scholarly journals Mobility Aware Energy Efficient Clustering for MANET: A Bio-Inspired Approach with Particle Swarm Optimization

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Naghma Khatoon ◽  
Amritanjali
Keyword(s):  
Energy Efficiency ◽  
Particle Swarm Optimization ◽  
Clustering Algorithm ◽  
Optimization Problems ◽  
Fitness Function ◽  
Particle Swarm ◽  
Delivery Ratio ◽  
Frequent Change ◽  
Swarm Optimization ◽  
Cluster Heads

Mobility awareness and energy efficiency are two indispensable optimization problems in mobile ad hoc networks (MANETs) where nodes move unpredictably in any direction with restricted battery life, resulting in frequent change in topology. These constraints are widely studied to increase the lifetime of such networks. This paper focuses on the problems of mobility as well as energy efficiency to develop a clustering algorithm inspired by multiagent stochastic parallel search technique of particle swarm optimization. The election of cluster heads takes care of mobility and remaining energy as well as the degree of connectivity for selecting nodes to serve as cluster heads for longer duration of time. The cluster formation is presented by taking multiobjective fitness function using particle swarm optimization. The proposed work is experimented extensively in the NS-2 network simulator and compared with the other existing algorithms. The results show the effectiveness of our proposed algorithm in terms of network lifetime, average number of clusters formed, average number of reclustering required, energy consumption, and packet delivery ratio.

Concurrent Societies Based on Genetic Algorithm and Particle Swarm Optimization

2010 ◽  
Vol 14 (1) ◽  
pp. 110-118
Author(s):  
Hrvoje Markovic ◽  
◽  
Fangyan Dong ◽  
Kaoru Hirota
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Nearest Neighbor ◽  
Optimization Problems ◽  
Fitness Function ◽  
Particle Swarm ◽  
Absolute Error ◽  
Population Based ◽  
Test Problems ◽  
Swarm Optimization

A parallel multi-population based metaheuristic optimization framework, called Concurrent Societies, inspired by human intellectual evolution, is proposed. It uses population based metaheuristics to evolve its populations, and fitness function approximations as representations of knowledge. By utilizing iteratively refined approximations it reduces the number of required evaluations and, as a byproduct, it produces models of the fitness function. The proposed framework is implemented as two Concurrent Societies: one based on genetic algorithm and one based on particle swarm optimization both using k -nearest neighbor regression as fitness approximation. The performance is evaluated on 10 standard test problems and compared to other commonly used metaheuristics. Results show that the usage of the framework considerably increases efficiency (by a factor of 7.6 to 977) and effectiveness (absolute error reduced by more than few orders of magnitude). The proposed framework is intended for optimization problems with expensive fitness functions, such as optimization in design and interactive optimization.

Improving AODV route recovery mechanism using PSO in WSN

2019 ◽  
Vol 09 ◽  
Author(s):  
Sunil Kumar K N ◽  
Shiva Shankar
Keyword(s):  
Particle Swarm Optimization ◽  
Network Performance ◽  
Fitness Function ◽  
Routing Algorithm ◽  
Particle Swarm ◽  
Delivery Ratio ◽  
Swarm Optimization ◽  
Packet Delivery ◽  
Control Overhead ◽  
Route Failure

Objective: The conventional Ad Hoc On-Demand Distance Vector (AODV) routing algorithm, route discovery methods pose route failure resulting in data loss and routing overhead. In the proposed method, needs significant low energy consumption while routing from one node to another node by considering the status of node forwards the packet. So that while routing it avoids unnecessary control overhead and improves the network performance. Methods: Particle Swarm Optimization (PSO) algorithm is a nature- inspired, population-based algorithm. Particle Swarm Optimization (PSO) is a Computational Intelligence technique which optimizes the objective function. It works by considering that every member of the swarm contributes in finding the ideal solution by keeping a track of their own best known location and the best-known location of the group and keeps updating them whenever there is a change and hence minimizes the objective fitness function. The fitness function which we considered here is the Node lifetime, Link Lifetime and available Bandwidth. If these parameters are with good then status of node will be strong and hence routing of packet over those nodes will reduce delay and improves network performance. Result: To verify the feasibility and effectiveness of our proposal, the routing performance of AODV and PSO-AODV is compared with respect to various network metrics like Network Lifetime, packet delivery ratio and routing overhead and validated the result by comparing both routing algorithm using Network Simulator 2. The results of the PSO-AODV has outperformed the AODV in terms of low energy, less end to end delay and high packet delivery ratio and less control overhead. Conclusion: Here we proposed to use Particle Swarm Optimization in order to obtain the more suitable parameters for the decision making. The existing AODV protocol was modified to make a decision to recover from route failure; at the link failure predecessor node implementing PSO based energy prediction concept and using weights for each argument considered in the decision function. The fitness values for each weight were found through PSO basic form. We observed that the PSO showed satisfactory behaviour improvement than the performance of AODV for all metrics on the investigated scenarios.

