scholarly journals Performance of four meta-heuristics to solve a forestry production planning problem

2020 ◽  
Vol 12 ◽  
pp. 1-5
Author(s):  
Emanuelly Canabrava Magalhães ◽  
Carlos Alberto Araújo Júnior ◽  
Francisco Conesa Roca ◽  
Mylla Vyctória Coutinho Sousa
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Net Present Value ◽  
Clonal Selection ◽  
Computational Effort ◽  
Neighborhood Search ◽  
Forest Planning ◽  
Planning Problem ◽  
Clonal Selection Algorithm ◽  
Forest Production

The use of artificial intelligence as a tool to aid in the planning of forest production has gained more and more space. Highlighting the metaheuristics, due to the ability to generate optimal solutions for a given optimization problem in a short time, without great computational effort. The present study aims to evaluate the performance of the metaheuristics Genetic Algorithm, Simulated Annealing, Variable Neighborhood Search and Clonal Selection Algorithm applied in a model of regulation of forest production. It was considered a planning horizon of 16 years, in which the model aims to maximize the Net Present Value (NPV), having as restrictions age of cut between 5 and 7 years and minimum and maximum logging demand of 140,000 and 160,000 m3, respectively. Different combinations of configurations were considered for each of the metaheuristics, 30-second processing time and 30 replicates for each configuration, all processing being performed in MeP - Metaheuristics for forest Planning software. The Simulated Annealing metaheuristic obtained the best results when compared to the others, reaching the minimum and maximum demand demanded in all tested configurations, in contrast, the Genetic Algorithm was the one with the worst performance. Thus, the capacity to use metaheuristics as a tool for forest planning is observed.

scholarly journals CAN LINEAR PROGRAMMING ASSIST METAHEURISTICS IN FOREST PRODUCTION PLANNING PROBLEM?

FLORESTA ◽  
2021 ◽  
Vol 51 (3) ◽  
pp. 751
Author(s):  
Carlos Alberto Araújo Júnior ◽  
Renato Vinícius Oliveira Castro ◽  
João Batista Mendes ◽  
Helio Garcia Leite
Keyword(s):  
Genetic Algorithm ◽  
Linear Programming ◽  
Variable Neighborhood Search ◽  
Clonal Selection ◽  
Neighborhood Search ◽  
Initial Population ◽  
Clonal Selection Algorithm ◽  
Selection Algorithm ◽  
Forest Production ◽  
Relaxed Problem

The planning of forest production requires the adoption of mathematical models to optimize the utilization of available resources. Hence, studies involving the improvement of decision-making processes must be performed. Herein, we evaluate an alternative method for improving the performance of metaheuristics when they are applied for identifying solutions to problems in forest production planning. The inclusion of a solution obtained by rounding the optimal solution of linear programming to a relaxed problem is investigated. Such a solution is included in the initial population of the clonal selection algorithm, genetic algorithm, simulated annealing, and variable neighborhood search metaheuristics when it is used to generate harvest and planting plans in an area measuring 4,210 ha comprising 120 management units with ages varying between 1 and 6 years. The same algorithms are executed without including the solutions mentioned in the initial population. Results show that the performance of the clonal selection algorithm, genetic algorithm, and variable neighborhood search algorithms improved significantly. Positive effects on the performance of the simulated annealing metaheuristic are not indicated. Hence, it is concluded that rounding off the solution to a relaxed problem is a good alternative for generating an initial solution for metaheuristics.

scholarly journals META-HEURISTIC CLONAL SELECTION ALGORITHM FOR OPTIMIZATION OF FOREST PLANNING

2018 ◽  
Vol 41 (6) ◽  
Author(s):  
Carlos Alberto Araújo Júnior ◽  
João Batista Mendes ◽  
Christian Dias Cabacinha ◽  
Adriana Leandra de Assis ◽  
Lisandra Maria Alves Matos ◽  
...  
Keyword(s):  
Linear Programming ◽  
Integer Linear Programming ◽  
Net Present Value ◽  
New Technologies ◽  
Clonal Selection ◽  
Forest Planning ◽  
Initial Population ◽  
Planning Problem ◽  
Clonal Selection Algorithm ◽  
Selection Algorithm

