditional serial algorithm cannot satisfy the requirement of traffic scale and condition changes. Therefore, this paper proposes a DNA non-dominated sorting genetic algorithm for route optimization problem of multi-objects. First, through Pareto frontiers solution set optimization and algorithm complexity analysis, we determine the multi-objects problem to be optimized. Then we convert the problem into optimization problem of single-object fitness function, namely the elite populations optimization strategy, through which we can obtain the optimal solution of timely traffic condition.

A Programming Method of Vehicle Dispatch for Service Centre

2011 ◽  
Vol 121-126 ◽  
pp. 662-666 ◽  
Author(s):  
Hang Sheng Jia ◽  
Fei Cheng
Keyword(s):  
Assignment Problem ◽  
Large Scale ◽  
Simulated Annealing Algorithm ◽  
Fitness Function ◽  
Optimal Solution ◽  
Algorithm Performance ◽  
Service Centre ◽  
Vehicle Assignment ◽  
Annealing Algorithm ◽  
Natural Description

This paper combines Genetic Algorithm with Simulated Annealing Algorithm, namely GA-SA，to discuss vehicle paths and take into account the condition of time with respect to multi-spot service combination problem in service centre. The prevalent genetic algorithms easily lose the optimal solution, which affects the entire algorithm performance for reality vehicle assignment problem in the service centre. Based on modelling the vehicle assignment problem with natural description, fitness function, crossover operation and mutation operation are made the improvement in the approach. The process of computation has also considered own characteristics of the service centre to enable the algorithm optimized performance, in order to obtain the large scale enhancement.

Ride-Hailing Order Dispatching at DiDi via Reinforcement Learning

2020 ◽  
Vol 50 (5) ◽  
pp. 272-286
Author(s):  
Zhiwei (Tony) Qin ◽  
Xiaocheng Tang ◽  
Yan Jiao ◽  
Fan Zhang ◽  
Zhe Xu ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Process Model ◽  
Large Scale ◽  
Optimization Problem ◽  
Supply And Demand ◽  
Optimal Solution ◽  
System Response ◽  
Optimization Approach ◽  
Markov Decision ◽  
System Response Time

Order dispatching is instrumental to the marketplace engine of a large-scale ride-hailing platform, such as the DiDi platform, which continuously matches passenger trip requests to drivers at a scale of tens of millions per day. Because of the dynamic and stochastic nature of supply and demand in this context, the ride-hailing order-dispatching problem is challenging to solve for an optimal solution. Added to the complexity are considerations of system response time, reliability, and multiple objectives. In this paper, we describe how our approach to this optimization problem has evolved from a combinatorial optimization approach to one that encompasses a semi-Markov decision-process model and deep reinforcement learning. We discuss the various practical considerations of our solution development and real-world impact to the business.

scholarly journals A Simple Fitness Function for Minimum Attribute Reduction

2015 ◽  
Vol 2015 ◽  
pp. 1-6
Author(s):  
Yuebin Su ◽  
Jin Guo ◽  
Zejun Li
Keyword(s):  
Heuristic Search ◽  
Optimization Problem ◽  
Fitness Function ◽  
Attribute Reduction ◽  
Optimal Solution ◽  
Boolean Space ◽  
Fitness Functions ◽  
Minimal Subset ◽  
Classification Quality ◽  
Better Than

The goal of minimal attribute reduction is to find the minimal subsetRof the condition attribute setCsuch thatRhas the same classification quality asC. This problem is well known to be NP-hard. When only one minimal attribute reduction is required, it was transformed into a nonlinearly constrained combinatorial optimization problem over a Boolean space and some heuristic search approaches were used. In this case, the fitness function is one of the keys of this problem. It required that the fitness function must satisfy the equivalence between the optimal solution and the minimal attribute reduction. Unfortunately, the existing fitness functions either do not meet the equivalence, or are too complicated. In this paper, a simple and better fitness function based on positive domain was given. Theoretical proof shows that the optimal solution is equivalent to minimal attribute reduction. Experimental results show that the proposed fitness function is better than the existing fitness function for each algorithm in test.

