I need to source code for pso algorithm in ns2

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pls send the ns2 code for pso algorithm quick

packet scheduling algorithm for collision reduction in manet by using pso algorith to minimized delay. package is ns2 and backend is c++ pls send the code

I need ns2 source code for pso algorithm.. Plz help me

pso algorithm ns2 source code

Abstract
In the conventional mobile ad hoc network (MANET) systems' route rediscovery methods, there exists route failure in all route discovery methods resulting in data loss and communication overheads. Hence, the routing has to be done in accordance with mobility character of the network. In this manuscript, a particle swarm optimization (PSO)-based lifetime prediction algorithm for route recovery in MANET has been proposed. This technique predicts the lifetime of page link and node in the available bandwidth based on the parameters like relative mobility of nodes and energy drain rate, etc. Using predictions, the parameters are fuzzified and fuzzy rules have been formed to decide on the node status. This information is made to exchange among all the nodes. Thus, the status of every node is verified before data transmission. Even for a weak node, the performance of a route recovery mechanism is made in such a way that corresponding routes are diverted to the strong nodes. With the aid of the simulated results, the minimization of data loss and communication overhead using PSO prediction has been discussed in detail.

1 Introduction
1.1 MANET
Mobile ad hoc network (MANET) is a multihop wireless network with mobile nodes that can move independently. MANET has no infrastructure in the sense that it does not require any access points or base stations for transmission [1]. Nodes can communicate with each other directly or through intermediates [2]. As the nodes move arbitrarily in the network, the network topology can change frequently. One node will communicate with the other node directly within sufficient radio propagation and indirectly through multihop routing with all other nodes. To help such kind of communication, many routing algorithms already have been developed. In MANET, the nodes are randomly present and they are supposed to develop and maintain the entire network automatically; hence, the routing algorithms are crucial. Due to active topology and limited resources, developing a dynamic routing protocol that can efficiently find a routing path with low control overhead is very important in MANETs [3-5]. Most of the devices and systems in MANET are designed in a performance-oriented manner, not considering the energy efficiency [6].

1.2 Significance of node and page link lifetime prediction for route recovery process
In general, the network depends on the node assistance for providing the packet routing. Routing is the basic operation in ad-hoc networks. The routing algorithm should be robust, adaptive, and in a self-organized way [5,7]. Nodes cannot forward the data packets to the receiver node when the prediction error is less than a pre-configured threshold value. Prediction is used to make the decision for transmission [8]. A. Vasilakos et al. [9] have presented an application of evolutionary-fuzzy prediction in inter-domain routing of broadband network connections with quality-of-service requirements in the case of an integrated ATM and SDH networking architecture.

The node mobility increases the complexity of routing because the greater the mobility of the nodes, the more chances of page link breakage. This breakage will in turn lead to increased routing control overhead and will reduce the efficiency of the network due to the increased frequency of the route discovery process. Hence, the action of page link breakages in MANET becomes a vital factor. Further, this kind page link breakage will also lead to frequent path failures and may cause route reconstructions. As a result, the overhead of the routing protocol will be increased and the lesser packet delivery ratio and longer end-to-end delay will be terminated [4,5]. Re-routing in a mobile ad-hoc network is costly and would result in flooding the network due to the lack of infrastructure [10]. In addition to that, the re-route discovery is also leading to the large control message overhead and high latency. Therefore, the re-route discovery reduces efficiency of the networks [11]. Routing in MANET is restricted by the network breakage due to their node mobility or energy depletion of the mobile nodes [12,13].

The existing MANET routing protocols do not operate well in environments prone to frequent and long-lived disruptions. These routing protocols assume usually connected network and require an end-to-end path to exist in order for a source to send data to a destination [14]. Nodes lie near the station are often included in the routing path. Hence, the energy of the node drains quickly [15].

1.4 Salient feature of particle swarm optimization
Besides computational intelligence (CI) [16], artificial intelligence techniques are nowadays involved in various applications. Several studies make use of genetic algorithm (GA)-based techniques to solve network problems [17]. Particle swarm optimization (PSO) is a stochastic optimization technique developed by the inspiration of the social behavior of bird flocking or fish schooling. In PSO, each single solution is a ‘bird’ in the search space (particle). The strength value is combined with each particle, which is calculated by the fitness function to be optimized, and it has the velocity, which expresses the flying of the particle. The particles will fly in the search space and will adjust with the velocities dynamically according to their historical behaviors. This process will guide the particles to fly toward the better search area in the search space. In MANET, the work of sending the packets from source to destination is difficult because of the mobility of the elements and there is no central control. To solve these problems, the swarm intelligence concept can be applied. The PSO algorithm was initially introduced by Kennedy and Eberhart (1995) in terms of social and cognitive behavior. This technique resolves the problems in various fields such as engineering and computer science [18-20].

