LAGA: A Software for Landscape Allocation using Genetic Algorithm

Authors

  • Abdolrassoul Salman Mahiny Associate Professor, Faculty of Fisheries and Environmental Sciences, Gorgan University of Agricultural Science & Natural Resource
  • Mohammad Shahraini Assistant Professor, Engineering & Technological Collage, Golestan University
Abstract:

In this paper, Landscape Allocation using Genetic Algorithm (LAGA), a spatial multi-objective land use optimization software is introduced. The software helps in searching for optimal land use when multiple objectives such as suitability, area, cohesion and edge density indices are simultaneously involved. LAGA is a flexible and easy to use genetic algorithm-based software for optimizing the spatial configurations of land use. LAGA uses a steady-state genetic algorithm with one-point crossover and flip-mutation as genetic operators. A major novelty is that spatial changes are performed according to patch topology that allows to simultaneously integrate changes of different landscape elements that improves the speed and performance. Another feature of this software is that exclusion areas (i.e.: cities, roads and water bodies) can also be locked or un-locked in the optimization process. The program reads and writes maps in Arc ASCII raster format, which is supported by many GIS (e.g. ArcGIS/ArcView, GRASS, and IDRISI). LAGA has been applied in a case study to find optimum land use in the Gorgan Township. The results suggest that LAGA can be a useful tool to land use planning.

Upgrade to premium to download articles

Sign up to access the full text

Already have an account?login

similar resources

laga: a software for landscape allocation using genetic algorithm

in this paper, landscape allocation using genetic algorithm (laga), a spatial multi-objective land use optimization software is introduced. the software helps in searching for optimal land use when multiple objectives such as suitability, area, cohesion and edge density indices are simultaneously involved. laga is a flexible and easy to use genetic algorithm-based software for optimizing the sp...

full text

A Memtic genetic algorithm for a redundancy allocation problem

Abstract In general redundancy allocation problems the redundancy strategy for each subsystem is predetermined. Tavakkoli- Moghaddam presented a series-parallel redundancy allocation problem with mixing components (RAPMC) in which the redundancy strategy can be chosen for individual subsystems. In this paper, we present a bi-objective redundancy allocation when the redundancy strategies for...

full text

Development of a Genetic Algorithm for Advertising Time Allocation Problems

Commercial advertising is the main source of income for TV channels and allocation of advertising time slots for maximizing broadcasting revenues is the major problem faced by TV channel planners. In this paper, the problem of scheduling advertisements on prime-time of a TV channel is considered. The problem is formulated as a multi-unit combinatorial auction based mathematical model. This is a...

full text

Testing Resource Allocation for Modular Software using Genetic Algorithm

Software testing is one of the important steps of SDLC. In software testing one of the important issues is how to allocate the limited resources so that we finish our testing on time and will deliver quality software. Number of Software Reliability Growth Models (SRGM) has been developed for allocating the testing resource in the past three decades but majority of models are developed in static...

full text

Practical Human Resource Allocation in Software Projects Using Genetic Algorithm

Software planning is becoming more complicated as the size of software project grows, making the planning process more important. Many approaches have been proposed to help software project managers by providing optimal human resource allocations in terms of minimizing the cost. Since previous approaches only concentrated on minimizing the cost, there has not been a study that considers the pra...

full text

Static Task Allocation in Distributed Systems Using Parallel Genetic Algorithm

Over the past two decades, PC speeds have increased from a few instructions per second to several million instructions per second. The tremendous speed of today's networks as well as the increasing need for high-performance systems has made researchers interested in parallel and distributed computing. The rapid growth of distributed systems has led to a variety of problems. Task allocation is a...

full text

My Resources

Save resource for easier access later

Save to my library Already added to my library

{@ msg_add @}


Journal title

volume 4  issue 2

pages  153- 166

publication date 2016-02-01

By following a journal you will be notified via email when a new issue of this journal is published.

Hosted on Doprax cloud platform doprax.com

copyright © 2015-2023