scholarly journals AND/OR Branch-and-Bound on a Computational Grid

2017 ◽  
Vol 59 ◽  
pp. 351-435 ◽  
Author(s):  
Lars Otten ◽  
Rina Dechter
Keyword(s):  
Branch And Bound ◽  
Empirical Evaluation ◽  
Search Space ◽  
Computational Grid ◽  
Complex Problem ◽  
Parallel Execution ◽  
Machine Learning Techniques ◽  
Depth Analysis ◽  
Parallel Speedup ◽  
Problem Instances

We present a parallel AND/OR Branch-and-Bound scheme that uses the power of a computational grid to push the boundaries of feasibility for combinatorial optimization. Two variants of the scheme are described, one of which aims to use machine learning techniques for parallel load balancing. In-depth analysis identifies two inherent sources of parallel search space redundancies that, together with general parallel execution overhead, can impede parallelization and render the problem far from embarrassingly parallel. We conduct extensive empirical evaluation on hundreds of CPUs, the first of its kind, with overall positive results. In a significant number of cases parallel speedup is close to the theoretical maximum and we are able to solve many very complex problem instances orders of magnitude faster than before; yet analysis of certain results also serves to demonstrate the inherent limitations of the approach due to the aforementioned redundancies.

Economic Load Dispatch Using Bacterial Foraging Optimization Algorithm Based on Evolution Strategies

2013 ◽  
Vol 860-863 ◽  
pp. 2040-2045 ◽  
Author(s):  
Xiao Hua Feng ◽  
Yu Yao He ◽  
Juan Yu
Keyword(s):  
Search Space ◽  
Complex Problem ◽  
Evolution Strategies ◽  
Power Losses ◽  
Bacterial Foraging Optimization ◽  
Thermal Generation ◽  
Bacterial Foraging ◽  
Global And Local ◽  
Generation Test ◽  
Bacterial Foraging Optimization Algorithm

This paper presents a novel modified bacterial foraging optimization(BFO) to solve economic loaddispatch (ELD) problems. BFO isalready successfully employed to solve variousoptimization problems. However original BFOfor small problems with moderate dimensionand searching space is satisfactory. As searchspace and complexity growexponentially in scalable ELD problems, it shows poorconvergence properties. To tackle this complex problem considering itshigh-dimensioned search space, the Evolution Strategies is introduced to thebasic BFO. The chemotactic step is adjusted to have a dynamic non-linearbehavior in order to improve balancing the global and local search. Theproposed algorithm is validated using several thermal generation test systems.The results are compared with those obtained by other algorithms previouslyapplied to solve the problem considering valve-point effects and power losses.

scholarly journals Minimizing the Number of Tardy Jobs on a Single Machine with an Availability Constraint

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Ehsan Molaee ◽  
Ghasem Moslehi
Keyword(s):  
Branch And Bound ◽  
Single Machine ◽  
Planning Horizon ◽  
Single Machine Scheduling ◽  
Upper And Lower Bounds ◽  
Scheduling Problems ◽  
Bearing Witness ◽  
Number Of Tardy Jobs ◽  
Problem Instances ◽  
Tardy Jobs

Most scheduling problems are based on the assumption that machines work continuously during the planning horizon. This assumption is not true in many production environments because the machine may not be available during one or more periods such as during breakdowns or maintenance operations. In this paper, the problem of the single machine scheduling with one unavailability period and nonresumable jobs with the aim of minimizing the number of tardy jobs is studied. A number of theorems are proved and a heuristic procedure is developed to solve the problem. A branch-and-bound approach is also presented which includes upper and lower bounds and efficient dominance rules. Computational results for 2680 problem instances show that the branch-and-bound approach is capable of solving 98.7% of the instances optimally, bearing witness to the efficiency of the proposed procedure. Our results also indicate that the proposed approaches are more efficient when compared to other methods.

scholarly journals Scaling Up AND/OR Abstraction Sampling

2020 ◽  
Author(s):  
Kalev Kask ◽  
Bobak Pezeshki ◽  
Filjor Broka ◽  
Alexander Ihler ◽  
Rina Dechter
Keyword(s):  
Importance Sampling ◽  
State Of The Art ◽  
Empirical Evaluation ◽  
Search Space ◽  
Scaling Up ◽  
Sampling Scheme ◽  
Search Spaces

Abstraction Sampling (AS) is a recently introduced enhancement of Importance Sampling that exploits stratification by using a notion of abstractions: groupings of similar nodes into abstract states. It was previously shown that AS performs particularly well when sampling over an AND/OR search space; however, existing schemes were limited to ``proper'' abstractions in order to ensure unbiasedness, severely hindering scalability. In this paper, we introduce AOAS, a new Abstraction Sampling scheme on AND/OR search spaces that allow more flexible use of abstractions by circumventing the properness requirement. We analyze the properties of this new algorithm and, in an extensive empirical evaluation on five benchmarks, over 480 problems, and comparing against other state of the art algorithms, illustrate AOAS's properties and show that it provides a far more powerful and competitive Abstraction Sampling framework.

Using Texture Feature in Fruit Classification

2021 ◽  
Vol 39 (1B) ◽  
pp. 67-79
Author(s):  
Mauj H. Abd al kreem ◽  
Abd allameer A. Karim
Keyword(s):  
Fruits And Vegetables ◽  
Real Life ◽  
Texture Feature ◽  
Extraction Methods ◽  
Complex Problem ◽  
Machine Learning Techniques ◽  
Tree Classifier ◽  
Fresh Products ◽  
Occurrence Matrix

Recent advances in computer vision have allowed wide-ranging applications in every area of ​​life. One such area of ​​application is the classification of fresh products, but the classification of fruits and vegetables has proven to be a complex problem and needs further development. In recent years, various machine learning techniques have been exploited with many methods of describing the different features of fruit and vegetable classification in many real-life applications. Classification of fruits and vegetables presents significant challenges due to similarities between layers and irregular characteristics within the class.Hence , in this work, three feature extractor/ descriptor which are local binary pattern (LBP), gray level co-occurrence matrix (GLCM) and, histogram of oriented gradient(HoG) has been proposed to extract fruite features , the  extracted  features have been saved in three feature vectors , then desicion tree classifier has been proposed to classify the fruit types. fruits 360 datasets  is  used  in this work,   where 70% of the dataset were used  in the training phase while 30% of it used in the testing phase. The three proposed feature extruction methods plus the tree  classifier have been used to  classifying  fruits 360 images, results show that the the three feature extraction methods  give a promising results , while the HoG method yielded a poerfull results in which  the accuracy obtained is 96%.

Machine Learning Techniques for Underwater Wireless Sensor Networks

2021 ◽  
pp. 194-211
Author(s):  
Deepti Rani ◽  
Anju Sangwan ◽  
Anupma Sangwan ◽  
Tajinder Singh
Keyword(s):  
Machine Learning ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Information Seeking ◽  
Complex Problem ◽  
Machine Learning Techniques ◽  
Wireless Sensor ◽  
Learning Techniques ◽  
Primary Focus

With the enormous growth of sensor networks, information seeking from such networks has become an invaluable source of knowledge for various organizations to enhance the comprehension of people interests. Not only wireless sensor networks (WSNs) but its various classes also remain the hot topics of research. In this chapter, the primary focus is to understand the concept of sensor network in underwater scenario. Various mechanisms are used to recognize the activities underwater using sensor which examines the real-time events. With these features, a few challenges are also associated with sensor networks, which are addressed here. Machine learning (ML) techniques are the perfect key of success to resolve such issues due to their feasibility and adaption in complex problem environment. Therefore, various ML techniques have been explained to enhance the operational performance of WSNs, especially in underwater WSNs (UWSNs). The main objective of this chapter is to understand the concepts of UWSNs and role of ML to address the performance issues of UWSNs.

Enumeration of the Nondominated Set of Multiobjective Discrete Optimization Problems

2020 ◽  
Author(s):  
Satya Tamby ◽  
Daniel Vanderpooten
Keyword(s):  
Discrete Optimization ◽  
Optimization Problems ◽  
Search Space ◽  
Integer Program ◽  
Starting Solution ◽  
Nondominated Set ◽  
Previous State ◽  
Nondominated Points ◽  
Problem Instances ◽  
Discrete Optimization Problems

In this paper, we propose a generic algorithm to compute exactly the set of nondominated points for multiobjective discrete optimization problems. Our algorithm extends the ε-constraint method, originally designed for the biobjective case only, to solve problems with two or more objectives. For this purpose, our algorithm splits the search space into zones that can be investigated separately by solving an integer program. We also propose refinements, which provide extra information on several zones, allowing us to detect, and discard, empty parts of the search space without checking them by solving the associated integer programs. This results in a limited number of calls to the integer solver. Moreover, we can provide a feasible starting solution before solving every program, which significantly reduces the time spent for each resolution. The resulting algorithm is fast and simple to implement. It is compared with previous state-of-the-art algorithms and is seen to outperform them significantly on the experimented problem instances.

