scholarly journals APPLICATION OF THE GRAVITY SEARCH METHOD TO MINIMIZE THE COST OF CONDUCTING A MULTIFACTOR EXPERIMENT

2021 ◽  
pp. 42-48
Author(s):  
N.D. Koshevoy ◽  
A.V. Malkova
Keyword(s):  
Experimental Research ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Optimization Method ◽  
Search Method ◽  
Main Task ◽  
Multifactor Experiment ◽  
Gravitational Search ◽  
The Cost

Experimental research methods are increasingly used in industry in the optimization of production processes. Experiments, as a rule, are multifactorial and are connected with optimization of quality of materials, search of optimum conditions of carrying out technological processes, development of the most rational designs of the equipment, etc. The use of experimental planning makes the behavior of the experimenter purposeful and organized, significantly increases productivity and reliability of the results. An important advantage is its versatility, suitability in the vast majority of research areas. When implementing an industrial experiment, the main task is to obtain the maximum amount of useful information about the influence of individual factors of the production process, provided that the minimum number of expensive observations in the shortest period of time. Therefore, it is important to increase the efficiency of experimental research with minimal time and cost. For this purpose, it is expedient to develop systems of automation of experiments which will allow to reduce terms of carrying out experimental researches and to reduce expenses for them. Object of research: processes of optimization of plans of multifactor experiment on cost and time expenses. Subject of research: an optimization method developed on the basis of the gravitational search algorithm, which consists in comparing the force of gravity (cost) of the first row of the planning matrix of the experiment to the next rows of the matrix. In the study of photoelectric transducers of angular displacements, the efficiency and effectiveness of the gravitational search method were analyzed in comparison with previously developed methods: analysis of line permutations, particle swarm, taboo search. The cost of carrying out the experiment plan and the efficiency for solving optimization problems in comparison with the original plan and the implementation of the above methods are shown.

scholarly journals APPLICATION OF THE GRAVITY SEARCH METHOD TO MINIMIZE THE COST OF CONDUCTING A MULTIFACTOR EXPERIMENT

2020 ◽  
pp. 51-57
Author(s):  
N.D. Koshevoy ◽  
A.V. Malkova
Keyword(s):  
Experimental Research ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Optimization Method ◽  
Search Method ◽  
Main Task ◽  
Multifactor Experiment ◽  
Gravitational Search ◽  
The Cost

Experimental research methods are increasingly used in industry in the optimization of production processes. Experiments, as a rule, are multifactorial and are connected with optimization of quality of materials, search of optimum conditions of carrying out technological processes, development of the most rational designs of the equipment, etc. The use of experimental planning makes the behavior of the experimenter purposeful and organized, significantly increases productivity and reliability of the results. An important advantage is its versatility, suitability in the vast majority of research areas. When implementing an industrial experiment, the main task is to obtain the maximum amount of useful information about the influence of individual factors of the production process, provided that the minimum number of expensive observations in the shortest period of time. Therefore, it is important to increase the efficiency of experimental research with minimal time and cost. For this purpose, it is expedient to develop systems of automation of experiments which will allow to reduce terms of carrying out experimental researches and to reduce expenses for them. Object of research: processes of optimization of plans of multifactor experiment on cost and time expenses. Subject of research: an optimization method developed on the basis of the gravitational search algorithm, which consists in comparing the force of gravity (cost) of the first row of the planning matrix of the experiment to the next rows of the matrix. In the study of photoelectric transducers of angular displacements, the efficiency and effectiveness of the gravitational search method were analyzed in comparison with previously developed methods: analysis of line permutations, particle swarm, taboo search. The cost of carrying out the experiment plan and the efficiency for solving optimization problems in comparison with the original plan and the implementation of the above methods are shown.

Automatic Mining of Quantitative Association Rules with Gravitational Search Algorithm

2017 ◽  
Vol 27 (03) ◽  
pp. 343-372 ◽  
Author(s):  
Umit Can ◽  
Bilal Alatas
Keyword(s):  
Association Rules ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Fitness Function ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Search Space ◽  
Search Method ◽  
Quantitative Association Rules ◽  
Gravitational Search

The classical optimization algorithms are not efficient in solving complex search and optimization problems. Thus, some heuristic optimization algorithms have been proposed. In this paper, exploration of association rules within numerical databases with Gravitational Search Algorithm (GSA) has been firstly performed. GSA has been designed as search method for quantitative association rules from the databases which can be regarded as search space. Furthermore, determining the minimum values of confidence and support for every database which is a hard job has been eliminated by GSA. Apart from this, the fitness function used for GSA is very flexible. According to the interested problem, some parameters can be removed from or added to the fitness function. The range values of the attributes have been automatically adjusted during the time of mining of the rules. That is why there is not any requirements for the pre-processing of the data. Attributes interaction problem has also been eliminated with the designed GSA. GSA has been tested with four real databases and promising results have been obtained. GSA seems an effective search method for complex numerical sequential patterns mining, numerical classification rules mining, and clustering rules mining tasks of data mining.