Reverse Search Particle Swarm Optimization to Deal with Dynamic Optimization Problems

2014 ◽  
Vol 571-572 ◽  
pp. 245-251
Author(s):  
Li Chen ◽  
Wei Jiang Wang ◽  
Lei Yao
Keyword(s):  
Particle Swarm Optimization ◽  
Dynamic Optimization ◽  
Optimization Problems ◽  
Fitness Function ◽  
Dynamic Environment ◽  
Particle Swarm ◽  
Dynamic Optimization Problems ◽  
Swarm Optimization ◽  
Promising Area ◽  
Coarse Search

Multiswarm approaches are used in many literatures to deal with dynamic optimization problems (DOPs). Each swarm tries to find promising areas where usually peaks lie and many good results have been obtained. However, steep peaks are difficult to be found with multiswarm approaches , which hinders the performance of the algorithm to be improved furtherly. Aiming at the bottleneck, the paper introduces the idea of sequential niche technique to traditional multiswarm approach and thus proposes a novel algorithm called reverse space search multiswarm particle swarm optimization (RSPSO) for DOPs. RSPSO uses the information of the peaks found by coarse search of traditional multiswarm approach to modify the original fitness function. A newly generated subswarm - reverse search subswarm evolves with the modified fitness function, at the same time, other subswarms using traditional mltiswarm approach still evolve. Two kinds of subswarm evolve in cooperation. Reverse search subswarm tends to find much steeper peak and so more promising area where peaks lie is explored. Elaborated experiments on MPB show the introduction of reverse search enhances the ability of finding peaks , the performance of RSPSO significantly outperforms traditional multiswarm approaches and it has better robustness to adapt to dynamic environment with wider-range change severity.

scholarly journals Fitness Function Based Particle Swarm Optimization Algorithm for Mobile Adhoc Networks

2018 ◽  
Vol 7 (3.1) ◽  
pp. 31
Author(s):  
Rohan Gupta ◽  
Gurpreet Singh ◽  
Amanpreet Kaur ◽  
Aashdeep Singh
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Network Performance ◽  
Particle Swarm Optimization Algorithm ◽  
Fitness Function ◽  
Particle Swarm ◽  
Delivery Ratio ◽  
Average Delay ◽  
Swarm Optimization ◽  
Mobile Adhoc Network

Mobile adhoc network is a network which carries out discussion between nodes in the absence of infrastructure. The fitness function based Particle Swarm Optimization Algorithm has been projected for improving the network performance. The effect of changing the number of nodes, communication range and transmission range is investigated on various qualities of service metrics namely packet delivery ratio, throughput and average delay. The investigation has been carried out using NS-2 simulator.  

scholarly journals A hybrid PSO approach for solving non-convex optimization problems

2012 ◽  
Vol 22 (1) ◽  
pp. 87-105 ◽  
Author(s):  
Timothy Ganesan ◽  
Pandian Vasant ◽  
Irraivan Elamvazuthy
Keyword(s):  
Particle Swarm Optimization ◽  
Convex Optimization ◽  
Optimization Problems ◽  
Fitness Function ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Convex Optimization Problems ◽  
Computational Performance ◽  
Engineering Design Problems

A hybrid PSO approach for solving non-convex optimization problemsThe aim of this paper is to propose an improved particle swarm optimization (PSO) procedure for non-convex optimization problems. This approach embeds classical methods which are the Kuhn-Tucker (KT) conditions and the Hessian matrix into the fitness function. This generates a semi-classical PSO algorithm (SPSO). The classical component improves the PSO method in terms of its capacity to search for optimal solutions in non-convex scenarios. In this work, the development and the testing of the refined the SPSO algorithm was carried out. The SPSO algorithm was tested against two engineering design problems which were; ‘optimization of the design of a pressure vessel’ (P1) and the ‘optimization of the design of a tension/compression spring’ (P2). The computational performance of the SPSO algorithm was then compared against the modified particle swarm optimization (PSO) algorithm of previous work on the same engineering problems. Comparative studies and analysis were then carried out based on the optimized results. It was observed that the SPSO provides a better minimum with a higher quality constraint satisfaction as compared to the PSO approach in the previous work.

scholarly journals Multi-objective clustering algorithm using particle swarm optimization with crowding distance (MCPSO-CD)

