This article presents the use of a multi‐population distributed evolutionary algorithm for path planning in navigation situation. The algorithm used is with partially exchanged population and migration between independently evolving populations. In this paper a comparison between a multi‐population and a classic single‐population algorithm takes place. The impact on the ultimate solution has been researched. It was shown that using several independent populations leads to an improvement of the ultimate solution compared to a single population approach. The concept was checked against a problem of maritime collision avoidance
This research project demonstrated the effectiveness of using evolutionary software techniques in th...
Based on evolutionary computation (EC) concepts, we developed an adaptive Evolutionary Planner/Navi...
One of the main challenges when developing mobile robots is path planning. Efficient and robust algor...
Nowadays, parallel genetic algorithms are one of the most used meta-heuristics for solving combinato...
For a given circumstance, i.e., a collision situation at sea, a decision support system for navigati...
Path planning is the art of deciding which route to take, based on and expressed in terms of the cur...
Many distributed systems (task scheduling, moving priorities, changing mobile environments, ...) can...
The ubiquitous presence of distributed systems has drastically changed the way the world interacts, ...
Distributed systems are one of the most vital components of the economy. The most promi-nent example...
Route planning is a classical kind of problem that arises in different areas of knowledge, such as p...
This paper describes the use of evolutionary software techniques for developing both genetic algorit...
Although conventional multi-objective evolutionary optimization algorithms (MOEAs) are proven to be ...
For many years researchers and decision makers (DMs) faced with multicriteria shortest path problems...
The purpose of this thesis is to examine the ability of evolutionary algorithms (EAs) to develop nea...
In this paper we evaluates the effectiveness of three different distributed genetic algorithms (DGAs...
This research project demonstrated the effectiveness of using evolutionary software techniques in th...
Based on evolutionary computation (EC) concepts, we developed an adaptive Evolutionary Planner/Navi...
One of the main challenges when developing mobile robots is path planning. Efficient and robust algor...
Nowadays, parallel genetic algorithms are one of the most used meta-heuristics for solving combinato...
For a given circumstance, i.e., a collision situation at sea, a decision support system for navigati...
Path planning is the art of deciding which route to take, based on and expressed in terms of the cur...
Many distributed systems (task scheduling, moving priorities, changing mobile environments, ...) can...
The ubiquitous presence of distributed systems has drastically changed the way the world interacts, ...
Distributed systems are one of the most vital components of the economy. The most promi-nent example...
Route planning is a classical kind of problem that arises in different areas of knowledge, such as p...
This paper describes the use of evolutionary software techniques for developing both genetic algorit...
Although conventional multi-objective evolutionary optimization algorithms (MOEAs) are proven to be ...
For many years researchers and decision makers (DMs) faced with multicriteria shortest path problems...
The purpose of this thesis is to examine the ability of evolutionary algorithms (EAs) to develop nea...
In this paper we evaluates the effectiveness of three different distributed genetic algorithms (DGAs...
This research project demonstrated the effectiveness of using evolutionary software techniques in th...
Based on evolutionary computation (EC) concepts, we developed an adaptive Evolutionary Planner/Navi...
One of the main challenges when developing mobile robots is path planning. Efficient and robust algor...