CAN LINEAR PROGRAMMING ASSIST METAHEURISTICS IN FOREST PRODUCTION PLANNING 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.

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A hybridization of clonal selection algorithm with iterated local search and variable neighborhood search for the feature selection problem

Memetic Computing ◽

10.1007/s12293-015-0161-2 ◽

2015 ◽

Vol 7 (3) ◽

pp. 181-201 ◽

Cited By ~ 9

Author(s):

Magdalene Marinaki ◽

Yannis Marinakis

Keyword(s):

Feature Selection ◽

Local Search ◽

Variable Neighborhood Search ◽

Clonal Selection ◽

Iterated Local Search ◽

Selection Problem ◽

Neighborhood Search ◽

Clonal Selection Algorithm ◽

Selection Algorithm ◽

Feature Selection Problem

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META-HEURISTIC CLONAL SELECTION ALGORITHM FOR OPTIMIZATION OF FOREST PLANNING

Revista Árvore ◽

10.1590/1806-90882017000600007 ◽

2018 ◽

Vol 41 (6) ◽

Cited By ~ 1

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.

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

Caderno de Ciências Agrárias ◽

10.35699/2447-6218.2020.15891 ◽

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.

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Modeling the Municipal Waste Collection Using Genetic Algorithms

Processes ◽

10.3390/pr8050513 ◽

2020 ◽

Vol 8 (5) ◽

pp. 513

Author(s):

Elisabete Alberdi ◽

Leire Urrutia ◽

Aitor Goti ◽

Aitor Oyarbide-Zubillaga

Keyword(s):

Genetic Algorithm ◽

Variable Neighborhood Search ◽

Real Data ◽

Municipal Waste ◽

Neighborhood Search ◽

Initial Population ◽

Waste Collection ◽

Reduction Of Emissions ◽

Apparent Reasons ◽

Unsolved Issue

Calculating adequate vehicle routes for collecting municipal waste is still an unsolved issue, even though many solutions for this process can be found in the literature. A gap still exists between academics and practitioners in the field. One of the apparent reasons why this rift exists is that academic tools often are not easy to handle and maintain by actual users. In this work, the problem of municipal waste collection is modeled using a simple but efficient and especially easy to maintain solution. Real data have been used, and it has been solved using a Genetic Algorithm (GA). Computations have been done in two different ways: using a complete random initial population, and including a seed in this initial population. In order to guarantee that the solution is efficient, the performance of the genetic algorithm has been compared with another well-performing algorithm, the Variable Neighborhood Search (VNS). Three problems of different sizes have been solved and, in all cases, a significant improvement has been obtained. A total reduction of 40% of itineraries is attained with the subsequent reduction of emissions and costs.

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