Optimization of Evolutionary Algorithm Using Machine Learning Techniques for Pattern Mining in Transactional Database

2020 ◽  
pp. 173-200
Author(s):  
Logeswaran K. ◽  
Suresh P. ◽  
Savitha S. ◽  
Prasanna Kumar K. R.
Keyword(s):  
Evolutionary Algorithm ◽  
Pattern Mining ◽  
Fitness Function ◽  
Search Space ◽  
Machine Learning Techniques ◽  
Dynamic Selection ◽  
Learning Techniques ◽  
Optimal Function ◽  
High Utility ◽  
Mining Algorithms

In recent years, the data analysts are facing many challenges in high utility itemset (HUI) mining from given transactional database using existing traditional techniques. The challenges in utility mining algorithms are exponentially growing search space and the minimum utility threshold appropriate to the given database. To overcome these challenges, evolutionary algorithm-based techniques can be used to mine the HUI from transactional database. However, testing each of the supporting functions in the optimization problem is very inefficient and it increases the time complexity of the algorithm. To overcome this drawback, reinforcement learning-based approach is proposed for improving the efficiency of the algorithm, and the most appropriate fitness function for evaluation can be selected automatically during execution of an algorithm. Furthermore, during the optimization process when distinct functions are skillful, dynamic selection of current optimal function is done.

scholarly journals Accelerating backtrack search with a best-first-search strategy

2014 ◽  
Vol 24 (4) ◽  
pp. 901-916
Author(s):  
Zoltán Ádám Mann ◽  
Tamás Szép
Keyword(s):  
Search Strategy ◽  
Search Algorithm ◽  
Search Space ◽  
New Approach ◽  
Backtrack Search ◽  
Speed Up ◽  
Hard Problems ◽  
Best First Search ◽  
Problem Instances ◽  
Np Hard Problems

Abstract Backtrack-style exhaustive search algorithms for NP-hard problems tend to have large variance in their runtime. This is because “fortunate” branching decisions can lead to finding a solution quickly, whereas “unfortunate” decisions in another run can lead the algorithm to a region of the search space with no solutions. In the literature, frequent restarting has been suggested as a means to overcome this problem. In this paper, we propose a more sophisticated approach: a best-firstsearch heuristic to quickly move between parts of the search space, always concentrating on the most promising region. We describe how this idea can be efficiently incorporated into a backtrack search algorithm, without sacrificing optimality. Moreover, we demonstrate empirically that, for hard solvable problem instances, the new approach provides significantly higher speed-up than frequent restarting.

scholarly journals An Automated Structural Optimisation Methodology for Scissor Structures Using a Genetic Algorithm

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Aushim Koumar ◽  
Tine Tysmans ◽  
Rajan Filomeno Coelho ◽  
Niels De Temmerman
Keyword(s):  
Genetic Algorithms ◽  
Search Space ◽  
Optimal Solutions ◽  
Multiobjective Optimisation ◽  
Nsga Ii ◽  
Design Variables ◽  
Depth Analysis ◽  
Making Choices ◽  
Optimisation Methods ◽  
Number Of Individuals

We developed a fully automated multiobjective optimisation framework using genetic algorithms to generate a range of optimal barrel vault scissor structures. Compared to other optimisation methods, genetic algorithms are more robust and efficient when dealing with multiobjective optimisation problems and provide a better view of the search space while reducing the chance to be stuck in a local minimum. The novelty of this work is the application and validation (using metrics) of genetic algorithms for the shape and size optimisation of scissor structures, which has not been done so far for two objectives. We tested the feasibility and capacity of the methodology by optimising a 6 m span barrel vault to weight and compactness and by obtaining optimal solutions in an efficient way using NSGA-II. This paper presents the framework and the results of the case study. The in-depth analysis of the influence of the optimisation variables on the results yields new insights which can help in making choices with regard to the design variables, the constraints, and the number of individuals and generations in order to obtain efficiently a trade-off of optimal solutions.

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