scholarly journals Electromagnetism metaheuristic algorithm for solving the strong minimum energy topology problem

2013 ◽  
Vol 23 (1) ◽  
pp. 43-57 ◽  
Author(s):  
Aleksandar Kartelj
Keyword(s):  
Local Search ◽  
Em Algorithm ◽  
Objective Function ◽  
Minimum Energy ◽  
Higher Dimensions ◽  
Metaheuristic Algorithm ◽  
Optimal Solutions ◽  
Running Time ◽  
Speed Up ◽  
Problem Instances

In this paper electromagnetism (EM) metaheuristic is used for solving the NP-hard strong minimum energy topology problem (SMETP). Objective function is adapted to the problem so that it effectively prevents infeasible solutions. Proposed EM algorithm uses efficient local search to speed up overall running time. This approach is tested on two sets of randomly generated symmetric and asymmetric instances. EM reaches all known optimal solutions for these instances. The solutions are obtained in a reasonable running time even for the problem instances of higher dimensions.

scholarly journals Improving Information Centrality of a Node in Complex Networks by Adding Edges

2018 ◽  
Author(s):  
Liren Shan ◽  
Yuhao Yi ◽  
Zhongzhi Zhang
Keyword(s):  
Complex Networks ◽  
Objective Function ◽  
Optimization Problem ◽  
Network Node ◽  
Resistance Distance ◽  
Running Time ◽  
Practical Applications ◽  
Approximation Factor ◽  
Speed Up ◽  
Effectiveness And Efficiency

The problem of increasing the centrality of a network node arises in many practical applications. In this paper, we study the optimization problem of maximizing the information centrality Iv of a given node v in a network with n nodes and m edges, by creating k new edges incident to v. Since Iv is the reciprocal of the sum of resistance distance Rv between v and all nodes, we alternatively consider the problem of minimizing Rv by adding k new edges linked to v. We show that the objective function is monotone and supermodular. We provide a simple greedy algorithm with an approximation factor (1 − 1/e) and O(n^3) running time. To speed up the computation, we also present an algorithm to compute (1 − 1/e − epsilon) approximate resistance distance Rv after iteratively adding k edges, the running time of which is Otilde(mk*epsilon^−2) for any epsilon > 0, where the Otilde(·) notation suppresses the poly(log n) factors. We experimentally demonstrate the effectiveness and efficiency of our proposed algorithms.

A Tabu Search Algorithm for the Stage Shop Problem

2012 ◽  
Vol 433-440 ◽  
pp. 3124-3129
Author(s):  
Mohammad Mahdi Nasiri ◽  
Farhad Kianfar
Keyword(s):  
Lower Bound ◽  
Tabu Search ◽  
Objective Function ◽  
Job Shop ◽  
Search Algorithm ◽  
Tabu Search Algorithm ◽  
Computational Results ◽  
Optimal Solutions ◽  
Problem Instances ◽  
Special Case

This paper presents stage shop problem which is a special case of the general shop. The stage shop is a more realistic generalization of the mixed shop problem. In the stage shop problem, each job has several stages of operations. In order to solve the stage shop problem with makespan objective function, a tabu search algorithm is developed. In addition, an existing lower bound of the job shop is adapted to the new problem and the computational results have been compared to it. The proposed TS algorithm has reached the optimal solutions for about half of the problem instances

Opinion Dynamics Optimization by Varying Susceptibility to Persuasion via Non-Convex Local Search

2021 ◽  
Vol 16 (2) ◽  
pp. 1-34
Author(s):  
Rediet Abebe ◽  
T.-H. HUBERT Chan ◽  
Jon Kleinberg ◽  
Zhibin Liang ◽  
David Parkes ◽  
...  
Keyword(s):  
Local Search ◽  
Objective Function ◽  
Iterative Process ◽  
Large Scale ◽  
Opinion Dynamics ◽  
Theoretical Models ◽  
Global Optimum ◽  
Local Optimum ◽  
Opinion Formation ◽  
Long Line

A long line of work in social psychology has studied variations in people’s susceptibility to persuasion—the extent to which they are willing to modify their opinions on a topic. This body of literature suggests an interesting perspective on theoretical models of opinion formation by interacting parties in a network: in addition to considering interventions that directly modify people’s intrinsic opinions, it is also natural to consider interventions that modify people’s susceptibility to persuasion. In this work, motivated by this fact, we propose an influence optimization problem. Specifically, we adopt a popular model for social opinion dynamics, where each agent has some fixed innate opinion, and a resistance that measures the importance it places on its innate opinion; agents influence one another’s opinions through an iterative process. Under certain conditions, this iterative process converges to some equilibrium opinion vector. For the unbudgeted variant of the problem, the goal is to modify the resistance of any number of agents (within some given range) such that the sum of the equilibrium opinions is minimized; for the budgeted variant, in addition the algorithm is given upfront a restriction on the number of agents whose resistance may be modified. We prove that the objective function is in general non-convex. Hence, formulating the problem as a convex program as in an early version of this work (Abebe et al., KDD’18) might have potential correctness issues. We instead analyze the structure of the objective function, and show that any local optimum is also a global optimum, which is somehow surprising as the objective function might not be convex. Furthermore, we combine the iterative process and the local search paradigm to design very efficient algorithms that can solve the unbudgeted variant of the problem optimally on large-scale graphs containing millions of nodes. Finally, we propose and evaluate experimentally a family of heuristics for the budgeted variant of the problem.

