Metaheuristic Approaches to Task Consolidation Problem in the Cloud

2017 ◽  
pp. 168-189 ◽  
Author(s):  
Sambit Kumar Mishra ◽  
Bibhudatta Sahoo ◽  
Kshira Sagar Sahoo ◽  
Sanjay Kumar Jena
Keyword(s):  
Combinatorial Optimization ◽  
Distributed Computing ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Bat Algorithm ◽  
Allocation Problem ◽  
Service Allocation ◽  
Multidimensional Knapsack ◽  
Np Hard Problem ◽  
Metaheuristic Techniques

The service (task) allocation problem in the distributed computing is one form of multidimensional knapsack problem which is one of the best examples of the combinatorial optimization problem. Nature-inspired techniques represent powerful mechanisms for addressing a large number of combinatorial optimization problems. Computation of getting an optimal solution for various industrial and scientific problems is usually intractable. The service request allocation problem in distributed computing belongs to a particular group of problems, i.e., NP-hard problem. The major portion of this chapter constitutes a survey of various mechanisms for service allocation problem with the availability of different cloud computing architecture. Here, there is a brief discussion towards the implementation issues of various metaheuristic techniques like Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Ant Colony Optimization (ACO), BAT algorithm, etc. with various environments for the service allocation problem in the cloud.

scholarly journals SOLVING INTEGER PROGRAMMING PROBLEMS BY USING POPULATION-BASED BEETLE ANTENNAE SEARCH ALGORITHM

2021 ◽  
Vol 9 (3-4) ◽  
pp. 89-99
Author(s):  
Ivona Brajević ◽  
Miodrag Brzaković ◽  
Goran Jocić
Keyword(s):  
Integer Programming ◽  
Optimization Problems ◽  
Mechanical Design ◽  
Search Algorithm ◽  
Optimal Solution ◽  
Search Space ◽  
Population Based ◽  
Local Optimum ◽  
Integer Programming Problems ◽  
Metaheuristic Techniques

Beetle antennae search (BAS) algorithm is a newly proposed single-solution based metaheuristic technique inspired by the beetle preying process. Although BAS algorithm has shown good search abilities, it can be easily trapped into local optimum when it is used to solve hard optimization problems. With the intention to overcome this drawback, this paper presents a population-based beetle antennae search (PBAS) algorithm for solving integer programming problems.  This method employs the population's capability to search diverse regions of the search space to provide better guarantee for finding the optimal solution. The PBAS method was tested on nine integer programming problems and one mechanical design problem. The proposed algorithm was compared to other state-of-the-art metaheuristic techniques. The comparisons show that the proposed PBAS algorithm produces better results for majority of tested problems.  

scholarly journals Local Search with a SAT Oracle for Combinatorial Optimization

2021 ◽  
pp. 87-104
Author(s):  
Aviad Cohen ◽  
Alexander Nadel ◽  
Vadim Ryvchin
Keyword(s):  
Combinatorial Optimization ◽  
Local Search ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Hill Climbing ◽  
Variable Neighbourhood Search ◽  
Np Hard ◽  
Neighbourhood Search ◽  
Sat Solver ◽  
Cell Placement

AbstractNP-hard combinatorial optimization problems are pivotal in science and business. There exists a variety of approaches for solving such problems, but for problems with complex constraints and objective functions, local search algorithms scale the best. Such algorithms usually assume that finding a non-optimal solution with no other requirements is easy. However, what if it is NP-hard? In such case, a SAT solver can be used for finding the initial solution, but how can one continue solving the optimization problem? We offer a generic methodology, called Local Search with SAT Oracle (), to solve such problems. facilitates implementation of advanced local search methods, such as variable neighbourhood search, hill climbing and iterated local search, while using a SAT solver as an oracle. We have successfully applied our approach to solve a critical industrial problem of cell placement and productized our solution at Intel.