2 Related works
Xin Ming Zhang et al. [5] have proposed an estimated distance (EstD)-based routing protocol (EDRP) to guide a route discovery. This protocol can restrict the propagation range of route request and reduce the routing overhead. The change regularity of the received signal strength is exploited to estimate the geometrical distance between a pair of nodes, which is called the estimated geometrical distance (EGD). An estimated topological distance (ETD) is a topology-based EstD that can mitigate the effect of inaccurate EGD. The EstD is a combination of EGD and ETD. Every node evaluates the page link quality through the computational process of the EGD to eliminate the weak links and then uses the EstD to steer the route request packets toward the general direction of the destination.

A. K. Daniel et al. [7] have proposed a new protocol in wireless mobile heterogeneous networks based on the use of path information, traffic, stability estimation factor, and bandwidth resource information at each node for allocating the route path and buffer. This can handle the hand-off problem of the mobile network. It uses two buffers for the new call and hand-off calls. If there is no channel available instead of dropping them, it will store in the buffer. Whenever the channel is free, it will allocate the packets for communication. This protocol greatly improves the performance of the network.

C. Priyadharshini et al., [21] have proposed a new algorithm, which utilizes the network parameters related to dynamic nature of nodes through energy drain rate and relative mobility estimation rate to predict the node lifetime and page link lifetime. The least dynamic route has been selected for forwarding the data packets. Finally, this route lifetime prediction algorithm is implemented in the new protocol environment, which is based on the dynamic source routing (DSR) protocol. This new protocol outperforms the existing protocols like the lifetime prediction routing (LPR) and DSR protocols in terms of throughput, routing failure, routing overhead, packet loss ratio, and packet delivery ratio.

Q. Han et al. [22] have proposed a page link availability prediction-based reliable routing for MANETs that takes unpredictable topology changes and frequent page link failure into account. The page link availability is predicted over a short period of time by estimating the distance between two adjacent nodes. They have derived an analytical expression for page link availability based on the relative mobility of the nodes.

Yen Yun-Sheng et al. [23] have proposed a multi-constrained QoS multicast routing method using genetic algorithm. It uses the available resources and minimum computation time in a dynamic environment. By selecting the appropriate values for parameters such as crossover, mutation, and population size, the genetic algorithm improves and tries to optimize the routes.

3 Work description
In PSO, every particle is considered as a possible solution to the numerical optimization problem in a D-dimensional space. In this search space, each particle contains its assigned location and velocity.

Let Pi denote the particle's position, Vi denote the particle's velocity, Lbp be the local memory space, and Gbp be the global memory space.

In addition, each particle contains a local memory space (Lbp) to store the best position experienced by the particle until then. Each particle contains a global memory space for storing the best global position experienced by the particle until then. Using this information, the velocity of the particle can be estimated using Equation 1. Equation 2 gives the updated position of the particle.

3.1 Estimation of metrics
3.1.1 Link lifetime
Link lifetime or page link availability is defined as the probability that a page link will be continuously available for a specified period of time. The page link availability can be predicted accurately over a short period of time, by estimating the distance between two nodes [21].

Let Mi represent the link, xi be the connection, LTxi the connection lifetime, Ni-1 and Ni be the adjacent nodes, and BNi and BNi-1 be the battery lifetime of the node Ni.

The connection lifetime (LTxi) depends on the relative mobility and distance among the nodes Ni-1 and Ni at time t. The page link lifetime (LTMi) is estimated using the following expression:

LTMi=min(LTxi,BNi−1,BNi) (5)
Thus, the lifetime of route R is defined as the minimum value of the lifetime of both nodes and connections involved in route R.

3.1.2 Node lifetime
The nodes may exist in two states such as active and inactive modes. The active mode node drains more energy that results in shorter lifetime than the inactive mode node. Therefore, the node lifetime routing depends upon the energy state of nodes such as residual energy and energy drain rate [21].

Let REi be the residual energy of the Ni, EDi be the energy depletion rate of Ni and T be the duration in seconds.

The node lifetime is estimated using Equation 6:

LTNi=REnTi/EDni,t∈[nT,(n+1)T] (6)
3.1.3 Available bandwidth
Every node is in charge for estimating the available bandwidth on its link. For a given node [24],

let β be the available bandwidth and C be the page link capacity associated with one-hop neighbor i.

AR is the cumulative assigned rates for all incoming and outgoing flows.

Hence, the sum of the assigned incoming and outgoing flow rates and available bandwidth on the page link should be equal to the capacity of the page link i. This can be expressed as

ARij+βi=Ci (7)
The page link capacity is measured and available bandwidth is defined by the following equation:

βjΔ¯¯¯¯¯¯max{0,Cj−ARij} (8)
3.2 PSO-based lifetime prediction algorithm
Our particle swarm optimization-based proposed algorithm predicts the page link lifetime and node lifetime, available bandwidth based on the parameters such as relative mobility of nodes, energy drain rate and page link capacity, respectively. It is described below.

Step 1
When the nodes are deployed in the network, swarm particles (SPi) are initialized such that the particle's position is randomly dispersed in space. Each SPi represents a search window equivalent to the node's position and velocity (Pi, Vi).

