Dynamic Network Formation Using Ant Colony Optimization

Military and industry are moving toward every device being network enabled and connected for reliable availability of communication and information. To make this type of system a reality, the devices must be capable of forming a network topology on their own in a dynamic environment to ensure that the correct information reaches a desired location and on-time. This research presents three contributions for solving highly dynamic (i.e. drastic change within the network) Multi-commodity Capacitated Network Design Problems (MCNDPs) resulting in a distributed multi-agent network design algorithm. The first contribution incorporates an Ant Colony Optimization (ACO) algorithm Ant Colony System (ACS) to solve the static MCNDP with weak constraints. Second, a new algorithm is developed and has the capability to dynamically adjust its exploration parameter of the solution space. This enhanced algorithm converges quickly and automatically adjusts to the dynamically changing network environment. Third, a distributed approach is created replacing the previous centralized solver. The distributed algorithm produces comparable results, but more importantly calculates the network topology in less than 20 percent of the computation time

Routing Algorithm for Vehicular Ad Hoc Network Based on Dynamic Ant Colony Optimization

Increasing interests in Vehicular Ad hoc networks over the last decade have led to huge investments. VANET (Vehicular Ad-hoc Network) is a new field of technology which has been widely used in autonomous systems. Due to rapid topology changing and frequent disconnection makes it difficult to design an efficient routing protocol. Various routing protocols for VANETs have been recently proposed. Most approaches ignored parameters which effect performance of real VANET applications like environmental changes. Environmental changes can affect both performance and throughput in VANET. In this paper, we proposed a routing algorithm based on ant colony optimization and DYMO (Dynamic MANET On-demand) protocol which copes with changes in environment. Ant colony optimization algorithm is a probabilistic technique which has been widely used in finding routes through graphs. Two parameters were considered to evaluate discovered paths in this paper: (i) delay time, (ii) path reliability. Ns-2 was used to implement the proposed algorithm and monitor its performance through different amount of modifications in environment. Results proved that the proposed ant colony routing algorithm can achieve better performance in compare of other well-known methods like Ad Hoc on Demand Distance Vector (AODV)

An approach for coordinating of the cooperative mapping in a self-adaptive formation system based on a modification of the ant colony algorithm

In this work, an approach for cooperative and distributed mapping in a self-adaptive formation system based on a modified version of the ant colony optimization algorithm is proposed. The strategy is distributed, decentralized, real time and it is applied to tasks in which formation characteristic is an essential requirement. The coordination system’s design is inspired by the biological mechanisms that define a social organization in collective systems, specifically, the ant colony system. Voronoi tessalation and Delaunay triangulation techniques are used to model the formation strategy. The approach is adaptable for scenarios with suffer changes in the structure of the environment. The performance of the system is evaluated using a simulator. Simulation results show that the cooperative mapping is efficient, the trials are performed considering an indoor environment. Besides results show that the proposed formation approach is able to rearrange spatially the robots as they navigate, changing the relative robot distances according to the spatial environment restrictions.FAPESP (Grant #2010/07955-8)CNP

A survey of swarm intelligence for dynamic optimization: algorithms and applications

Swarm intelligence (SI) algorithms, including ant colony optimization, particle swarm optimization, bee-inspired algorithms, bacterial foraging optimization, firefly algorithms, fish swarm optimization and many more, have been proven to be good methods to address difficult optimization problems under stationary environments. Most SI algorithms have been developed to address stationary optimization problems and hence, they can converge on the (near-) optimum solution efficiently. However, many real-world problems have a dynamic environment that changes over time. For such dynamic optimization problems (DOPs), it is difficult for a conventional SI algorithm to track the changing optimum once the algorithm has converged on a solution. In the last two decades, there has been a growing interest of addressing DOPs using SI algorithms due to their adaptation capabilities. This paper presents a broad review on SI dynamic optimization (SIDO) focused on several classes of problems, such as discrete, continuous, constrained, multi-objective and classification problems, and real-world applications. In addition, this paper focuses on the enhancement strategies integrated in SI algorithms to address dynamic changes, the performance measurements and benchmark generators used in SIDO. Finally, some considerations about future directions in the subject are given

Memory-based immigrants for ant colony optimization in changing environments

Copyright @ 2011 SpringerAnt colony optimization (ACO) algorithms have proved that they can adapt to dynamic optimization problems (DOPs) when they are enhanced to maintain diversity. DOPs are important due to their similarities to many real-world applications. Several approaches have been integrated with ACO to improve their performance in DOPs, where memory-based approaches and immigrants schemes have shown good results on different variations of the dynamic travelling salesman problem (DTSP). In this paper, we consider a novel variation of DTSP where traffic jams occur in a cyclic pattern. This means that old environments will re-appear in the future. A hybrid method that combines memory and immigrants schemes is proposed into ACO to address this kind of DTSPs. The memory-based approach is useful to directly move the population to promising areas in the new environment by using solutions stored in the memory. The immigrants scheme is useful to maintain the diversity within the population. The experimental results based on different test cases of the DTSP show that the memory based immigrants scheme enhances the performance of ACO in cyclic dynamic environments.This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) of UK under Grant EP/E060722/2