2020 ◽  
Vol 6 (1) ◽  
pp. 72
Author(s):  
Alwatben Batoul Rashed ◽  
Hazlina Hamdan ◽  
Nurfadhlina Mohd Sharef ◽  
Md Nasir Sulaiman ◽  
Razali Yaakob ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Clustering Algorithm ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Solution Technique ◽  
Pareto Optimal ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Crowding Distance

Clustering, an unsupervised method of grouping sets of data, is used as a solution technique in various fields to divide and restructure data to become more significant and transform them into more useful information. Generally, clustering is difficult and complex phenomenon, where the appropriate numbers of clusters are always unknown, comes with a large number of potential solutions, and as well the datasets are unsupervised. These problems can be addressed by the Multi-Objective Particle Swarm Optimization (MOPSO) approach, which is commonly used in addressing optimization problems. However, MOPSO algorithm produces a group of non-dominated solutions which make the selection of an “appropriate” Pareto optimal or non-dominated solution more difficult. According to the literature, crowding distance is one of the most efficient algorithms that was developed based on density measures to treat the problem of selection mechanism for archive updates. In an attempt to address this problem, the clustering-based method that utilizes crowding distance (CD) technique to balance the optimality of the objectives in Pareto optimal solution search is proposed. The approach is based on the dominance concept and crowding distances mechanism to guarantee survival of the best solution. Furthermore, we used the Pareto dominance concept after calculating the value of crowding degree for each solution. The proposed method was evaluated against five clustering approaches that have succeeded in optimization that comprises of K-means Clustering, MCPSO, IMCPSO, Spectral clustering, Birch, and average-link algorithms. The results of the evaluation show that the proposed approach exemplified the state-of-the-art method with significant differences in most of the datasets tested.

scholarly journals Effectual Clustering and Node Placement with Differential Evolution Particle Swarm Optimization using Markov Chain Clustering in FANET

2019 ◽  
Vol 9 (1) ◽  
pp. 3927-3934
Keyword(s):  
Particle Swarm Optimization ◽  
Markov Chain ◽  
Differential Evolution ◽  
Clustering Algorithm ◽  
Fitness Function ◽  
Particle Swarm ◽  
Future Generation ◽  
Superior Performance ◽  
Swarm Optimization ◽  
Clustering Model

Flying ad hoc network (FANET) comprises of multiple unmanned aerial vehicles (UAVs) which is effectual technology for future generation. In this investigation, the specific way for constructing a FANET topology using clustering technique to achieve end-to-end communication is elaborated. For this purpose, an application that uses the meta-heuristics approach for cluster analysis is anticipated. Specifically, the parameters of differential evolution (DE) and particle swarm optimization (PSO) have gained the attention and extensive popularity in various communities based on its working effectiveness in resolving complex combinational optimization crisis. Thus, hybrid model of DE and PSO based Markov Chain Clustering Model (MCCM) is designed in this investigation to analyse the problems of clustering in FANET and reliability parameters are examined. The proposed (DEPSO-MCM) model is to enhance search capability and to attain superior flexibility in forming nodes cluster. Empirical outcomes demonstrate DEPSOMCM based clustering algorithm attains superior performance in number of epochs to acquire fitness function effectually. The simulation was carried out in NS-2 simulator, the outcomes based on the simulation shows that the proposed method works effectually and shows better trade-off than the existing techniques to provide a meaningful clustering.

scholarly journals A Constructive Data Classification Version of the Particle Swarm Optimization Algorithm

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Alexandre Szabo ◽  
Leandro Nunes de Castro
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Clustering Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Classification Problems ◽  
Swarm Optimization ◽  
Swarm Algorithms ◽  
Wide Range

The particle swarm optimization algorithm was originally introduced to solve continuous parameter optimization problems. It was soon modified to solve other types of optimization tasks and also to be applied to data analysis. In the latter case, however, there are few works in the literature that deal with the problem of dynamically building the architecture of the system. This paper introduces new particle swarm algorithms specifically designed to solve classification problems. The first proposal, named Particle Swarm Classifier (PSClass), is a derivation of a particle swarm clustering algorithm and its architecture, as in most classifiers, is pre-defined. The second proposal, named Constructive Particle Swarm Classifier (cPSClass), uses ideas from the immune system to automatically build the swarm. A sensitivity analysis of the growing procedure of cPSClass and an investigation into a proposed pruning procedure for this algorithm are performed. The proposals were applied to a wide range of databases from the literature and the results show that they are competitive in relation to other approaches, with the advantage of having a dynamically constructed architecture.

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