EUCOR: An Efficient Unequal Clustering and Optimal Routing in wireless sensor networks for energy conservation

2021 ◽  
pp. 1-9
Author(s):  
C. Jothikumar ◽  
Revathi Venkataraman ◽  
T. Sai Raj ◽  
J. Selvin Paul Peter ◽  
T.Y.J. Nagamalleswari
Keyword(s):  
Objective Function ◽  
Physical Phenomenon ◽  
Minimum Energy ◽  
Cluster Head ◽  
Industrial Applications ◽  
Base Station ◽  
Wireless Sensor ◽  
Unequal Clustering ◽  
Minimum Energy Consumption ◽  
Variable Cluster

Wireless sensor network is a wide network that works as a cutting edge model in industrial applications. The sensor application is mostly used for high security systems that provide safety support to the environment. The sensor system senses the physical phenomenon, processes the input signal and communicates with the base station through its neighbors. Energy is the most important criterion to support a live network for long hours. In the proposed system, the EUCOR (Efficient Unequal Clustering and Optimized Routing) protocol uses the objective function to identify the efficient cluster head with variable cluster size. The computation of the objective function deals with the ant colony approach for minimum energy consumption and the varying size of the cluster in each cycle is calculated based on the competition radius. The system prolongs the lifespan of the nodes by minimizing the utilization of energy in the transmission of packets in the networks when compared with the existing system.

scholarly journals Temporal concatenation for Markov decision processes

2021 ◽  
pp. 1-28
Author(s):  
Ruiyang Song ◽  
Kuang Xu
Keyword(s):  
Markov Decision Processes ◽  
Large Scale ◽  
Optimal Solution ◽  
Upper Bounds ◽  
Black Box ◽  
Decision Processes ◽  
Optimal Solutions ◽  
Wide Range ◽  
Markov Decision ◽  
Speed Up

We propose and analyze a temporal concatenation heuristic for solving large-scale finite-horizon Markov decision processes (MDP), which divides the MDP into smaller sub-problems along the time horizon and generates an overall solution by simply concatenating the optimal solutions from these sub-problems. As a “black box” architecture, temporal concatenation works with a wide range of existing MDP algorithms. Our main results characterize the regret of temporal concatenation compared to the optimal solution. We provide upper bounds for general MDP instances, as well as a family of MDP instances in which the upper bounds are shown to be tight. Together, our results demonstrate temporal concatenation's potential of substantial speed-up at the expense of some performance degradation.

A DECOMPOSITION-COORDINATION METHOD FOR COMPLEX MULTI-OBJECTIVE SYSTEMS

2009 ◽  
Vol 26 (06) ◽  
pp. 735-757 ◽  
Author(s):  
F. MIGUEL ◽  
T. GÓMEZ ◽  
M. LUQUE ◽  
F. RUIZ ◽  
R. CABALLERO
Keyword(s):  
Information Exchange ◽  
Exchange Process ◽  
Efficient Solutions ◽  
Pareto Optimal ◽  
Relative Importance ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions ◽  
Conflicting Objectives ◽  
Upper Level ◽  
Problem Instances

The generation of Pareto optimal solutions for complex systems with multiple conflicting objectives can be easier if the problem can be decomposed and solved as a set of smaller coordinated subproblems. In this paper, a new decomposition-coordination method is proposed, where the global problem is partitioned into subsystems on the basis of the connection structure of the mathematical model, assigning a relative importance to each of them. In order to obtain Pareto optimal solutions for the global system, the aforementioned subproblems are coordinated taking into account their relative importance. The scheme that has been developed is an iterative one, and the global efficient solutions are found through a continuous information exchange process between the coordination level (upper level) and the subsystem level (lower level). Computational experiments on several randomly generated problem instances show that the suggested algorithm produces efficient solutions within reasonable computational times.

