08-05-2017, 08:37 PM
MATLAB code for multi objective ant colony optimisation
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matlab coding for multi objective ant colony optimization
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11-05-2017, 09:53 AM
Multiple Objective Optimization is a fast growing research area, so several approaches to Optimization of Ant Colony have been proposed for a variety of these problems. In this work, a taxonomy is proposed for Multiple Object Ants Optimization algorithms and many existing approaches are reviewed and described using the taxonomy. The taxonomy provides guidelines for the development and use of algorithms for the optimization of Multiple Ants Colony Objective.
The multi-objective vendor (MOTSP) problem, as one of the typical multi-objective optimization (MOOP) problems, is an important field in operational research and networks. Networks form the backbone of many complex systems, ranging from the Internet to human societies. And network models have been widely used. Many of the real world problems, such as multi-objective network structure design problems and multi-objective vehicle routing problems, can be formulated as MOTSP. Establishing an effective approach to finding a set of solutions with a good balance between the different objectives of a MOTSP is of great practical importance. As a colony-based optimization approach, multi-objective ant colony optimization (MOACOs) can obtain a number of trade-off solutions in a single run. And MOACOs are suitable and have been widely applied to solve multi-objective optimization problems. In the last two decades, many investigations of MOACOs have been presented.
The multi-objective vendor (MOTSP) problem, as one of the typical multi-objective optimization (MOOP) problems, is an important field in operational research and networks. Networks form the backbone of many complex systems, ranging from the Internet to human societies. And network models have been widely used. Many of the real world problems, such as multi-objective network structure design problems and multi-objective vehicle routing problems, can be formulated as MOTSP. Establishing an effective approach to finding a set of solutions with a good balance between the different objectives of a MOTSP is of great practical importance. As a colony-based optimization approach, multi-objective ant colony optimization (MOACOs) can obtain a number of trade-off solutions in a single run. And MOACOs are suitable and have been widely applied to solve multi-objective optimization problems. In the last two decades, many investigations of MOACOs have been presented.
For example, the BicriterionAnt (BIANT) algorithm has been proposed to solve bi-criteria vehicle routing problems, Pareto Ange Colony Optimization (PACO) has been designed to solve the problems of multi-objective portfolio selection and MACS Proposed for Solve vehicle routing problems with time windows. García-Martínez et al. They have discussed a taxonomy of MOACOs according to the number of heuristic matrices and pheromone matrices. According to research by García-Martínez et al. , MOACOs have been used to carry out the MOTSPs and some guidelines have been proposed on how to design MOACOs. However, due to disturbance of optimal non-global paths, MOACOs often can not achieve a good compensation solution or fall into local optimal solutions.
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