CHROME: IMPROVING THE TRANSMISSION RELIABILITY BY BANDWIDTH OPTIMIZATION USING HYBRID ALGORITHM 1 Ajeeth Kumar J, 2 C.P Maheswaran, Noorul Islam University Abstract - An approach to improve the transmission reliability by network bandwidth optimization using the hybrid algorithm called chrome. The main objective is to design an optimization algorithm to achieve bandwidth optimization in wireless networks using hybrid of Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) which improves the transmission reliability. In this approach, global search character of PSO and local search character of GA are used. In chrome algorithm, the PSO algorithm is used to search around the environment and where ever necessary the GA s searching techniques are used for optimization. Index Terms Hybrid Algorithm, Wireless Communication, Bandwidth Optimization, Particle Swarm Optimization, Genetic Algorithm, Chrome, Hybrid Algorithm, Transmission Reliability. 1. INTRODUCTION Wireless communications is the fastest growing segment of the communications industry that has captured the attention of the media and the imagination of the public. Cellular systems have experienced exponential growth over the last decade and there are currently about two billion users worldwide. In addition, wireless local area networks supplement or replace wired networks in many homes, businesses, and campuses. Many new applications including wireless sensor networks, automated highways and factories, smart homes and appliances, and remote telemedicine are emerging from research ideas to concrete systems [1]. The explosive growth of wireless systems coupled with the proliferation of laptop and palmtop computers suggests a bright future for wireless networks, both as stand-alone systems and as part of the larger networking infrastructure. However, many technical challenges remain in designing robust wireless networks that deliver the performance necessary to support emerging applications. In recent years, the network transmission rate and routers processing capability have been ever increasing. On the other hand, with rapid broadening of networks and applications, the traffic amount over networks grows explosively each time period. As a result it is always the case that the available network resources cannot meet the needs of network users yet. The optimization of the network resource allocation is of great importance for enhancing the network throughput and improving the network performance [2]. The max min fairness algorithm has been widely used in digital networks, where it is used to allot bandwidth as equally as possible to all the users under certain transmission conditions. Although the algorithm is easy to realize, it tends to yield lower utilization of bandwidth resources than other approaches. The algorithms proposed by Kelly and Low et al. dynamically control the data transmission rates of source nodes in the network so that the global utility of all the source nodes may be maximized. These algorithms can usually achieve a comparatively higher utilization of network resources with some degree of fairness. Using these algorithms, however, flow control is centralized, which makes the algorithms difficult to realize in real environments. Evolutionary algorithms (EA) are heuristic-based global search and optimization methods that have found their way into virtually every area of real world optimization [3]. Ant colony optimization (ACO) and genetic algorithm (GA) are well -known examples; they belong to the class of meta-heuristics or approximate algorithms capable of obtaining fairly good solutions to hard combinatorial optimization problems in a reasonable amount of computation time. Their main limitation is that their empirical performance is unknown, and they could sometimes consume excessive amounts of computation time. GA is a very powerful search and optimization tool which works differently compared to classical search and optimization methods. GA is nowadays being increasingly applied to various optimizing problems owing to its wide applicability, ease of use and global perspective. GA algorithm solves every optimization problem which can be described with the chromosome
encoding. This algorithm can be used in search space that is discrete and is highly constrained and discontinuous. Also all particles tend to converge to the best solution. GA does not demand the knowledge of mathematics. Cross-over can lead to new combinations of genes which are more fit than any in the previous generation. Creating new variants is the key to genetic algorithm, as there is good chance of finding better solution. This is why mutation is also a necessary part of GA. Also GA uses a specified population size. Due to these advantages, GA algorithm is being used for optimization. Swarm intelligence is an intelligent paradigm based on the behavior of the social insects such as bird flocks, fish school, ant colony etc. in which individual species change its position and velocity depending on its neighbour. Particle Swarm Optimization (PSO) is based on the swarm intelligence. It is a population based tool used to find a solution to some optimization problems. The problem (e.g. bandwidth reservation problem) can be transformed to the function optimization problem. As an algorithm, the main strength of the PSO is its fast convergence. PSO algorithm is easy to perform, has few parameters to adjust, only looks for the best solution and used in search space that is continuous. PSO algorithm is efficient in global search. Due to these advantages, PSO algorithm is