Multiobjective Optimization of Multiloop Fractional Order PID Controller Tuned Using Bat Algorithm for Two Interacting Conical Tank Process

2014 ◽  
Vol 704 ◽  
pp. 373-379
Author(s):  
S.K. Lakshmanaprabu ◽  
U. Sabura Banu
Keyword(s):  
Fractional Order ◽  
Pid Controller ◽  
Controller Design ◽  
Optimization Problems ◽  
Linear Optimization ◽  
Optimal Solution ◽  
Bat Algorithm ◽  
Absolute Error ◽  
Fractional Order Pid ◽  
Fractional Order Pid Controller

Multiloop fractional order PID controller is tuned using Bat algorithm for two interacting conical tank process. Two interacting conical tank process is modelled using mass balance equations. Two Interacting Conical Tank process is a complex system involving tedious interaction. Straight forward multiloop PID controller design involves various steps to design the controller. Due to easy implementation and quick convergence, Bat algorithm is used in recent past for solving continuous non-linear optimization problems to achieve global optimal solution. Bat algorithm, a swarm intelligence technique will be attempted to tune the multiloop fractional order PID controller for two interacting conical tank process. The multi objective optimized multiloop fractional PID controller is tested for tracking, disturbance rejection for minimum Integral time absolute error.

A Linear Time Solution for N-Queens Problem Using Generalized Networks of Evolutionary Polarized Processors

2020 ◽  
Vol 31 (01) ◽  
pp. 7-21 ◽  
Author(s):  
Fernando Arroyo Montoro ◽  
Sandra Gómez-Canaval ◽  
Karina Jiménez Vega ◽  
Alfonso Ortega de la Puente
Keyword(s):  
Combinatorial Optimization ◽  
Numerical Evaluation ◽  
Optimization Problems ◽  
Linear Time ◽  
Combinatorial Optimization Problems ◽  
New Variant ◽  
Generalized Networks ◽  
Relevant Role ◽  
Np Hard Problem ◽  
Hard Problems

In this paper we consider a new variant of Networks of Polarized Evolutionary Processors (NPEP) named Generalized Networks of Evolutionary Polarized Processors (GNPEP) and propose them as solvers of combinatorial optimization problems. Unlike the NPEP model, GNPEP uses its numerical evaluation over the processed data from a quantitative perspective, hence this model might be more suitable to solve specific hard problems in a more efficient and economic way. In particular, we propose a GNPEP network to solve a well-known NP-hard problem, namely the [Formula: see text]-queens. We prove that this GNPEP algorithm requires a linear time in the size of a given instance. This result suggests that the GNPEP model is more suitable to address problems related to combinatorial optimization in which integer restrictions have a relevant role.

scholarly journals UAV Task Allocation Based on Clone Selection Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Xiaopan Zhang ◽  
Xingjun Chen
Keyword(s):  
Task Allocation ◽  
Large Scale ◽  
Optimal Solution ◽  
Allocation Problem ◽  
Network Technology ◽  
Selection Algorithm ◽  
Intelligent Optimization ◽  
Optimization Control ◽  
Np Hard Problem ◽  
Multi Uav

With the continuous development of computer and network technology, the large-scale and clustered operations of drones have gradually become a reality. How to realize the reasonable allocation of UAV cluster combat tasks and realize the intelligent optimization control of UAV cluster is one of the most challenging difficulties in UAV cluster combat. Solving the task allocation problem and finding the optimal solution have been proven to be an NP-hard problem. This paper proposes a CSA-based approach to simultaneously optimize four objectives in multi-UAV task allocation, i.e., maximizing the number of successfully allocated tasks, maximizing the benefits of executing tasks, minimizing resource costs, and minimizing time costs. Experimental results show that, compared with the genetic algorithm, the proposed method has better performance on solving the UAV task allocation problem with multiple objectives.

scholarly journals A Compact Bat Algorithm for Unequal Clustering in Wireless Sensor Networks

2019 ◽  
Vol 9 (10) ◽  
pp. 1973 ◽  
Author(s):  
Trong-The Nguyen ◽  
Jeng-Shyang Pan ◽  
Thi-Kien Dao
Keyword(s):  
Wireless Sensor Networks ◽  
Wireless Sensor Network ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Solution Space ◽  
Bat Algorithm ◽  
Wireless Sensor ◽  
Unequal Clustering ◽  
Large Solution ◽  
Limited Memory

Everyday, a large number of complex scientific and industrial problems involve finding an optimal solution in a large solution space. A challenging task for several optimizations is not only the combinatorial operation but also the constraints of available devices. This paper proposes a novel optimization algorithm, namely the compact bat algorithm (cBA), to use for the class of optimization problems involving devices which have limited hardware resources. A real-valued prototype vector is used for the probabilistic operations to generate each candidate for the solution of the optimization of the cBA. The proposed cBA is extensively evaluated on several continuous multimodal functions as well as the unequal clustering of wireless sensor network (uWSN) problems. Experimental results demonstrate that the proposed algorithm achieves an effective way to use limited memory devices and provides competitive results.

