Bi-velocity discrete particle swarm optimization and its application to multicast routing problem in communication networks

This paper proposes a novel bi-velocity discrete particle swarm optimization (BVDPSO) approach and extends its application to the NP-complete multicast routing problem (MRP). The main contribution is the extension of PSO from continuous domain to the binary or discrete domain. Firstly, a novel bi-velocity strategy is developed to represent possibilities of each dimension being 1 and 0. This strategy is suitable to describe the binary characteristic of the MRP where 1 stands for a node being selected to construct the multicast tree while 0 stands for being otherwise. Secondly, BVDPSO updates the velocity and position according to the learning mechanism of the original PSO in continuous domain. This maintains the fast convergence speed and global search ability of the original PSO. Experiments are comprehensively conducted on all of the 58 instances with small, medium, and large scales in the OR-library (Operation Research Library). The results confirm that BVDPSO can obtain optimal or near-optimal solutions rapidly as it only needs to generate a few multicast trees. BVDPSO outperforms not only several state-of-the-art and recent heuristic algorithms for the MRP problems, but also algorithms based on GA, ACO, and PSO

Distributed Topology Control based on Swarm Intelligence In Unmanned Aerial Vehicles Networks

Unmanned aerial vehicles (UAVs) have shown enormous potential in both public and civil domains. Although multi-UAV systems can collaboratively accomplish missions efficiently, UAV network(UAVNET) design faces many challenging issues, such as high mobility, dynamic topology, power constraints, and varying quality of communication links. Topology control plays a key role for providing high network connectivity while conserving power in UAVNETs. In this paper, we propose a distributed topology control algorithm based on discrete particle swarm optimization with articulation points(AP-DPSO). To reduce signaling overhead and facilitate distributed control, we first identify a set of articulation points (APs) to partition the network into multiple segments. The local topology control problem for individual segments is formulated as a degree-constrained minimum spanning tree problem. Each node collects local topology information and adjusts its transmit power to minimize power consumption. We conduct simulation experiments to evaluate the performance of the proposed AP-DPSO algorithm. Numerical results show that AP-DPSO outperforms some known algorithms including LMST and LSP, in terms of network connectivity, average link length and network robustness for a dynamic UAVNET

Particle swarm optimization for the Steiner tree in graph and delay-constrained multicast routing problems

This paper presents the first investigation on applying a particle swarm optimization (PSO) algorithm to both the Steiner tree problem and the delay constrained multicast routing problem. Steiner tree problems, being the underlining models of many applications, have received significant research attention within the meta-heuristics community. The literature on the application of meta-heuristics to multicast routing problems is less extensive but includes several promising approaches. Many interesting research issues still remain to be investigated, for example, the inclusion of different constraints, such as delay bounds, when finding multicast trees with minimum cost. In this paper, we develop a novel PSO algorithm based on the jumping PSO (JPSO) algorithm recently developed by Moreno-Perez et al. (Proc. of the 7th Metaheuristics International Conference, 2007), and also propose two novel local search heuristics within our JPSO framework. A path replacement operator has been used in particle moves to improve the positions of the particle with regard to the structure of the tree. We test the performance of our JPSO algorithm, and the effect of the integrated local search heuristics by an extensive set of experiments on multicast routing benchmark problems and Steiner tree problems from the OR library. The experimental results show the superior performance of the proposed JPSO algorithm over a number of other state-of-the-art approaches

ETEA: A euclidean minimum spanning tree-Based evolutionary algorithm for multiobjective optimization

