A Hybrid GABFO Scheduling for Optimal Makespan in Computational Grid

2014 ◽  
Vol 5 (3) ◽  
pp. 57-83 ◽  
Author(s):  
Shiv Prakash ◽  
Deo Prakash Vidyarthi
Keyword(s):  
Optimization Problem ◽  
Hybrid Approach ◽  
Optimal Solution ◽  
Characteristic Parameter ◽  
Search Space ◽  
Computational Grid ◽  
Complex Task ◽  
Proposed Model ◽  
Evolutionary Technique ◽  
Complex Optimization

Scheduling in Computational Grid (CG) is an important but complex task. It is done to schedule the submitted jobs onto the nodes of the grid so that some characteristic parameter is optimized. Makespan of the job is an important parameter and most often scheduling is done to optimize makespan. Genetic Algorithm (GA) is a search procedure based on the evolutionary technique that is able to solve a class of complex optimization problem. However, GA takes longer to converge towards its near optimal solution. Bacteria Foraging Optimization (BFO), also derived from nature, is a technique to optimize a given function in a distributed manner. Due to limited availability of bacteria, BFO is not suitable to optimize the solution for the problem involving a large search space. Characteristics of both GA and BFO are combined so that their benefits can be reaped. The hybrid approach is referred to as Genetic Algorithms Bacteria Foraging Optimization (GABFO) algorithm. The proposed GABFO has been applied to optimize makespan of a given schedule in a computational grid. Results of the simulation, conducted to evaluate the performance of the proposed model, reveal the effectiveness of the proposed model.

Nesting Arbitrary Shapes Using Geometric Mating

2002 ◽  
Vol 2 (3) ◽  
pp. 171-178
Author(s):  
Chan Yu ◽  
Souran Manoochehri
Keyword(s):  
Optimization Problem ◽  
Optimal Solution ◽  
Computation Time ◽  
Search Space ◽  
Optimization Method ◽  
The Other ◽  
Reasonable Time ◽  
Common Features ◽  
Object Relative ◽  
Arbitrary Shapes

A genetic algorithm-based optimization method is proposed for solving the problem of nesting arbitrary shapes. Depending on the number of objects and the size of the search space, realizing an optimal solution within a reasonable time may not be possible. In this paper, a mating concept is introduced to reduce the solution time. Mating between two objects is defined as the positioning of one object relative to the other by merging common features that are assigned by the mating condition between them. A constrained move set is derived from a mating condition that allows the transformation of the object in each mating pair to be fully constrained with respect to the other. Properly mated objects can be placed together, thus reducing the overall computation time. Several examples are presented to demonstrate the efficiency of utilizing the mating concept to solve a nesting optimization problem.

A Particle Swarm Optimizer Based Approach for Aircraft Parking Position Assignment

2015 ◽  
Vol 713-715 ◽  
pp. 1525-1529
Author(s):  
Di Ming Ai ◽  
Yu Fei Jia ◽  
Jun Yan Zhao ◽  
Ling Jie Kong
Keyword(s):  
Numerical Experiment ◽  
Assignment Problem ◽  
Optimization Problem ◽  
Particle Swarm ◽  
Optimization Strategy ◽  
Particle Swarm Optimizer ◽  
Assignment Model ◽  
Proposed Model ◽  
Complex Optimization

A aircraft parking position assignment problem is a complex optimization problem with many constrains. In this paper, an assignment model is built with the consideration of constrain treatment. A 2 neighborhood particle swarm optimizer is adopted. The numerical experiment results demonstrate the effectiveness of proposed model and optimization strategy.

scholarly journals An Optimally Configured HP-GRU Model Using Hyperband for the Control of Wall Following Robot

2021 ◽  
Vol 1 (1) ◽  
pp. 66-74
Author(s):  
Abdul Rehman Khan ◽  
Ameer Tamoor Khan ◽  
Masood Salik ◽  
Sunila Bakhsh
Keyword(s):  
Optimization Problem ◽  
Sequence Data ◽  
Optimal Solution ◽  
Optimal Parameters ◽  
Control Framework ◽  
Complex Optimization ◽  
To Come ◽  
Gated Recurrent Unit ◽  
Wall Following ◽  
Selection Of

In this paper, we presented an autonomous control framework for the wall following robot using an optimally configured Gated Recurrent Unit (GRU) model with the hyperband algorithm. GRU is popularly known for the time-series or sequence data, and it overcomes the vanishing gradient problem of RNN. GRU also consumes less memory and is computationally more efficient than LSTMs. The selection of hyper-parameters of the GRU model is a complex optimization problem with local minima. Usually, hyper-parameters are selected through hit and trial, which does not guarantee an optimal solution. To come around this problem, we used a hyperband algorithm for the selection of optimal parameters. It is an iterative method, which searches for the optimal configuration by discarding the least performing configurations on each iteration. The proposed HP-GRU model is used on a dataset of SCITOS G5 robots with 24 sensors mounted. The results show that HP-GRU has a mean accuracy of 0.9857 and a mean loss of 0.0810, and it is comparable with other deep learning algorithms.

LOG FILE TEMPLATE DETECTION AS A MULTI-OBJECTIVE OPTIMIZATION PROBLEM

2022 ◽  
Vol 13 (1) ◽  
pp. 0-0
Keyword(s):  
Optimization Problem ◽  
Optimal Solution ◽  
Search Space ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Detection Tool ◽  
Detection Approach ◽  
Optimal Solution Set ◽  
Log File ◽  
Chicken Swarm Optimization

There is a need for automatic log file template detection tool to find out all the log messages through search space. On the other hand, the template detection tool should cope with two constraints: (i) it could not be too general and (ii) it could not be too specific These constraints are, contradict to one another and can be considered as a multi-objective optimization problem. Thus, a novel multi-objective optimization based log-file template detection approach named LTD-MO is proposed in this paper. It uses a new multi-objective based swarm intelligence algorithm called chicken swarm optimization for solving the hard optimization issue. Moreover, it analyzes all templates in the search space and selects a Pareto front optimal solution set for multi-objective compensation. The proposed approach is implemented and evaluated on eight publicly available benchmark log datasets. The empirical analysis shows LTD-MO detects large number of appropriate templates by significantly outperforming the existing techniques on all datasets.

scholarly journals Containerships Sailing Speed and Fleet Deployment Optimization under a Time-Based Differentiated Freight Rate Strategy

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Hualong Yang ◽  
Yuwei Xing
Keyword(s):  
Optimization Problem ◽  
Optimal Solution ◽  
Freight Rate ◽  
Liner Shipping ◽  
Mixed Integer ◽  
Time Value ◽  
Proposed Model ◽  
Discretization Algorithm ◽  
Deployment Optimization ◽  
Fleet Deployment

This paper investigates the problem of containership sailing speed and fleet deployment optimization in an intercontinental liner shipping network. Under the consideration of the time value of container cargo, three kinds of impact of sailing speed changes on long legs of each liner route are analysed, and a time-based freight rate strategy is proposed. Then, the optimization problem is formulated as a mixed-integer nonlinear programming. Its goal is to maximize the total profits of a container liner shipping. To find the optimal solution to the model and improve the efficiency of model solution, a discretization algorithm is proposed. Numerical results verify the applicability of the proposed model and the efficiency of the algorithm. In addition, the time-based freight rate strategy is able to achieve more profit compared to a fixed freight rate strategy.

scholarly journals Learning programs for the quantum computer

2021 ◽  
Author(s):  
Olivia-Linda Enciu
Keyword(s):  
Genetic Programming ◽  
Optimization Problem ◽  
Quantum Computer ◽  
Optimal Solution ◽  
Programming Approach ◽  
Quantum Computers ◽  
Learning Programs ◽  
Estimation Of Distribution ◽  
Evolutionary Technique ◽  
Quantum Programming

Manual quantum programming is generally diffcult for humans, due to the often hard-to-grasp properties of quantum mechanics and quantum computers. By outlining the target (or desired) behaviour of a particular quantum program, the task of programming can be turned into a search and optimization problem. A flexible evolutionary technique known as genetic programming may then be used as an aid in the search for quantum programs. In this work a genetic programming approach uses an estimation of distribution algorithm (EDA) to learn the probability distribution of optimal solution(s), given some target behaviour of a quantum program.

scholarly journals Learning programs for the quantum computer

2021 ◽  
Author(s):  
Olivia-Linda Enciu
Keyword(s):  
Genetic Programming ◽  
Optimization Problem ◽  
Quantum Computer ◽  
Optimal Solution ◽  
Programming Approach ◽  
Quantum Computers ◽  
Learning Programs ◽  
Estimation Of Distribution ◽  
Evolutionary Technique ◽  
Quantum Programming

Manual quantum programming is generally diffcult for humans, due to the often hard-to-grasp properties of quantum mechanics and quantum computers. By outlining the target (or desired) behaviour of a particular quantum program, the task of programming can be turned into a search and optimization problem. A flexible evolutionary technique known as genetic programming may then be used as an aid in the search for quantum programs. In this work a genetic programming approach uses an estimation of distribution algorithm (EDA) to learn the probability distribution of optimal solution(s), given some target behaviour of a quantum program.

scholarly journals Impact of Trapezoidal Demand and Deteriorating Preventing Technology in an Inventory Model in Interval Uncertainty under Backlogging Situation

Mathematics ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 78
Author(s):  
Rajan Mondal ◽  
Ali Akbar Shaikh ◽  
Asoke Kumar Bhunia ◽  
Ibrahim M. Hezam ◽  
Ripon K. Chakrabortty
Keyword(s):  
Inventory Management ◽  
Optimization Problem ◽  
Mathematical Formulation ◽  
Optimal Solution ◽  
Inventory Model ◽  
Interval Uncertainty ◽  
Swarm Optimization ◽  
Preservation Technology ◽  
Proposed Model ◽  
The Impact

The demand for a product is one of the important components of inventory management. In most cases, it is not constant; it may vary from time to time depending upon several factors which cannot be ignored. For any seasonal product, it is observed that at the beginning of the season, demand escalates over time, then it is stable and after that, it decreases. This type of demand is known as the trapezoidal type. Also, due to the uncertainty of customers’ behavior, inventory parameters are not always fixed. Combining these two concepts together, an inventory model is formulated for decaying items in an interval environment. Preservative technology is incorporated to preserve the product from deterioration. The corresponding mathematical formulation is derived in such a way that the profit of the inventory system is maximized. Consequently, the corresponding optimization problem is converted into an interval optimization problem. To solve the same, different variants of quantum-behaved particle swarm optimization (QPSO) techniques are employed to determine the duration of stock-in time and preservation technology cost. To illustrate and also to validate the model, three numerical examples are considered and solved. Then the computational results are compared. Thereafter, to study the impact of different parameters of the proposed model on the best found (optimal or very close to optimal) solution, sensitivity analysis are performed graphically.

LOG FILE TEMPLATE DETECTION AS A MULTI-OBJECTIVE OPTIMIZATION PROBLEM

2022 ◽  
Vol 13 (1) ◽  
pp. 0-0
Keyword(s):  
Optimization Problem ◽  
Optimal Solution ◽  
Search Space ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Detection Tool ◽  
Detection Approach ◽  
Optimal Solution Set ◽  
Log File ◽  
Chicken Swarm Optimization

There is a need for automatic log file template detection tool to find out all the log messages through search space. On the other hand, the template detection tool should cope with two constraints: (i) it could not be too general and (ii) it could not be too specific These constraints are, contradict to one another and can be considered as a multi-objective optimization problem. Thus, a novel multi-objective optimization based log-file template detection approach named LTD-MO is proposed in this paper. It uses a new multi-objective based swarm intelligence algorithm called chicken swarm optimization for solving the hard optimization issue. Moreover, it analyzes all templates in the search space and selects a Pareto front optimal solution set for multi-objective compensation. The proposed approach is implemented and evaluated on eight publicly available benchmark log datasets. The empirical analysis shows LTD-MO detects large number of appropriate templates by significantly outperforming the existing techniques on all datasets.

Observations on Effect of IPC in GA Based Scheduling on Computational Grid

2012 ◽  
Vol 4 (1) ◽  
pp. 67-80 ◽  
Author(s):  
Shiv Prakash ◽  
Deo P. Vidyarthi
Keyword(s):  
Optimization Problems ◽  
Scheduling Algorithm ◽  
Arrival Rate ◽  
Computational Grid ◽  
Process Communication ◽  
Model Studies ◽  
Proposed Model ◽  
Distributed Platform ◽  
Complex Optimization ◽  
Inter Process Communication

Computational Grid (CG) provides a wide distributed platform for high end compute intensive applications. Inter Process Communication (IPC) affects the performance of a scheduling algorithm drastically. Genetic Algorithms (GA), a search procedure based on the evolutionary computation, is able to solve a class of complex optimization problems. This paper proposes a GA based scheduling model observing the effect of IPC on the performance of scheduling in computational grid. The proposed model studies the effects of Inter Process Communication (IPC), processing rate () and arrival rate (). Simulation experiment, to evaluate the performance of the proposed algorithm is conducted and results reveal the effectiveness of the model.

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