Decomposition Algorithms for a Multi-Hard Problem

2018 ◽  
Vol 26 (3) ◽  
pp. 507-533 ◽  
Author(s):  
M. R. Przybylek ◽  
A. Wierzbicki ◽  
Z. Michalewicz
Keyword(s):  
Systematic Study ◽  
Real World ◽  
Optimization Problems ◽  
Decomposition Algorithms ◽  
Hard Problem ◽  
The Past ◽  
Real World Problems

Real-world optimization problems have been studied in the past, but the work resulted in approaches tailored to individual problems that could not be easily generalized. The reason for this limitation was the lack of appropriate models for the systematic study of salient aspects of real-world problems. The aim of this article is to study one of such aspects: multi-hardness. We propose a variety of decomposition-based algorithms for an abstract multi-hard problem and compare them against the most promising heuristics.

scholarly journals An Ensemble Framework of Evolutionary Algorithm for Constrained Multi-Objective Optimization

Symmetry ◽  
2022 ◽  
Vol 14 (1) ◽  
pp. 116
Author(s):  
Junhua Ku ◽  
Fei Ming ◽  
Wenyin Gong
Keyword(s):  
Evolutionary Algorithms ◽  
Real World ◽  
Optimization Problems ◽  
State Of The Art ◽  
Multi Objective Optimization ◽  
The Real ◽  
Multi Objective ◽  
The Past ◽  
Hypervolume Indicator ◽  
Real World Problems

In the real-world, symmetry or asymmetry widely exists in various problems. Some of them can be formulated as constrained multi-objective optimization problems (CMOPs). During the past few years, handling CMOPs by evolutionary algorithms has become more popular. Lots of constrained multi-objective optimization evolutionary algorithms (CMOEAs) have been proposed. Whereas different CMOEAs may be more suitable for different CMOPs, it is difficult to choose the best one for a CMOP at hand. In this paper, we propose an ensemble framework of CMOEAs that aims to achieve better versatility on handling diverse CMOPs. In the proposed framework, the hypervolume indicator is used to evaluate the performance of CMOEAs, and a decreasing mechanism is devised to delete the poorly performed CMOEAs and to gradually determine the most suitable CMOEA. A new CMOEA, namely ECMOEA, is developed based on the framework and three state-of-the-art CMOEAs. Experimental results on five benchmarks with totally 52 instances demonstrate the effectiveness of our approach. In addition, the superiority of ECMOEA is verified through comparisons to seven state-of-the-art CMOEAs. Moreover, the effectiveness of ECMOEA on the real-world problems is also evaluated for eight instances.

Review of Network Flow Algorithms David P. Williamson

2021 ◽  
Vol 52 (1) ◽  
pp. 12-15
Author(s):  
S.V. Nagaraj
Keyword(s):  
Graduate Students ◽  
Real World ◽  
Network Flow ◽  
Network Flows ◽  
Optimization Problems ◽  
Capacity Constraints ◽  
Incoming Flow ◽  
Network Flow Problems ◽  
Flow Problems ◽  
Real World Problems

This book is on algorithms for network flows. Network flow problems are optimization problems where given a flow network, the aim is to construct a flow that respects the capacity constraints of the edges of the network, so that incoming flow equals the outgoing flow for all vertices of the network except designated vertices known as the source and the sink. Network flow algorithms solve many real-world problems. This book is intended to serve graduate students and as a reference. The book is also available in eBook (ISBN 9781316952894/US$ 32.00), and hardback (ISBN 9781107185890/US$99.99) formats. The book has a companion web site www.networkflowalgs.com where a pre-publication version of the book can be downloaded gratis.

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.

Enacting Actions in Simulated Environments

2009 ◽  
Author(s):  
Devin Pierce ◽  
Shulan Lu ◽  
Derek Harter
Keyword(s):  
Human Body ◽  
Common Sense ◽  
Real World ◽  
Mental Representations ◽  
The Past ◽  
Simulated Environments ◽  
The World ◽  
The Common ◽  
Bodily States ◽  
Real World Problems

The past decade has witnessed incredible advances in building highly realistic and richly detailed simulated worlds. We readily endorse the common-sense assumption that people will be better equipped for solving real-world problems if they are trained in near-life, even if virtual, scenarios. The past decade has also witnessed a significant increase in our knowledge of how the human body as both sensor and as effector relates to cognition. Evidence shows that our mental representations of the world are constrained by the bodily states present in our moment-to-moment interactions with the world. The current study investigated whether there are differences in how people enact actions in the simulated as opposed to the real world. The current study developed simple parallel task environments and asked participants to perform actions embedded in a stream of continuous events (e.g., cutting a cucumber). The results showed that participants performed actions at a faster speed and came closer to incurring injury to the fingers in the avatar enacting action environment than in the human enacting action environment.

