A Process Algebra Genetic Algorithm

Karaman, Sertaç; Shima, Tal

doi:10.1109/tevc.2011.2160400

Cited by 15 publications

(11 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Both activity rates and model structure make up the search space for the metaheuristic. Similarly Karaman et al use genetic algorithms to construct a process algebra model satisfying a path optimisation property, again focussing on the time series view of the process algebra model output [4]. In contrast our work identifies emergent global properties of the process algebra model as the goal.…”

Section: Related Workmentioning

confidence: 99%

Automated Capacity Planning for PEPA Models

Williams

Hillston

2014

Computer Performance Engineering

View full text Add to dashboard Cite

Abstract. Capacity planning is concerned with the provisioning of systems in order to ensure that they meet the demand or performance requirements of users. Currently for PEPA models, a modeller who wishes to solve a capacity planning problem has to either carry out a manual search for an optimal configuration or work outside the provided tool suite. We present a new extension to the Eclipse Plug-in for PEPA which integrates automated capacity planning into the functionality of the tool, thus allowing optimal configurations of large scale PEPA models to be found.

show abstract

Section: Related Workmentioning

confidence: 99%

Automated Capacity Planning for PEPA Models

Williams

Hillston

2014

Computer Performance Engineering

View full text Add to dashboard Cite

show abstract

“…In recent years, with the advances of the cross discipline, some concepts in other research areas have been introduced to describe this problem. For example, Karaman, S. et al introduce the Process Algebra into the modeling procedures of this problem [7]. The authors firmly grasp the time-sequence of these tasks, and then, apply the composition, which is an important concept of the Process Algebra, to express all actions of the multiple UAVs.…”

Section: Related Workmentioning

confidence: 99%

“…In a word, the above four constraints (2)-(5) can ensure that the paths of all UAVs are connected and flyable. The other two constraints (6)- (7) are the limit of the flying distance and the task number respectively. According to this model, its variable dimension is Nv (Nv+3Nt) 2 , which means that this problem will become very difficult to solve, when Nv and Nt increase.…”

Section: B Problem Formulationmentioning

confidence: 99%

Multi-UAV Task Assignment With Parameter and Time-Sensitive Uncertainties Using Modified Two-Part Wolf Pack Search Algorithm

Chen

Yang

2018

IEEE Trans. Aerosp. Electron. Syst.

130

View full text Add to dashboard Cite

This paper presents a systematical framework to solve the multiple unmanned aerial vehicles (multi-UAV) cooperative task assignment problem. Based on a combinatorial optimization model, it is solved by a digraph-based method and a novel meta-heuristic optimization method, named modified two-part wolf pack search (MTWPS) algorithm. When the number of UAVs/targets is large, in order to reduce the simulation time, we also present a new solution framework based on an easy-computing objective function. Additionally, the parameter and time-sensitive uncertainty are considered in the extended task assignment problem. For the problem with parameter uncertainty, it is formulated by a robust optimization method and solved by a novel combined algorithm, including the classical interior point method and our MTWPS algorithm. For the problem with time-sensitive uncertainty, it is solved by a practical online hierarchical planning algorithm. Finally, numerical simulations and physical experiments demonstrate that the proposed methods can provide a flyable solution for the UAVs and achieve outstanding performance in comparison with other algorithms. Index Terms-Multiple unmanned aerial vehicles (multi-UAV) cooperative task assignments problem, modified two-part wolf pack search (MTWPS) algorithm, robust optimization method, online hierarchical planning algorithm Yongbo Chen received his B.S. degree in Beijing Institute of Technology in 2012. He is currently working toward a dual doctoral degree at Beijing

show abstract

“…Holland from the University of Michigan proposed the GA [14], which uses selection, crossover, and mutation operators to involve the evolutionary process of organisms [15][16][17]. With powerful parallel processing and global optimization ability, this study presented an improved AGA to realize the optimization of the constellation layout design of pseudolites under multiple constraints.…”

Section: Proposed Algorithmmentioning

confidence: 99%

“…The area constraints associated with the constellation layout of pseudolites mainly refer to the geological terrain or traffic that is needed in order to ensure the safety of the equipment and personnel. Given one or more small regions, the total regional area can be expressed as: X , which satisfies the following constraints: (15) The chromosomes are selected randomly into the next generation with a probability proportional to its fitness, which may damage or lose the best individual in the current population. The elite reservation strategy was introduced to improve the efficiency and the convergence speed of the algorithm.…”

Section: A Coding Formatmentioning

confidence: 99%

Study of Constellation Design of Pseudolites Based on Improved Adaptive Genetic Algorithm

Song¹,

Hou²,

Xue³

2016

JCM

View full text Add to dashboard Cite

Global Navigation Satellite System (GNSS) is vulnerable to interferences and has other shortcomings such as unreliable signals in locations that are indoors, in urban canyons, and deep mines. Therefore, the pseudolite (pseudo-satellite) positioning technology, which has gained wide attention in recent years, is used to complement and enhance GNSS. The constellation layout of pseudolites creates geometrical benchmarks in spatial positioning, which in turn affects the receiver positioning accuracy by impacting the Dilution of Precision (DOP) value of each location point within the service area. The constellation layout of pseudolites poses a combinatorial optimization problem with multiple constraints, given the service area and station layout area, such as the distance between the pseudolite and the receiver, making the indicators of spatial configuration and redundancy maintenance and the availability within the service area to achieve best. An improved Adaptive Genetic Algorithm (AGA) is presented based on the mathematical modeling of pseudolite positioning and constraints. The simulation results show that the novel algorithm can effectively solve the optimization problem associated with the constellation layout of pseudolites under a variety of constraints.

show abstract

A Process Algebra Genetic Algorithm

Cited by 15 publications

References 23 publications

Automated Capacity Planning for PEPA Models

Automated Capacity Planning for PEPA Models

Multi-UAV Task Assignment With Parameter and Time-Sensitive Uncertainties Using Modified Two-Part Wolf Pack Search Algorithm

Study of Constellation Design of Pseudolites Based on Improved Adaptive Genetic Algorithm

Contact Info

Product

Resources

About