A new algorithm for finding CRM-model coefficients

2019 ◽  
Vol 5 (3) ◽  
pp. 164-185 ◽  
Author(s):  
Alexander D. Bekman ◽  
Sergey V. Stepanov ◽  
Alexander A. Ruchkin ◽  
Dmitry V. Zelenin
Keyword(s):  
Approximate Solution ◽  
Optimization Problem ◽  
Optimal Solution ◽  
Complex Form ◽  
Approximate Solutions ◽  
The Other ◽  
Programming Algorithm ◽  
Stochastic Algorithms ◽  
Large Set ◽  
Flow Rates

The quantitative evaluation of producer and injector well interference based on well operation data (profiles of flow rates/injectivities and bottomhole/reservoir pressures) with the help of CRM (Capacitance-Resistive Models) is an optimization problem with large set of variables and constraints. The analytical solution cannot be found because of the complex form of the objective function for this problem. Attempts to find the solution with stochastic algorithms take unacceptable time and the result may be far from the optimal solution. Besides, the use of universal (commercial) optimizers hides the details of step by step solution from the user, for example&nbsp;— the ambiguity of the solution as the result of data inaccuracy.<br> The present article concerns two variants of CRM problem. The authors present a new algorithm of solving the problems with the help of “General Quadratic Programming Algorithm”. The main advantage of the new algorithm is the greater performance in comparison with the other known algorithms. Its other advantage is the possibility of an ambiguity analysis. This article studies the conditions which guarantee that the first variant of problem has a unique solution, which can be found with the presented algorithm. Another algorithm for finding the approximate solution for the second variant of the problem is also considered. The method of visualization of approximate solutions set is presented. The results of experiments comparing the new algorithm with some previously known are given.

scholarly journals Algebraic Solution to Constrained Bi-Criteria Decision Problem of Rating Alternatives through Pairwise Comparisons

Mathematics ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 303
Author(s):  
Nikolai Krivulin
Keyword(s):  
Decision Problem ◽  
Optimization Problem ◽  
Pareto Frontier ◽  
Sufficient Conditions ◽  
Approximation Error ◽  
Optimal Solution ◽  
Algebraic Solution ◽  
Pairwise Comparisons ◽  
Logarithmic Scale ◽  
Idempotent Algebra

We consider a decision-making problem to evaluate absolute ratings of alternatives from the results of their pairwise comparisons according to two criteria, subject to constraints on the ratings. We formulate the problem as a bi-objective optimization problem of constrained matrix approximation in the Chebyshev sense in logarithmic scale. The problem is to approximate the pairwise comparison matrices for each criterion simultaneously by a common consistent matrix of unit rank, which determines the vector of ratings. We represent and solve the optimization problem in the framework of tropical (idempotent) algebra, which deals with the theory and applications of idempotent semirings and semifields. The solution involves the introduction of two parameters that represent the minimum values of approximation error for each matrix and thereby describe the Pareto frontier for the bi-objective problem. The optimization problem then reduces to a parametrized vector inequality. The necessary and sufficient conditions for solutions of the inequality serve to derive the Pareto frontier for the problem. All solutions of the inequality, which correspond to the Pareto frontier, are taken as a complete Pareto-optimal solution to the problem. We apply these results to the decision problem of interest and present illustrative examples.

scholarly journals Leveraging Edge Intelligence for Video Analytics in Smart City Applications

Information ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 14
Author(s):  
Aluizio Rocha Neto ◽  
Thiago P. Silva ◽  
Thais Batista ◽  
Flávia C. Delicato ◽  
Paulo F. Pires ◽  
...  
Keyword(s):  
Distributed System ◽  
Smart City ◽  
Large Scale ◽  
Facial Recognition ◽  
Public Spaces ◽  
Hardware Accelerators ◽  
Video Streams ◽  
Video Analytics ◽  
Learning Tasks ◽  
Naive Approach

In smart city scenarios, the huge proliferation of monitoring cameras scattered in public spaces has posed many challenges to network and processing infrastructure. A few dozen cameras are enough to saturate the city’s backbone. In addition, most smart city applications require a real-time response from the system in charge of processing such large-scale video streams. Finding a missing person using facial recognition technology is one of these applications that require immediate action on the place where that person is. In this paper, we tackle these challenges presenting a distributed system for video analytics designed to leverage edge computing capabilities. Our approach encompasses architecture, methods, and algorithms for: (i) dividing the burdensome processing of large-scale video streams into various machine learning tasks; and (ii) deploying these tasks as a workflow of data processing in edge devices equipped with hardware accelerators for neural networks. We also propose the reuse of nodes running tasks shared by multiple applications, e.g., facial recognition, thus improving the system’s processing throughput. Simulations showed that, with our algorithm to distribute the workload, the time to process a workflow is about 33% faster than a naive approach.

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