A Strength Pareto Gravitational Search Algorithm for Multi-Objective Optimization Problems

2015 ◽  
Vol 29 (06) ◽  
pp. 1559010 ◽  
Author(s):  
Xiaohui Yuan ◽  
Zhihuan Chen ◽  
Yanbin Yuan ◽  
Yuehua Huang ◽  
Xiaopan Zhang
Keyword(s):  
Optimization Problems ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Population Diversity ◽  
Benchmark Problems ◽  
Multi Objective Optimization ◽  
Local Optima ◽  
Multi Objective ◽  
Fine Grained ◽  
Gravitational Search

A novel strength Pareto gravitational search algorithm (SPGSA) is proposed to solve multi-objective optimization problems. This SPGSA algorithm utilizes the strength Pareto concept to assign the fitness values for agents and uses a fine-grained elitism selection mechanism to keep the population diversity. Furthermore, the recombination operators are modeled in this approach to decrease the possibility of trapping in local optima. Experiments are conducted on a series of benchmark problems that are characterized by difficulties in local optimality, nonuniformity, and nonconvexity. The results show that the proposed SPGSA algorithm performs better in comparison with other related works. On the other hand, the effectiveness of two subtle means added to the GSA are verified, i.e. the fine-grained elitism selection and the use of SBX and PMO operators. Simulation results show that these measures not only improve the convergence ability of original GSA, but also preserve the population diversity adequately, which enables the SPGSA algorithm to have an excellent ability that keeps a desirable balance between the exploitation and exploration so as to accelerate the convergence speed to the true Pareto-optimal front.

scholarly journals A Memetic Chaotic Gravitational Search Algorithm for unconstrained global optimization problems

2019 ◽  
Vol 79 ◽  
pp. 14-29 ◽  
Author(s):  
Ricardo García-Ródenas ◽  
Luis Jiménez Linares ◽  
Julio Alberto López-Gómez
Keyword(s):  
Global Optimization ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Gravitational Search

scholarly journals Multi-Objective Operation of Cascade Hydropower Reservoirs Using TOPSIS and Gravitational Search Algorithm with Opposition Learning and Mutation

Water ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 2040 ◽  
Author(s):  
Feng ◽  
Liu ◽  
Jiang ◽  
Luo ◽  
Miao
Keyword(s):  
Learning Strategy ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Optimization Method ◽  
Multi Objective ◽  
Practical Constraint ◽  
Hydropower System ◽  
Order Preference ◽  
Gravitational Search ◽  
The Individual

In this research, a novel enhanced gravitational search algorithm (EGSA) is proposed to resolve the multi-objective optimization model, considering the power generation of a hydropower enterprise and the peak operation requirement of a power system. In the proposed method, the standard gravity search algorithm (GSA) was chosen as the fundamental execution framework; the opposition learning strategy was adopted to increase the convergence speed of the swarm; the mutation search strategy was chosen to enhance the individual diversity; the elastic-ball modification strategy was used to promote the solution feasibility. Additionally, a practical constraint handling technique was introduced to improve the quality of the obtained agents, while the technique for order preference by similarity to an ideal solution method (TOPSIS) was used for the multi-objective decision. The numerical tests of twelve benchmark functions showed that the EGSA method could produce better results than several existing evolutionary algorithms. Then, the hydropower system located on the Wu River of China was chosen to test the engineering practicality of the proposed method. The results showed that the EGSA method could obtain satisfying scheduling schemes in different cases. Hence, an effective optimization method was provided for the multi-objective operation of hydropower system.

scholarly journals Weight Minimization of Helical Compression Spring Using Gravitational Search Algorithm (GSA)