being used for optimization. The aim of optimization is to find amounts of collection of parameters that maximizes or minimizes mean function to certain limits. Whole appropriate amounts are possible solutions and the best amount of these amounts is optimized solution. Optimization is used in machinery, technology, engineering, and computer science. Finding solution for NP problems [4] is very difficult, approaches like heuristic algorithms is a way of solving this problem. Using these approaches, some solutions can be found that are close to results [5]. Some common approaches are: genetic algorithm, particle swarm optimization and simulate annealing. Major use of these approaches is solving optimization problems. Here a new model known as chrome algorithm is proposed. In the proposed model, global search characters of PSO [5] and local search characters of GA algorithms [6] are used in an efficient way. In this algorithm, PSO initiates a global search process by long steps (either using sphere function or step function) on the available particles and the gbest particle is determined. As per this algorithm all particles consists of 28 chromosomes each and each chromosome is of 8 bits. This selected gbest particle is now provide to the GA algorithm, where chromosome are selection and crossover functions is performed. This crossed-over chromosome forms the new gbest particle, this particle is now used by the PSO s function to determine the new velocity and the new position. Further the GA s mutation function is performed. These processes are repeated until an optimal solution is arrived. 2. LITERATURE SURVEYED Vedantham. S et al. [4] proposed the use of Genetic Algorithm technique to select a subset of calls from the set of incoming call requests for transmission, so that the available network bandwidth is utilized effectively, thus maximizing the revenue generated while preserving the promised QoS. The Asynchronous Transfer Mode (ATM) network model has been evolving as the standard for future networking that is expected to carry voice, real time video and a large volume of still images in addition to the growing volumes of computer data. ATM networks are predominantly expected to be implemented using optical fiber with very low data error rate and guaranteed Quality of Service (QoS). Depending upon the bandwidth requirements and the rigor with which the network is expected to ensure a high QoS, the revenue generated by different calls may vary widely. Other than the revenue, there can be several factors (like call content, source and destination, etc.) making one call more important than another. Under such circumstances, the network needs to allocate the available bandwidth in the most efficient fashion. This process is called the Bandwidth Allocation Problem or BAP. A new heuristic algorithm designed to handle BAP specifically might compete well with the Genetic Algorithm in providing an effective solution for BAP. They consider a situation where a router has to select and accept a subset of incoming calls for transmission so as to maximize the use of the available bandwidth. The maximization is defined by the value of revenue generated and the percentage of available bandwidth utilized by the calls that are accepted.
This approach has the advantage that bandwidth allocation is better than the allocation made by Greedy algorithm and can adapt to the variations that occur in the types of incoming calls during different times of the day. The disadvantage of this approach is that there is more optimal bandwidth than this approach. The main objective proposed by Pradeep Kumar Tiwari et al [1] is to design an optimization algorithm to achieve optimum bandwidth allocation in wireless networks using Genetic Algorithm (GA). It investigates the channel allocation methods for a video transmission over wireless network. Transporting video over the wireless network is expected to be an important component of many emerging multimedia applications. One of the critical issues for multimedia applications is to ensure that the quality-of-service (QoS) requirement is maintained at an acceptable level. The Dynamic Channel Allocation (DCA) method using a Genetic Algorithm is used to monitor dynamically the traffic and adjust the bandwidth according to the QOS parameters and thus provide an optimized bandwidth allocation. A dynamic channel allocation scheme is proposed that can significantly improve bandwidth utilization in wireless networks for video applications. It adjusts the amount of reserved resources using genetic algorithm, while guarantying the required QoS. The results show that the average fitness of the entire population converges to best fitness and the convergence rate is faster using this method. The proposed algorithm provides better solution compared to the other dynamic channel allocation techniques. Anbar. M et al. [3] suggest that real-time traffic requires more attention and it is given priority over nonreal-time traffic in Cellular IP networks. Bandwidth reservation is often applied to serve such traffic in order to achieve better Quality of Service (QoS). Evolutionary Algorithms are quite useful in solving optimization problems of such nature. Both models, GA based and PSO based, try to minimize the Connection Dropping Probability for real-time users in the network by searching the free available bandwidth in the user s cell or in the neighbor cells and assigning it to the real-time users. Alternatively, if the free bandwidth is not available, the model borrows the bandwidth from nonreal time-users and