Ant colony optimization with immigrants schemes in dynamic environments

This is the post-print version of this article. The official published version can be accessed from the link below - Copyright @ 2010 Springer-VerlagIn recent years, there has been a growing interest in addressing dynamic optimization problems (DOPs) using evolutionary algorithms (EAs). Several approaches have been developed for EAs to increase the diversity of the population and enhance the performance of the algorithm for DOPs. Among these approaches, immigrants schemes have been found beneficial for EAs for DOPs. In this paper, random, elitismbased, and hybrid immigrants schemes are applied to ant colony optimization (ACO) for the dynamic travelling salesman problem (DTSP). The experimental results show that random immigrants are beneficial for ACO in fast changing environments, whereas elitism-based immigrants are beneficial for ACO in slowly changing environments. The ACO algorithm with hybrid immigrants scheme combines the merits of the random and elitism-based immigrants schemes. Moreover, the results show that the proposed algorithms outperform compared approaches in almost all dynamic test cases and that immigrant schemes efficiently improve the performance of ACO algorithms in DTSP.This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) of UK under Grant EP/E060722/1

A memetic ant colony optimization algorithm for the dynamic travelling salesman problem

Copyright @ Springer-Verlag 2010.Ant colony optimization (ACO) has been successfully applied for combinatorial optimization problems, e.g., the travelling salesman problem (TSP), under stationary environments. In this paper, we consider the dynamic TSP (DTSP), where cities are replaced by new ones during the execution of the algorithm. Under such environments, traditional ACO algorithms face a serious challenge: once they converge, they cannot adapt efficiently to environmental changes. To improve the performance of ACO on the DTSP, we investigate a hybridized ACO with local search (LS), called Memetic ACO (M-ACO) algorithm, which is based on the population-based ACO (P-ACO) framework and an adaptive inver-over operator, to solve the DTSP. Moreover, to address premature convergence, we introduce random immigrants to the population of M-ACO when identical ants are stored. The simulation experiments on a series of dynamic environments generated from a set of benchmark TSP instances show that LS is beneficial for ACO algorithms when applied on the DTSP, since it achieves better performance than other traditional ACO and P-ACO algorithms.This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) of UK under Grant EP/E060722/01 and Grant EP/E060722/02

Interactive and non-interactive hybrid immigrants schemes for ant algorithms in dynamic environments

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Dynamic optimization problems (DOPs) have been a major challenge for ant colony optimization (ACO) algorithms. The integration of ACO algorithms with immigrants schemes showed promising results on different DOPs. Each type of immigrants scheme aims to address a DOP with specific characteristics. For example, random and elitism-based immigrants perform well on severely and slightly changing environments, respectively. In this paper, two hybrid immigrants, i.e., non-interactive and interactive, schemes are proposed to combine the merits of the aforementioned immigrants schemes. The experiments on a series of dynamic travelling salesman problems showed that the hybridization of immigrants further improves the performance of ACO algorithms

A New Dynamic Path Planning Approach for Unmanned Aerial Vehicles

Dynamic path planning is one of the key procedures for unmanned aerial vehicles (UAV) to successfully fulfill the diversified missions. In this paper, we propose a new algorithm for path planning based on ant colony optimization (ACO) and artificial potential field. In the proposed algorithm, both dynamic threats and static obstacles are taken into account to generate an artificial field representing the environment for collision free path planning. To enhance the path searching efficiency, a coordinate transformation is applied to move the origin of the map to the starting point of the path and in line with the source-destination direction. Cost functions are established to represent the dynamically changing threats, and the cost value is considered as a scalar value of mobile threats which are vectors actually. In the process of searching for an optimal moving direction for UAV, the cost values of path, mobile threats, and total cost are optimized using ant optimization algorithm. The experimental results demonstrated the performance of the new proposed algorithm, which showed that a smoother planning path with the lowest cost for UAVs can be obtained through our algorithm. (PDF) A New Dynamic Path Planning Approach for Unmanned Aerial Vehicles. Available from: https://www.researchgate.net/publication/328765418_A_New_Dynamic_Path_Planning_Approach_for_Unmanned_Aerial_Vehicles [accessed Nov 20 2018]