ABSTRACT It is important to evaluate the application of new technologies in the field of computational science to forest science. The goal of this study was to test a different kind of metaheuristic, namely Clonal Selection Algorithm, in a forest planning problem. In this problem, the total management area is 4.210 ha that is distributed in 120 stands in ages between 1 and 6 years and site indexes of 22 m to 31 m. The problem was modeled considering the maximization of the net present value subject to the constraints: annual harvested volume between 140,000 m3 and 160,000 m3, harvest ages equal to 5, 6 or 7 years, and the impossibility of division of the management unity at harvest time. Different settings for Clonal Selection Algorithm were evaluated to include: varying selection, cloning, hypermutation, and replacement rates beyond the size of the initial population. A generation value equal to 100 was considered as a stopping criteria and 30 repetitions were performed for each setting. The results were compared to those obtained from integer linear programming and linear programming. The integer linear programming, considered to be the best solution, was obtained after 1 hour of processing. The best setting for Clonal Selection Algorithm was 80 individuals in the initial population and selection. Cloning, hypermutation, and replacement rates equal to 0.20, 0.80, 0.20 and 0.50, respectively, were found. The results obtained by Clonal Selection Algorithm were 1.69% better than the integer linear programming and 4.35% worse than the linear programming. It is possible to conclude that the presented metaheuristic can be used in the resolution of forest scheduling problems.

scholarly journals TUNING OF THE METAHEURISTIC VARIABLE NEIGHBORHOOD SEARCH FOR A FOREST PLANNING PROBLEM

CERNE ◽  
2018 ◽  
Vol 24 (3) ◽  
pp. 259-268
Author(s):  
Carlos Alberto Araújo Júnior ◽  
João Batista Mendes ◽  
Adriana Leandra de Assis ◽  
Christian Dias Cabacinha ◽  
Jonathan James Stocks ◽  
...  
Keyword(s):  
Variable Neighborhood Search ◽  
Neighborhood Search ◽  
Forest Planning ◽  
Planning Problem

Efficient Power Control and Clustering Method Based on Hybrid Clonal Genetic Algorithm in Wireless Sensor Networks

2012 ◽  
Vol 263-266 ◽  
pp. 889-897
Author(s):  
Xiang Xian Zhu ◽  
Su Feng Lu
Keyword(s):  
Genetic Algorithm ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Large Scale ◽  
Clonal Selection ◽  
Base Station ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Clonal Selection Algorithm ◽  
Clustering Methods

Wireless sensor networks (WSNs) lifetime for large-scale surveillance systems is defined as the time span that all targets can be covered. How to manage the combination of the sensor nodes efficiently to prolong the whole network’s lifetime while insuring the network reliability, it is one of the most important problems to research in WSNs. An effective optimization framework is then proposed, where genetic algorithm and clonal selection algorithm are hybridized to enhance the searching ability. Our goal can be described as minimizing the number of active nodes and the scheduling cost, thus reducing the overall energy consumption to prolong the whole network’s lifetime with certain coverage rate insured. We compare the proposed algorithm with different clustering methods used in the WSNs. The simulation results show that the proposed algorithm has higher efficiency and can achieve better network lifetime and data delivery at the base station.

Evaluation of a simulated annealing metaheuristic to solve a forest planning problem

2013 ◽  
Vol 5 (4) ◽  
pp. 376
Author(s):  
Antonio Almeida De Barros Junior ◽  
Gustavo Willam Pereira ◽  
Geraldo Regis Mauri ◽  
Robson De Souza Melo
Keyword(s):  
Simulated Annealing ◽  
Forest Planning ◽  
Planning Problem
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