scholarly journals Structural Damage Identification Using a Modified Directional Bat Algorithm

2021 ◽  
Vol 11 (14) ◽  
pp. 6507
Author(s):  
Yonghui Su ◽  
Lijun Liu ◽  
Ying Lei
Keyword(s):  
Structural Damage ◽  
Damage Identification ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Bat Algorithm ◽  
Maximum Relative Error ◽  
Accurate Identification ◽  
Population Number ◽  
Structural Damage Identification ◽  
Beam Type

Bat algorithm (BA) has been widely used to solve optimization problems in different fields. However, there are still some shortcomings of standard BA, such as premature convergence and lack of diversity. To solve this problem, a modified directional bat algorithm (MDBA) is proposed in this paper. Based on the directional bat algorithm (DBA), the individual optimal updating mechanism is employed to update a bat’s position by using its own optimal solution. Then, an elimination strategy is introduced to increase the diversity of the population, in which individuals with poor fitness values are eliminated, and new individuals are randomly generated. The proposed algorithm is applied to the structural damage identification and to an objective function composed of the actual modal information and the calculated modal information. Finally, the proposed MDBA is used to solve the damage detection of a beam-type bridge and a truss-type bridge, and the results are compared with those of other swarm intelligence algorithms and other variants of BA. The results show that in the case of the same small population number and few iterations, MDBA has more accurate identification and better convergence than other algorithms. Moreover, the study on anti-noise performance of the MDBA shows that the maximum relative error is only 5.64% at 5% noise level in the beam-type bridge, and 6.53% at 3% noise in the truss-type bridge, which shows good robustness.

scholarly journals Solving integer programming problems by using population-based beetle antennae search algorithm

2021 ◽  
Vol 9 (3-4) ◽  
pp. 89-99
Author(s):  
Ivona Brajević ◽  
Miodrag Brzaković ◽  
Goran Jocić
Keyword(s):  
Integer Programming ◽  
Optimization Problems ◽  
Mechanical Design ◽  
Search Algorithm ◽  
Optimal Solution ◽  
Search Space ◽  
Population Based ◽  
Local Optimum ◽  
Integer Programming Problems ◽  
Metaheuristic Techniques

Beetle antennae search (BAS) algorithm is a newly proposed single-solution based metaheuristic technique inspired by the beetle preying process. Although BAS algorithm has shown good search abilities, it can be easily trapped into local optimum when it is used to solve hard optimization problems. With the intention to overcome this drawback, this paper presents a population-based beetle antennae search (PBAS) algorithm for solving integer programming problems. This method employs the population's capability to search diverse regions of the search space to provide better guarantee for finding the optimal solution. The PBAS method was tested on nine integer programming problems and one mechanical design problem. The proposed algorithm was compared to other state-of-the-art metaheuristic techniques. The comparisons show that the proposed PBAS algorithm produces better results for majority of tested problems.

scholarly journals An Interactive Regret-Based Genetic Algorithm for Solving Multi-Objective Combinatorial Optimization Problems

2020 ◽  
Vol 34 (03) ◽  
pp. 2335-2342
Author(s):  
Nawal Benabbou ◽  
Cassandre Leroy ◽  
Thibaut Lust
Keyword(s):  
Combinatorial Optimization ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Computation Time ◽  
Preference Elicitation ◽  
Combinatorial Optimization Problem ◽  
New Approach ◽  
Multi Objective ◽  
Combinatorial Optimization Problems

We propose a new approach consisting in combining genetic algorithms and regret-based incremental preference elicitation for solving multi-objective combinatorial optimization problems with unknown preferences. For the purpose of elicitation, we assume that the decision maker's preferences can be represented by a parameterized scalarizing function but the parameters are initially not known. Instead, the parameter imprecision is progressively reduced by asking preference queries to the decision maker during the search to help identify the best solutions within a population. Our algorithm, called RIGA, can be applied to any multi-objective combinatorial optimization problem provided that the scalarizing function is linear in its parameters and that a (near-)optimal solution can be efficiently determined when preferences are known. Moreover, RIGA runs in polynomial time while asking no more than a polynomial number of queries. For the multi-objective traveling salesman problem, we provide numerical results showing its practical efficiency in terms of number of queries, computation time and gap to optimality.

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