gives it to the real-time users. PSO model does not use crossover operation (i.e. there is no material exchange between particles) that makes the particles same without change but they are influenced by their own previous best positions and best positions in the neighborhood in the global population. In GA, there is a crossover operation (i.e. there is exchange in the material between the individuals in the population) that means there is a chance to generate new offspring with better specifications than the parents. GA model is better in sense of values obtained in every generation but PSO model is better in sense of time taken for the convergence. Asynchronous Transfer Mode (ATM) [11] is widely used in telecommunications systems to send data, video and voice at a very high speed. In ATM network optimizing the bandwidth through dynamic routing is an important consideration. Susmi Routray et al. [6] proposed the Genetic Algorithm (GA) and Tabu search (TS), based on non-traditional Optimization approach, for solving the dynamic routing problem in ATM networks which in return will optimize the bandwidth. The optimized bandwidth could mean that some attractive business applications would become feasible such as high speed LAN interconnection, teleconferencing etc. GA is a non-traditional based optimizing technique which can be used to optimize the ATM network. GA operations can be briefly described as Coding, Initialization, Evaluation, Reproduction, Crossover, Mutation and Terminating condition. GA has been used in previous studies to optimize the ATM network and also in the design of ATM network. Pan and Wang used GA for allocating bandwidth in the ATM network but the limiting factor of their work is the encoding mechanism which is very complex for large networks. The basic concept of Tabu Search is described by Glover in 1989 for solving combinatorial optimization problem. It is kind of iterative search and is characterized by the use of a flexible memory. Tabu search is basically, a single solution, deterministic neighborhood search technique that uses memory - a Tabu list, to prohibit certain moves, even if they are improving. This makes Tabu search a global optimizer rather than a local optimizer. The components of Tabu
search algorithm are Encoding, Initial solution, Objective Function, Move operator, Definition of Neighborhood, Structure of Tabu list(s), Aspiration criteria (optional), Termination criteria. The future ATM based broadband integrated service digital network is expected to support varied traffic with varied traffic patterns, so dynamic routing is an important factor for desired network performance. Genetic algorithm is a better option to solve the dynamic routing in ATM network problem. Also the results obtained by implementing the new initialization technique in GA shows that the configuration string does not converge to a consistent value prematurely as a result the solution obtained is optimal and the amount of time required by the algorithm, to generate an optimal solution, is also reduced. Sajjad Ghatei et al. [3] suggest a new combined approach known as PSO-Great Deluge algorithm for optimization. In this approach the PSO and the great deluge algorithms are combined. Global search character of PSO and local search factor of great deluge algorithm are used based on series. At first step, PSO algorithm is used to search around environment and its results are given to great deluge algorithm to search about taken results accurately. 3. CHROME ALGORITHM PSO and GA algorithms have their own advantage as mentioned in the above sections, besides that there are several disadvantages of using these algorithms as well. GA have the following drawbacks, (a) poor fitness function would generate bad chromosome blocks, (b) no global optimum solution, only local optimum solution and (c) requires large number of response function evaluations depending on the number of individuals. PSO have the following drawbacks, (a) slow convergence in refined search stage, (b) weak local search ability and (c) the method easily suffers from the partial optimism, which causes the less exact at the regulation of its speed and the direction. Some of the drawbacks of PSO and GA are being overcome by creating a hybrid of PSO and GA algorithm for optimization called Chrome. In this algorithm, the global search characters of PSO and the local search characters of GA are used in an efficient way. Parameters used by chrome algorithm are as follows. 1. Each particle has 28 chromosomes each and each chromosome has 8 bit each. 2. The value of each chromosome ranges from -127 to +127, where 8 th bit is set if the value is ve. 3. Fitness Functions a. Sphere fitness function :- i 2 b. Step fitness function :- i 4. G best particle is the particle that has maximum fitness function. 5. The fitness function used for selecting best chromosomes is y = -(x ^ x) + 5. 6. Chromosomes having higher fitness value are selected for cross-over. 7. First half of the selected last 14 chromosomes from end and the first 14 chromosomes from beginning and the second half of the first 14 chromosomes from beginning and the last 14 from beginning are crossed-over. 8. Initial pbestfit value for all particles is set to 64. 9. Initial velocity for all particles is set to 0. 10. Inertia weight w = 0.75. 11. c1 and c2 are the learning rates for individual ability (cognitive) and social influence (group) respectively, c1=c2=2. 12. r1 and r2 are uniform random numbers which are distributed in the interval 0 and 1. 13. A mutation probability of 3% is used.