2015 ◽  
Vol 773-774 ◽  
pp. 277-281 ◽  
Author(s):  
Noor Hafizah Amer ◽  
Nurhidayati Ahmad ◽  
Amar Faiz Zainal Abidin
Keyword(s):  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Pso Algorithm ◽  
Optimization Method ◽  
Standard Case ◽  
Weight Minimization ◽  
Compression Spring ◽  
Parametric Studies ◽  
Gravitational Search ◽  
Better Than

Compression spring is one of the most common mechanical componet being used in most mechanisms. Many criteria and constraints should be considered in designing and specifying the spring dimensions. Therefore, it has been one of the standard case studies considered to test a new optimisation algorithm. This paper introduced an optimization method named Gravitational search Algorithm (GSA) to solve the problem of weight minimization of spring. From previous studies, weight minimization of a spring has been investigated by many researcher using various optimization algorithm technique. The result of this study were compared to one of the previous studies using Particle Swarm Optimization (PSO) algorithm. Also, parametric studies were conducted to select the best values of GSA parameters, beta and epsilon. From the results obtained, it was observed that the optimum dimensions and weight obtained by GSA are better than the values obtained by PSO. The best values of beta and epsilon was found to be 0.6 and 0.01 respectively.

scholarly journals Gravitational Search Algorithm for NURBS Curve Fitting

2018 ◽  
Vol 3 (1) ◽  
pp. 33
Author(s):  
Erkan Ülker ◽  
İsmail Babaoğlu
Keyword(s):  
Curve Fitting ◽  
Computer Aided Design ◽  
High Performance ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Optimization Method ◽  
Simultaneous Optimization ◽  
Nurbs Curve ◽  
B Spline ◽  
Gravitational Search

By providing great flexibility non-uniform rational B-spline (NURBS) curves and surfaces are reason of preferability on areas like computer aided design, medical imaging and computer graphics. Knots, control points and weights provide this flexibility. Computation of these parameters makes the problem as a non-linear combinational optimization problem on a process of reverse engineering. The ability of solving these problems using meta-heuristics instead of conventional methods attracts researchers. In this paper, NURBS curve estimation is carried out by a novel optimization method namely gravitational search algorithm. Both knots and knots together weights simultaneous optimization process is implemented by using research agents. The high performance of the proposed method on NURBS curve fitting is showed by obtained results.Keywords: Non-uniform rational B-spline, gravitational search algorithm, meta-heuristic

scholarly journals Risk Response Selection in Construction Projects

2018 ◽  
Vol 3 (12) ◽  
pp. 1208 ◽  
Author(s):  
Hafeth I. Naji ◽  
Rouwaida Hussein Ali
Keyword(s):  
Risk Management ◽  
Optimal Strategy ◽  
Construction Projects ◽  
Response Selection ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Response Strategy ◽  
Risk Response ◽  
Gravitational Search ◽  
The Cost

Risk and its management  is  important  for the success of the project, the  risk management, which encompassed of planning, identification, analysis, and response has an important phase, which is risk response  and it should not be undermined, as its  success going to  the projects  the capability  to overcome the  uncertainty and  thus an effective  tool in project risk management, risk response used the collective information in the analysis stage and in order  to take decision how to improve the possibility to complete the project within time, cost and performance. This stage work on preparing the response to the main risks and appoint the people who are responsible for each response.  When it's needed risk response may be started in quantitative analysis stage and the repetition may be possible between the analysis and risk response stage. The aim of this research is to provide a methodology to make the plane for unexpected events and control uncertain situations and identify the reason for risk response failure and to respond to risk successfully by using the optimization method to select the best strategy. The methodology of this research divided into four parts, the first part main object is to find the projects whose risk response is failed, the second part includes the reasons for risk response Failure, the third part includes   finding   the most important risks generated from risk response that leads to increasing the cost of construction projects, the fourth part of the management system is selecting the optimal risk response strategy. An optimization model was used to select the optimal strategy to treat the risk by using Serval constraints such as the cost of the project, time of the project, Gravitational Search Algorithm and particle swarm used. The result of the risk response selection shows that The investment (contractor, bank) strategy shows a very good strategy as it saves the cost about 30%, while the Mitigate (pay for advances with interest 0. 1) Strategy show saving the cost 40%   and giving land to contractors show saving the cost 40% finally the BIM strategy show saving the cost 25%. The risk response is an important part and should give a great attention and it must be used sophisticated method to select the optimal strategy, the two techniques both show high efficiency in selecting the strategy but Gravitational Search Algorithm show better performance.

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