Adaptive Deep Rider LSTM-enabled Objective Functions for RPL routing in IoT applications

2022 ◽  
Vol 16 (1) ◽  
pp. 0-0
Keyword(s):  
Energy Dissipation ◽  
Objective Function ◽  
Minimum Energy ◽  
Link Quality ◽  
Maximum Throughput ◽  
Routing Metrics ◽  
Energy Prediction ◽  
Iot Applications ◽  
Energy Distance ◽  
Application Requirements

This paper presents a proposed Objective Function (OF) design using various routing metrics for improving the performance of IoT applications. The most important idea of the proposed design is the selection of the routing metrics with respect to the application requirements. The various metrics, such as Energy, Distance, Delay, Link quality, Trust (EDDLT) are used for improving the objective function design of the RPL in various IoT applications. Here, the Adaptive Deep rider LSTM is newly employed for the energy prediction where the Adaptive Deep Rider LSTM is devised by the combination of the adaptive theory with the Rider Adam Algorithm (RAA), and the Deep-Long Short Memory (Deep-LSTM). However, the evaluation of the proposed method is carried out energy dissipation, throughput, and delay by achieving a minimum energy dissipation of 0.549, maximum throughput of 1, and a minimum delay of 0.191, respectively.

scholarly journals An evolutionary-based optimization algorithm for truss sizing design

2016 ◽  
Vol 38 (4) ◽  
pp. 307-317
Author(s):  
Pham Hoang Anh
Keyword(s):  
Local Search ◽  
Objective Function ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Optimal Solution ◽  
The Other ◽  
Truss Structures ◽  
Benchmark Problems ◽  
Discrete Variables ◽  
Objective Functions

In this paper, the optimal sizing of truss structures is solved using a novel evolutionary-based optimization algorithm. The efficiency of the proposed method lies in the combination of global search and local search, in which the global move is applied for a set of random solutions whereas the local move is performed on the other solutions in the search population. Three truss sizing benchmark problems with discrete variables are used to examine the performance of the proposed algorithm. Objective functions of the optimization problems are minimum weights of the whole truss structures and constraints are stress in members and displacement at nodes. Here, the constraints and objective function are treated separately so that both function and constraint evaluations can be saved. The results show that the new algorithm can find optimal solution effectively and it is competitive with some recent metaheuristic algorithms in terms of number of structural analyses required.

scholarly journals Comparison of Serial and Parallel Computation on Predicting Missing Data with EM Algorithm

2021 ◽  
Vol 18 (1) ◽  
pp. 22-30
Author(s):  
Erna Nurmawati ◽  
Robby Hasan Pangaribuan ◽  
Ibnu Santoso
Keyword(s):  
Parallel Computing ◽  
Missing Data ◽  
Data Processing ◽  
Em Algorithm ◽  
Parallel Computation ◽  
Incomplete Data ◽  
Parallel Programs ◽  
Missing Value ◽  
Speed Up ◽  
Compute Time

One way to deal with the presence of missing value or incomplete data is to impute the data using EM Algorithm. The need for large and fast data processing is necessary to implement parallel computing on EM algorithm serial program. In the parallel program architecture of EM Algorithm in this study, the controller is only related to the EM module whereas the EM module itself uses matrix and vector modules intensively. Parallelization is done by using OpenMP in EM modules which results in faster compute time on parallel programs than serial programs. Parallel computing with a thread of 4 (four) increases speed up, reduces compute time, and reduces efficiency when compared to parallel computing by the number of threads 2 (two).

ON THE ACCELERATION OF OPTIMIZATION METHODS FOR THE PROBLEM OF SYNTHESIS OF MULTILAYER OPTICAL COATINGS

2021 ◽  
Vol 6 ◽  
pp. 13-26
Author(s):  
Alexander Mitsa ◽  
◽  
Petr Stetsyuk ◽  
Alexander Levchuk ◽  
Vasily Petsko ◽  
...  
Keyword(s):  
Objective Function ◽  
Golden Section ◽  
Optical Coatings ◽  
Generalized Gradient ◽  
Arithmetic Operations ◽  
Section Method ◽  
Suffix Arrays ◽  
Multidimensional Search ◽  
Golden Section Method ◽  
Speed Up

Five ways to speed up the multidimensional search in order to solve the problem of synthesis of multilayer optical coatings by using the methods of zero and first orders have been considered. The first way is to use an analytical derivative for the target quality function of the multilayer coating. It allows us to calculate accurately (within the computer arithmetic) the value of the gradient of a smooth objective function and generalized gradient of a non-smooth objective one. The first way requires the same number of arithmetic operations as well as finite-difference methods of calculating the gradient and the generalized gradient. The second way is to use a speedy finding of the objective function gradient using the prefix- and suffix-arrays in the analytical method of calculating the gradient. This technique allows us to reduce the number of arithmetic operations thrice for large-scale problems. The third way is the use of tabulating the values of trigonometric functions to calculate the characteristic matrices. This technique reduces the execution time of multiplication operations of characteristic matrices ten times depending on the computer’s specifications. For some computer architectures, this advantage is more than 140 times. The fourth method is the use of the golden section method for the one-dimensional optimization in the problems of synthesis of optical coatings. In particular, when solving one partial problem it is shown that the ternary search method requires approximately 40% more time than the golden section method. The fifth way is to use the effective implementation of multiplication of two matrices. It lies in changing the order of the second and third cycles for the well-known method of multiplying two matrices and fixing in a common variable value of the element of the first matrix. This allows us to speed up significantly the multiplication operation of two matrices. For matrices having 1000 x 1000 dimension the acceleration is from 2 to 15 times, depending on the computer's specifications.

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