The pseudo-code of the CHROME algorithm is as follows. CHROME ( ) Create a population of particles, with each particle having 28 chromosomes; Do For each particle do Evaluate the fitness function using either sphere or step function; Identify particle having maximum fitness value, that is the Gbest particle; Select best chromosomes from Gbest particle using GA s fitness function; Perform crossover using Chromosomes having higher fitness value, to create a new offspring, this would be the new Gbest particle; New fitness value is calculate for the newly derived Gbest particle; If the value of the fitness function at the current position is better than the fitness value at pbest then, set the current value as the new Pbest; For each particle do Update the velocity of each particle as Vj k = w.vjk + cl.rl.(pbestk Xjk) + c2.r2.(gbestk Xjk); Update the position of each particle as Xjk = Xjk + Vjk; Perform mutation based on the probability; Until the optimal solution converges; 14. In this algorithm, first the population of particles is being created, such that each particle has 28 chromosomes and each chromosome takes an initial random value ranging from -127 to +127. Each chromosome is of 8th bits, with the 8th bit set to 1, if its value is negative. PSO initiates a global search process by long steps (either using sphere function or step function) on the available particles and the gbest particle is determined (that is the particle that has the maximum fitness value). To further enhance the fitness of the selected gbest particle, this selected gbest particle is now provide to the GA algorithm for the creation of new offspring. For the creating of new offspring the chromosome are selection based on the GA s fitness function and cross-over functions is performed. This crossed-over chromosome form the new gbest particle, this particle is now used by the PSO s function to determine the new velocity and the new position. Further the GA s mutation function is performed based on the mutation probability. These processes are repeated until an optimal solution is arrived. 4. PERFORMANCE EVALUATION The below graphs show the test results comparing the proposed approach CHROME with the existing method PSO, using Sphere fitness function and Step fitness function. Here a new hybrid optimization is presented. The aim of this model is to combine global search character of PSO and local search characters of GA. The PSO algorithm is used to search around environment and the GA is used to search with the selected particle to derive the optimal solution. Both global and local character are used well, first PSO algorithm with long steps starts to search and then by obtaining some results, new results are delivered as entrance to GA algorithm, in this step this algorithm searches with small steps, the results of simulation shows that presented model gives better results compared to particle swarm algorithms.
Fig.1 Simulation result using Sphere Fitness Function The above graph (Fig. 1) shows the simulation result of both PSO and Chrome algorithm using Sphere fitness function. Here the fitness value generated by the Chrome algorithm is higher than that of the PSO algorithm using the same fitness function. Fig.2 Simulation result using Step Fitness Function The above graph (Fig. 2) shows the simulation result of both PSO and Chrome algorithm using Step fitness function. Here the fitness value generated by the Chrome algorithm is higher than that of the PSO algorithm using the same fitness function. Fig.3 Combined simulation results of both Fitness Function The above graph ( Fig. 3) shows the combined simulation results of both PSO and Chrome algorithm using both Sphere and Step fitness function. Here the fitness value generated by the Chrome algorithm is higher than that of the PSO algorithm using the step fitness function. 5. CONCLUSION The core issue with transmission reliability is bandwidth optimization. If the available network bandwidth is optimized efficiently there would be tremendous improvement in the transmission reliability. From the above test results it could be inferred that the new CHROME algorithm which has been developed using the global searching feature of particle swarm optimization technique and the local search capacity of the genetic algorithm provides bandwidth optimization. Chrome algorithm when used along with Step fitness function, which doesn t have much mathematical calculation, provides an optimal solution for bandwidth allocation. Using this new algorithm CHROME for bandwidth optimization will improve the transmission reliability in wireless networks. In future, will be undertaking research on bandwidth optimization using Bacterial Foraging Optimization and Ant Colony Optimization techniques.
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