AEGA: A New Real-Coded Genetic AlgorithmTaking Account of Extrapolation

2016 ◽  
Vol 20 (3) ◽  
pp. 429-437 ◽  
Author(s):  
Kento Uemura ◽  
◽  
Isao Ono
Keyword(s):  
Real World ◽  
Optimization Problems ◽  
Function Optimization ◽  
Benchmark Problems ◽  
Lens Design ◽  
Real World Problem ◽  
Real Coded Genetic Algorithm ◽  
Stable Manner ◽  
Ridge Structures ◽  
Real World Problems

This study proposes a new real-coded genetic algorithm (RCGA) taking account of extrapolation, which we call adaptive extrapolation RCGA (AEGA). Real-world problems are often formulated as black-box function optimization problems and sometimes have ridge structures and implicit active constraints. mAREX/JGG is one of the most powerful RCGAs that performs well against these problems. However, mAREX/JGG has a problem of search inefficiency. To overcome this problem, we propose AEGA that generates offspring outside the current population in a more stable manner than mAREX/JGG. Moreover, AEGA adapts the width of the offspring distribution automatically to improve its search efficiency. We evaluate the performance of AEGA using benchmark problems and show that AEGA finds the optimum with fewer evaluations than mAREX/JGG with a maximum reduction ratio of 45%. Furthermore, we apply AEGA to a lens design problem that is known as a difficult real-world problem and show that AEGA reaches the known best solution with approximately 25% fewer evaluations than mAREX/JGG.

scholarly journals A Survey on Meta-heuristic Algorithms for Global Optimization Problems

2020 ◽  
pp. 48-60
Author(s):  
Abdel Nasser H. Zaied ◽  
Mahmoud Ismail ◽  
Salwa El-Sayed ◽  
◽  
◽  
...  
Keyword(s):  
Global Optimization ◽  
Real World ◽  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Optimization Algorithms ◽  
Traditional Methods ◽  
Complex Problems ◽  
First Choice ◽  
Important Field ◽  
Real World Problems

Optimization is a more important field of research. With increasing the complexity of real-world problems, the more efficient and reliable optimization algorithms vital. Traditional methods are unable to solve these problems so, the first choice for solving these problems becomes meta-heuristic algorithms. Meta-heuristic algorithms proved their ability to solve more complex problems and giving more satisfying results. In this paper, we introduce the more popular meta-heuristic algorithms and their applications in addition to providing the more recent references for these algorithms.

President's Report: Linking Yesterday to Tomorrow

1994 ◽  
Vol 25 (5) ◽  
pp. 512-522
Author(s):  
Mary M. Lindquist
Keyword(s):  
Annual Meeting ◽  
Real World ◽  
Task Forces ◽  
Professional Groups ◽  
The Past ◽  
The One ◽  
Real World Problems ◽  
And Task

“Linkages,” our theme for this year's annual meeting, represents more than lin king yeste rday to tomorrow. It is about linking today. We need to link in many ways—with members, with each of the Affiliated Groups, with our committees and task forces, with the Headquarters staff, and with other professional groups. Most important, we need to link with our students, who are facing a much different world from the one that many of us experienced as students. We need to help them link ideas within mathematics and between mathematical topics and link mathematics to real-world problems. We need to strengthen many of our links, forge new ones, and sever some links to the past.

Communicating Advice: Introduction to the Special Issue

2020 ◽  
Vol 39 (3) ◽  
pp. 287-291
Author(s):  
Erina L. MacGeorge
Keyword(s):  
Social Support ◽  
Social Influence ◽  
Real World ◽  
Cultural Context ◽  
Special Issue ◽  
The Past ◽  
Methodological Improvement ◽  
Scholarly Attention ◽  
Real World Problems

Advice is a ubiquitous and consequential form of social support and social influence in virtually every social and cultural context, and has therefore garnered considerable scholarly attention over the past two decades, including the development of several theories specific to explaining advice evaluation and outcomes. The studies selected for this special issue extend existing theory through critique, extension, and integration; showcase methodological improvement and innovation; and illustrate meaningful application of theory and research to address real-world problems.

scholarly journals Optimization of Chance-Constrained Submodular Functions

2020 ◽  
Vol 34 (02) ◽  
pp. 1460-1467
Author(s):  
Benjamin Doerr ◽  
Carola Doerr ◽  
Aneta Neumann ◽  
Frank Neumann ◽  
Andrew Sutton
Keyword(s):  
Real World ◽  
Optimization Problems ◽  
Greedy Algorithms ◽  
Chance Constraints ◽  
Chance Constraint ◽  
High Quality ◽  
Submodular Optimization ◽  
Network Problems ◽  
Surrogate Functions ◽  
Real World Problems

Submodular optimization plays a key role in many real-world problems. In many real-world scenarios, it is also necessary to handle uncertainty, and potentially disruptive events that violate constraints in stochastic settings need to be avoided. In this paper, we investigate submodular optimization problems with chance constraints. We provide a first analysis on the approximation behavior of popular greedy algorithms for submodular problems with chance constraints. Our results show that these algorithms are highly effective when using surrogate functions that estimate constraint violations based on Chernoff bounds. Furthermore, we investigate the behavior of the algorithms on popular social network problems and show that high quality solutions can still be obtained even if there are strong restrictions imposed by the chance constraint.

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