Step 2
Each SPi monitors certain parameters of each node such as node lifetime, page link lifetime, and available bandwidth.

Step 3
Based on the monitored parameters, fitness function (Fi) of each particle is estimated as per Equation 9:

Fi=(α1*LTMi)+(α2*LTNi)+(α3*βi) (9)
where α1, α2, and α3 are the weight values.

Step 4
The local best (Lbp) and global best (Gbp) value of fitness and position of each particle is estimated.

Step 5
Update the position of Lbp and Gbp according to the following condition

i. If Fi > Fi (Lbpi)

Then

Update the position of Lbp with the fitness value Fi

End if

ii. If Fi > Fi (Gbpi)

Then

Update the position of Gbp with fitness value Fi

End if

Step 6
Update the velocity and position of each particle using Equations 1 and 2.

Step 7
The value updated in the global best particle is considered as the best-predicted value.

Step 8
The predicted page link lifetime, node lifetime, and available bandwidth are fuzzified and fuzzy rules are formed to decide the type of node whether it is a weak, normal, or strong node.

3.3 Fuzzy-based node status estimation
This technique involves the detection of the node's status by fuzzy logic technique. The steps to determine the fuzzy rule-based interference are as follows:

• Fuzzification: In Fuzzification, the crisp inputs are obtained from the selected input variables and then the degrees to which the inputs belong to each of the suitable fuzzy set are estimated.

• Rule evaluation: The fuzzified inputs are taken and applied to the antecedents of the fuzzy rules. It is then applied to the consequent membership function.

• Aggregation of the rule outputs: This involves merging of the output of all rules.

• Defuzzification: The merged output of the aggregate output fuzzy set is the input for the defuzzification process and a single crisp number is obtained as output.

Initially, the fuzzy logic engine analyzes each node for the detection of the node status such as normal (N), weak (W) and strong (S) based on predicted parameters such as page link lifetime (LTM), node lifetime (LTN), and available bandwidth (β).

3.3.1 Fuzzification
This involves fuzzification of input variables such as page link lifetime (M), node lifetime (N), and available bandwidth (AB) and these inputs are given a degree to appropriate fuzzy sets. The output crisp inputs are the combination of M, N, and B. We take two possibilities, high and low, for M, N, and AB.Figures 2, 3, 4, and 5 show the membership function for the input and output variables. Due to the computational efficiency and uncomplicated formulas, the triangulation functions are utilized. These triangular functions are widely used in real-time applications. Also, a positive impact is offered by this design of membership function.

plzz anyone post the source code of pso in ns2

To get full information or details of pso algorithm ns2 source code please have a look on the pages

http://codeforges/0/implementation-code-for-particle-swarm-optimization-in-ns2

http://codeforges/0/PSO---ns2

if you again feel trouble on pso algorithm ns2 source code please reply in that page and ask specific fields in pso algorithm ns2 source code

please anyone got source codes for pso in php&html?

pso algorithm ns2 source code

Particle swarm optimization (PSO) is a population based stochastic optimization technique developed by Dr. Eberhart and Dr. Kennedy in 1995, inspired by social behavior of bird flocking or fish schooling.

PSO shares many similarities with evolutionary computation techniques such as Genetic Algorithms (GA). The system is initialized with a population of random solutions and searches for optima by updating generations. However, unlike GA, PSO has no evolution operators such as crossover and mutation. In PSO, the potential solutions, called particles, fly through the problem space by following the current optimum particles.

Each particle keeps track of its coordinates in the problem space which are associated with the best solution (fitness) it has achieved so far. (The fitness value is also stored.) This value is called pbest. Another "best" value that is tracked by the particle swarm optimizer is the best value, obtained so far by any particle in the neighbors of the particle. This location is called lbest. when a particle takes all the population as its topological neighbors, the best value is a global best and is called gbest.

The particle swarm optimization concept consists of, at each time step, changing the velocity of (accelerating) each particle toward its pbest and lbest locations (local version of PSO). Acceleration is weighted by a random term, with separate random numbers being generated for acceleration toward pbest and lbest locations.

In past several years, PSO has been successfully applied in many research and application areas. It is demonstrated that PSO gets better results in a faster, cheaper way compared with other methods.

Another reason that PSO is attractive is that there are few parameters to adjust. One version, with slight variations, works well in a wide variety of applications. Particle swarm optimization has been used for approaches that can be used across a wide range of applications, as well as for specific applications focused on a specific requirement.

(19-03-2015, 10:10 AM)Guest Wrote: I need to source code for pso algorithm in ns2

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pls send the ns2 code for pso algorithm quick

In computer science, particle swarm optimization (PSO) is a computational method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality. PSO optimizes a problem by having a population of candidate solutions, here dubbedparticles, and moving these particles around in the search-space according to simple mathematical formulae over the particle's position and velocity. Each particle's movement is influenced by its local best known position and is also guided toward the best known positions in the search-space, which are updated as better positions are found by other particles. This is expected to move the swarm toward the best solutions.