© the Massachusetts Institute of TechnologyAbstract The Euclidean minimum spanning tree (EMST), widely used in a variety of domains, is a minimum spanning tree of a set of points in the space, where the edge weight between each pair of points is their Euclidean distance. Since the generation of an EMST is entirely determined by the Euclidean distance between solutions (points), the properties of EMSTs have a close relation with the distribution and position information of solutions. This paper explores the properties of EMSTs and proposes an EMST-based Evolutionary Algorithm (ETEA) to solve multiobjective optimization problems (MOPs). Unlike most EMO algorithms that focus on the Pareto dominance relation, the proposed algorithm mainly considers distance-based measures to evaluate and compare individuals during the evolutionary search. Specifically in ETEA, four strategies are introduced: 1) An EMST-based crowding distance (ETCD) is presented to estimate the density of individuals in the population; 2) A distance comparison approach incorporating ETCD is used to assign the fitness value for individuals; 3) A fitness adjustment technique is designed to avoid the partial overcrowding in environmental selection; 4) Three diversity indicators-the minimum edge, degree, and ETCD-with regard to EMSTs are applied to determine the survival of individuals in archive truncation. From a series of extensive experiments on 32 test instances with different characteristics, ETEA is found to be competitive against five state-of-the-art algorithms and its predecessor in providing a good balance among convergence, uniformity, and spread.Engineering and Physical Sciences Research Council (EPSRC) of the United Kingdom under Grant EP/K001310/1, and the National Natural Science Foundation of China under Grant 61070088

Development and Evaluation of a Topology Optimization Framework for District Thermal Energy Systems to Lower Life Cycle Cost and Carbon Emissions

It is accepted that the most cost-effective approach to decarbonizing energy systems will involve widespread electrification of combustion processes, including space heating. So-called "fifth-generation'' district thermal energy systems, which operate at near-ambient temperatures, have the potential to provide significant reductions in source energy use intensity in appropriate applications, as well as facilitating beneficial electrification of heating, and the integration of renewable and waste heat sources. However, obstacles remain to the adoption of such systems, including a large "search space'' of potential network configurations. This research addresses that obstacle through the development of a framework for network topology optimization of district thermal energy systems. The topology optimization framework seeks to address the question, for a given set of buildings with known locations and loads, "What is the optimal subset of buildings, if any, to connect to a district thermal energy system, and by what network should they be connected, to minimize life cycle cost?'' Though the topology optimization framework is extensible to district thermal energy systems in general, the problem is particularly interesting and relevant in the context of fifth-generation (or ``advanced'') systems. To motivate the focus on fifth-generation district thermal energy systems, this research also evaluates the potential for reduction in source energy-use intensity from the use of low-exergy building level HVAC systems, which would be compatible with a district thermal energy system operating at near-ambient temperatures. The topology optimization problem is addressed in two parts: the selection of the best subset of buildings (if any) to connect to a district thermal energy system, and the selection of the network by which to connect them. The particle swarm optimization (PSO) algorithm is applied to the first part of the problem, and a heuristic --the use of the minimal spanning tree, a concept leveraged from graph theory, which connects the given buildings and plant with a network having the least possible total edge length--is applied to the second part of the problem. This research validates those two approaches for use in network topology optimization in the context of fifth-generation systems. This research also demonstrates the value of network topology optimization through the application of the developed framework to a case study.</p

Optimal Relay Placement in Multi-hop Wireless Networks

Relay node placement in wireless environments is a research topic recurrently studied in the specialized literature. A variety of network performance goals, such as coverage, data rate and network lifetime, are considered as criteria to lead the placement of the nodes. In this work, a new relay placement approach to maximize network connectivity in a multi-hop wireless network is presented. Here, connectivity is defined as a combination of inter-node reachability and network throughput. The nodes are placed following a two-step procedure: (i) initial distribution, and (ii) solution selection. Additionally, a third stage for placement optimization is optionally proposed to maximize throughput. This tries to be a general approach for placement, and several initialization, selection and optimization algorithms can be used in each of the steps. For experimentation purposes, a leave-one-out selection procedure and a PSO related optimization algorithm are employed and evaluated for second and third stages, respectively. Other node placement solutions available in the literature are compared with the proposed one in realistic simulated scenarios. The results obtained through the properly devised experiments show the improvements achieved by the proposed approach

A genetic algorithm for shortest path with real constraints in computer networks

The shortest path problem has many different versions. In this manuscript, we proposed a muti-constrained optimization method to find the shortest path in a computer network. In general, a genetic algorithm is one of the common heuristic algorithms. In this paper, we employed the genetic algorithm to find the solution of the shortest path multi-constrained problem. The proposed algorithm finds the best route for network packets with minimum total cost, delay, and hop count constrained with limited bandwidth. The new algorithm was implemented on four different capacity networks with random network parameters, the results showed that the shortest path under constraints can be found in a reasonable time. The experimental results showed that the algorithm always found the shortest path with minimal constraints

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms