CHAPTER 4 PROPOSED HYBRID INTELLIGENT APPROCH FOR MULTIPROCESSOR SCHEDULING

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1 79 CHAPTER 4 PROPOSED HYBRID INTELLIGENT APPROCH FOR MULTIPROCESSOR SCHEDULING The present chapter proposes a hybrid intelligent approach (IPSO-AIS) using Improved Particle Swarm Optimization (IPSO) with Artificial Immune System (AIS) for multiprocessor task scheduling problem with two cases, namely, static task scheduling and dynamic task scheduling with and without load balancing. 4.1 INTRODUCTION The drawback in the hybrid approach IPSO-SA is that it slightly slower in convergence due to the slow speed of SA. To conquer the drawback of the hybrid algorithm IPSO-SA s slow convergence, the search is guided towards untried regions in the solution space and also to improve further, an immune based intelligent approach is thought of. An Immune system is a naturally occurring event response system that can quickly adapt to the changing situations. In AIS, the models of vaccination and receptor editing are designed to improve the immune performance. The combined effect of IPSO with AIS directs to a promising result. The basic idea of combining IPSO with AIS is to combine the good features of both the algorithms. In the present chapter, a new hybrid intelligent algorithm is developed using IPSO with AIS to achieve better solutions.

2 REVIEW OF LITERATURE Luh and Liu (2004) developed a Reactive Immune Network (RIN) for mobile robot learning navigation strategies within unknown environments. In their approach, a modified virtual target method is integrated to solve the local minima problem. Wojtyla et al (2006) proposed an efficient method of extracting knowledge when scheduling parallel programs onto processors using AIS. The author s proposed approach reorders the nodes of the program according to the optimal execution order on one processor, which works in either learning or production mode. In the learning mode an immune system to optimize the allocation of the tasks to individual processors was used. In the production mode the optimization module is not invoked, only the stored allocations are used. The proposed approach gives similar results to the optimization by a Genetic Algorithm (GA) but requires only a fraction of function evaluations. Fu et al (2007) proposed a hybrid artificial immune network which uses the swarm learning of Particle Swarm Optimization to speed up the convergence of the artificial immune system. Yu (2008) developed an algorithm based on AIS to schedule for heterogeneous computing environments. Empirical studies on bench mark task graphs show that this algorithm significantly outperforms a deterministic algorithm. Lin and Ying (2012) developed an algorithm based on revised AIS and Simulated Annealing effect (RAIS) to minimize the makespan of blocking flow shop. The proposed algorithm is evaluated and compared with

3 81 well-known bench mark problems of taillard used. The results show that the proposed algorithm outperforms the state-of-art algorithms on the same bench mark problems. Engin et al (2004) used a computational method based on the principle of clonal selection and affinity maturation mechanism of the immune response. The objective is to minimize the makespan of the scheduling problem. The operating parameters of meta-heuristics play an important role. He presents a genetric systematic procedure which is based on a multi-step experimental design approach for determining the optimum system parameters of AIS and tested with benchmark problems. Results infer that an AIS based algorithm is effective to solve hybrid flow shop problems. Lin et al (2011) presented a Hybrid Taguchi-Immune Algorithm (HTIA) to deal with the unit commitment problem, which integrates Taguchi method and traditional immune algorithm and provides a powerful global exploration capability. The proposed algorithm shows better performance compared with the other methods published before. The test results reveal that the proposed method is feasible and robust and more effective. Bagheri et al (2010) proposed an artificial based on integrated approach to solve the flexible job shop scheduling to minimize makespan. The algorithm uses several strategies for selecting the individuals for reproduction and also different mutation operators used for reproducing new individuals. The proposed approach is validated with benchmark problems, and computational results show the quality. Zandieh et al (2006) used an immune algorithm to tackle complex problems of a Sequence Dependent Setup Times (SDST) and produce a

4 82 reasonable manufacturing schedule within an acceptable time. Results were compared with the Random Key Genetic Algorithm (RKGA) presented previously. From the results, it is known that IA outperformed RKGA. King et al (2001) described an intelligent agent for task allocation in a heterogeneous computing environment. It exploits some of the functionalities in designing agent-based parallel control systems. Sun and Yang (2008) presents a new hybrid optimization algorithm which combines the strong global search ability of Artificial Immune System (AIS) with a strong local search ability of External Optimization (EO) algorithm. The algorithm tested with benchmark problems with makespan criterion, and results show that the proposed method is effective. Ebrahimi Moghaddam et al (2012) an Immune based Genetic Algorithm (IGA) is proposed which reduces the search space of Multiprocessor Task Scheduling Problem(MTSP), and effectively influences the convergence speed of the optimization process and guarantees the validity of the solutions by using crossover and mutation operators. Experimental results showed that the proposed algorithm uses less number of iterations to find the solution, when applied to the benchmark problems. Kahraman et al (2009) proposed a new artificial immune system algorithm to solve multi objective fuzzy flow shop scheduling problem, in which fuzzy sets are used to model processing times and due dates. The objective of the algorithm is to minimize the average tardiness and number of tardy jobs. The effectiveness are tested by comparing it with genetic algorithms. The outcome tells that the proposed algorithm is more effective.

5 83 Zelenka (2011) compared two approaches for Job Shop Scheduling Problem (JSSP) in real manufacturing system. The first approach is based on the mechanisms inspired by biological evolution and the immune system. The second stochastic optimization algorithm is based on social simulation models. 4.3 HYBRID INTELLIGENT ALGORITHM The main objective of the hybridization is to integrate different learning and adaptation techniques to overcome individual limitations and to achieve synergetic effects through the combination of these techniques. Biological immune systems can be viewed as a powerful distributed information processing systems, capable of learning and selfadaptation. AIS is rapidly emerging, which is inspired by theoretical immunology and observed immune functions, principles and models. The efficient mechanisms of immune system, including clonal selection, learning ability, memory, robustness and flexibility make AISs useful in many applications. AIS appear to offer powerful and robust information processing capabilities for solving complex problems. Hence, a hybrid intelligent algorithm IPSO-AIS is developed to improve the performance of the multiprocessor scheduling problem Basic AIS-Based Scheduling Algorithm The brief outline of the proposed algorithm based on AIS can be described as follows (Ge et al 2008).

6 84 Step 1 : Step 2 : Step 3 : Step 4 : Initialize pop_size antibodies as an initial population, where pop_size denotes the population size. Select m antibodies from the population by the proportional selection model and clone them to a clonal library. Perform the mutation operation for each of the antibodies in the clonal library. Randomly select s antibodies from the clonal library to perform the operation of vaccination. Step 5 : Replace the worst s antibodies in the population by the best s antibodies from the clonal library. Step 6 : Perform the operation of receptor editing if there is no improvement of the highest affinity degree for a certain number of generations. Step 7 : Stop if the termination condition is satis ed; else, repeat Steps 2 to PROPOSED HYBRID INTELLIGENT ALGORITHM (IPSO-AIS) The steps involved in the proposed hybrid algorithm (Improved PSO with Artificial Immune System is as follows, Step 1 : Initialize Population size of the antibodies as PS A. Step 2 : Initialize the number of particles N and its value may be generated randomly. Initialize swarm with random positions and velocities.

7 85 Step 3 : Step 4 : Compute the finishing time for each and every particle using the objective function and also find the pbest. If current fitness of particle is better than pbest the set pbest to current value. If pbest is better than gbest then set gbest to current particle fitness value. Select particles individual pworst value, that means particle moving away from the solution point. Step 5 : Update the velocity and position of particle as per Equation (2.1) and (2.2). Step 6 : If best particle is not changed over a period of time, a) Select m antibodies out of the population PS A by the proportional selection model and clone them to a colonal library. Step 7 : Step 8 : Step 9 : Step 10 : Perform the mutation operation for each of the antibodies in the clonal library. Randomly select s antibodies from the clonal library to perform the operation of vaccination. Replace the worst s antibodies in the population by the best s antibodies from the clonal Library Terminate the process if maximum number of iterations reached or optimal value is obtained, else go to step 3. The flow chart for the hybrid algorithm is shown in Figure 4.1

8 86 Start Initialize population size of Antibodies Initialize the population Input number of processors, number of jobs and population size D Compute the objective function Invoke Hybrid algorithm Yes If E < best E (P best) so far No Search is terminated optimal solution reached For each generation B For each particle Current value = new p best Choose the minimum F of all particles as the g best Calculate particle velocity using Equation (2.1) A Figure 4.1 Flowchart for the proposed hybrid intelligent approach IPSO-AIS

9 87 A Calculate particle position using (2.2) Update memory of each particle No If the best particle is not changed over a period of time B Yes Select m antibodies from the population and clone them to clonal library Perform mutation operation to the antibodies Perform vaccination operation on randomly selected s antibodies Replace the worst antibodies by best antibodies C Figure 4.1 (Continued)

10 88 C Yes If improvement in highest affinity degree No Perform receptor editing operation End B End No D If stopping condition reached Yes Stop Figure 4.1 (Continued)

11 SIMULATION PROCEUDRE The details of the simulation carried out for implementing the proposed hybrid algorithm is given in the present section. Benchmark datasets are taken from EricTailard s site for dynamic task scheduling. Two datasets are taken for simulation. Data set 1involves 50 tasks and 20 processors. Data set 2 involves 100 tasks with 20 processors. The data for the static scheduling is randomly generated, such as 2 processors with 20 tasks, 3 processors with 20 tasks, 3 processors with 40 tasks, 4 processors with 30 tasks, 4 processors with 50 tasks, 5 processors with 45 tasks and 5 processors with 60 tasks. To demonstrate the effectiveness of the proposed hybrid algorithm, the proposed approach is run with 30 independent trials with different values of random seeds and control parameters. The optimal result is obtained for following the parameter settings Artificial Immune System Number of generations : 200 Mutation rate : 0.1 Sampling rate : 0.1 Antibodies : Twice the number of tasks Improved Particle Swarm Optimization The initial solution is generated randomly C 1g, C 1b and C 2 : 2,2 and 2 Population size : Twice the number of tasks (Salman et al 2002)

12 90 W min - W max : 0.5 Max. Iteration : 500 The proposed hybrid approach IPSO-ACO is developed using MATLAB R2009 and executed in a PC with Intel core i3 processor with 3 GB RAM and 2.13 GHz speed. 4.6 STATIC SCHEDULING The tasks considered are independent. Hence, the tasks can be executed in any order in any processor. The objective function is the same as specified in the Equations (2.4) to (2.9). The application of intelligent hybrid algorithm (IPSO-AIS) for scheduling multiprocessor tasks is shown in the present chapter Results and Discussion The proposed hybrid approach IPSO-AIS is tested for static task scheduling problem with the datasets specified in the simulation procedure and the results achieved are shown in Table 4.1. Table 4.1 Total finishing time and average waiting time using the proposed hybrid intelligent approach IPSO-AIS No of Processors No of jobs Proposed IPSO-AIS AWT TFT

13 91 The proposed hybrid approach IPSO-AIS is tested with various randomly generated datasets. For the dataset 3 processors with 40 tasks, IPSO-AIS produces total finishing time 61.20s and average waiting time 34.26s, for dataset 4 processors with 50 tasks 67.83s as total finishing time and 25.96s as average waiting time and for dataset 5 processors with 60 tasks s as average waiting time and 69.01s as total finishing time Performance Comparison In order to validate the performance of the proposed hybrid intelligent approach IPSO-AIS, comparisons have been made with the approaches IPSO, IPSO-SA with the same datasets, and are reported in Table 4.2. These results reveal that the proposed hybrid approach IPSO-AIS is comparatively better than the other approaches. Table 4.2 Comparison of job finishing time and average waiting time using IPSO, IPSO-SA and the proposed IPSO-AIS No of Processors No of jobs IPSO IPSO-SA Proposed IPSO-AIS AWT TFT AWT TFT AWT TFT For the dataset 3 processors with 40 tasks, IPSO produces average waiting time 41.03s, hybrid approach IPSO-SA produces as 38.45s and the proposed hybrid intelligent approach IPSO-AIS produces as 34.26s. For the

14 92 same dataset, total finishing time produced by IPSO is 69.04s, by hybrid approach IPSO-SA is 65.40s and by the proposed intelligent approach is 61.20s. For the dataset 5 processors with 60 tasks, IPSO produces average waiting time as 36.56s and total finishing time as 72.31s, IPSO-SA produces average waiting time as 32.76s and total finishing time as 69.13s and the proposed hybrid intelligent algorithm IPSO-AIS produces 30.19s as average waiting time and total finishing time as 69.01s. It is empirically proved that the proposed hybrid approach IPSO-AIS simultaneously reduces both Total finishing time and average waiting time. Thus, based on the results, it is inferred that the proposed hybrid intelligent algorithm IPSO-AIS produces better results than the conventional methodologies LPT, SPT, GA, standard PSO, and hybrid approach IPSO-SA. The variations found in the total finishing time and average waiting time using different approaches namely, using IPSO, IPSO-SA and IPSO-AIS are shown from Figures 4.2 to 4.8. Figure 4.2 Total Finishing Time and Average waiting time for 2 processors with 20 jobs using IPSO, IPSO-SA and IPSO-AIS

15 93 Figure 4.3 Total Finishing Time and Average waiting time for 3 processors with 20 jobs using IPSO, IPSO-SA and IPSO-AIS Figure 4.4 Total Finishing Time and Average waiting time for 3 processors with 40 jobs using IPSO, IPSO-SA and IPSO-AIS

16 94 Figure 4.5 Total Finishing Time and Average waiting time for 4 processors with 30 jobs using IPSO, IPSO-SA and IPSO-AIS Figure 4.6 Total Finishing Time and Average waiting time for 4 processors with 50 jobs using IPSO, IPSO-SA and IPSO-AIS

17 95 Figure 4.7 Total finishing time and average waiting time for 5 processors with 45 jobs using IPSO, IPSO-SA and IPSO-AIS Figure 4.8 Total finishing time and average waiting time for 5 processors with 60 jobs using IPSO, IPSO-SA and IPSO-AIS

18 96 Thus, the results reveal that the proposed IPSO-AIS produce an improvement in the performance, when compared to the standard PSO and hybrid approach IPSO-SA. 4.7 DYNAMIC TASK SCHEDULING WITHOUT LOAD BALANCING In the dynamic task scheduling problem, reducing the total completion time of processors is a major issue. Hence, to minimize the makespan of the entire schedule, the objective function is represented in Equations (2.10) to (2.12) Results and Discussion The obtained results have been tabulated and shown in Table 4.3, which represents the cost and convergence time comparison of IPSO, IPSO- SA and Intelligent Hybrid Algorithm. The results reveal that the IPSO-AIS performs better than the other algorithms. Table 4.3 Best cost, worst cost, average cost and convergence time using IPSO, IPSO-SA and the proposed hybrid intelligent approach IPSO-AIS for dynamic task scheduling without load balancing Method IPSO IPSO-SA Proposed IPSO-AIS Number of tasks Best Cost Worst Cost Average Cost Convergence Time

19 97 Comparisons have been made based on the algorithms with IPSO, IPSO-SA and the proposed hybrid intelligent approach IPSO-AIS on the best, average and worst cost achieved for dynamic task scheduling. For dataset 1, IPSO achieves the best cost as 2374, IPSO-SA achieves the best cost as 2156 and the proposed hybrid intelligent approach IPSO-AIS achieves the best cost as For dataset 2, IPSO produces best cost 4527, IPSO-SA produces the best cost as 4376 and the proposed hybrid intelligent approach IPSO-AIS produces the best cost as The proposed hybrid approach IPSO-AIS produces better results compared with other algorithms, but the convergence time for the proposed hybrid algorithm IPSO-AIS is higher (app 1.16 times) than IPSO-SA, because of the extra calculation involved in the immunization. The best cost obtained using the proposed hybrid intelligent approach IPSO-AIS for dataset 1 and dataset2 are shown in Figures 4.9 and Figure 4.9 Best costs for 50 tasks and 20 processors using IPSO, IPSO-SA and IPSO-AIS

20 98 Figure 4.10 Best costs for 100 tasks 20 processors using IPSO, IPSO-SA and IPSO-AIS The proposed hybrid intelligent approach IPSO-AIS performs better than with the IPSO and the hybrid algorithm IPSO-SA Performance Comparison The performance of the proposed hybrid approach IPSO-AIS is compared with the previously proposed ((Visalakshi and Sivanandam 2009) hybrid PSO algorithms PSO-HC and PSO-SA for the same datasets. Table 4.4 Performance comparison of various PSO based hybrid approaches Method PSO-HC (Visalakshi and Sivanandam 2009) PSO-SA (Visalakshi and Sivanandam 2009) Proposed IPSO-AIS Number of tasks Best cost Worst cost Average cost Convergence time in seconds

21 99 For the dataset 1, PSO-HC produces the best cost as 2322, PSO-SA produces the best cost as 2186 and the proposed hybrid approach IPSO-AIS produces the best cost as For dataset 2, PSO-HC produces the best cost as 4621, PSO-SA produces the best cost as 4496 and the proposed hybrid approach IPSO-AIS produces the best cost as The proposed hybrid intelligent approach IPSO-AIS performs better when compared with the other previously proposed hybrid methods of PSO-HC and PSO-SA. Thus, the result concludes that the proposed hybrid intelligent approach IPSO-AIS performs better than the other hybrid approaches PSO- HC and PSO-SA. 4.8 DYNAMIC TASK SCHEDULING WITH LOAD BALANCING In order to improve the performance and utilization of multiprocessor system, load balancing of tasks have to be considered. Therefore, the concept of load balancing is dealt, in which the objective function is the same as represented in the Equations (2.13) to (2.16) Results and Discussion Table 4.5 illustrates the best cost, worst cost, average cost and convergence time for, IPSO, hybrid algorithm IPSO-SA and the proposed hybrid intelligent algorithm IPSO-AIS. For dataset 1, the best cost achieved using IPSO is , IPSO- SA produces the best cost as and the proposed hybrid intelligent algorithm IPSO-AIS produces the best cost as For dataset 2, the best cost produced by IPSO is , IPSO-SA produces the best cost as and the proposed hybrid intelligent approach IPSO-AIS produces the best cost as The average cost is also improved in the proposed hybrid intelligent algorithm IPSO-AIS. The convergence time for the proposed

22 100 IPSO-AIS method is s for dataset 1 and s for dataset 2, which is higher than the hybrid algorithm IPSO-SA. Table 4.5 Best cost, worst cost, average cost and convergence time using IPSO, IPSO-SA and the proposed hybrid intelligent approach IPSO-AIS for dynamic task scheduling with load balancing Method IPSO IPSO-SA Proposed IPSO-AIS Number of tasks Best Cost Worst Cost Average Cost Convergence Time in seconds The best cost obtained using the intelligent hybrid algorithm IPSO-AIS for data set 1 and data set2 are shown in Figures 4.11 and Figure 4.11 Best costs for 50 tasks and 20 processors using IPSO, IPSO-SA and IPSO-AIS

23 101 Figure 4.12 Best costs for 100 tasks and 20 processors using IPSO, IPSO-SA and IPSO-AIS Thus, the results conclude that the proposed hybrid intelligent approach IPSO-AIS performs well when compared to the standard PSO, IPSO and IPSO-SA for the dynamic task scheduling problem with load balancing concept. However, the time taken for convergence is slightly (app 1.2 times) higher than IPSO-SA Performance Comparison The performance of the proposed IPSO is compared with the previously proposed (Visalakshi and Sivanandam 2009) hybrid PSO algorithms PSO-HC and PSO-SA for the same datasets. For the dataset 1, PSO-HC produces the best cost as , PSO- SA produces the best cost as and the proposed hybrid approach IPSO- AIS produces best cost as For dataset 2, PSO-HC produces best cost as , PSO-SA produces as the best cost and the proposed hybrid approach IPSO-AIS produces as the best cost. The proposed hybrid

24 102 intelligent approach IPSO-AIS perform well when compared with the other previously (Visalakshi and Sivanandam 2009) proposed hybrid methods PSO- HC and PSO-SA. Table 4.6 Performance comparison of various PSO based hybrid approaches Method PSO-HC (Visalakshi and Sivanandam 2009) PSO-SA (Visalakshi and Sivanandam 2009) Proposed IPSO-AIS Number of tasks Best cost Worst cost Average cost Convergence time in seconds Thus, the comparison reveals that the proposed hybrid approach IPSO-AIS achieves better results than the other approaches. 4.9 CONCLUSION The chapter four has thus dealt with the application of IPSO-AIS to solve different types of multiprocessor task scheduling with two cases, namely, static independent task scheduling and dynamic scheduling with and without load balancing. The proposed hybrid intelligent approach IPSO-AIS is tested with a static task scheduling problem to reduce both the total finishing time and average waiting time. For the dataset 5 processors with 60 tasks, IPSO produces an average waiting time of 36.56s and the total finishing time of 72.31s, IPSO-SA produces average waiting time of 32.76s and the total

25 103 finishing time of 69.13s. The proposed hybrid intelligent algorithm IPSO-AIS produces 30.19s as average waiting time and the total finishing time as 69.01s for the same dataset. Thus, IPSO-AIS reduce simultaneously both the total finishing time and average waiting time. The proposed hybrid intelligent approach IPSO-AIS is applied to dynamic task scheduling without load balancing problem. For dataset 2, IPSO produces the best cost as 4527, IPSO-SA produces the best cost as 4376 and the proposed hybrid intelligent approach IPSO-AIS produces The proposed hybrid intelligent approach IPSO-AIS is applied to dynamic task scheduling with load balancing problem. For dataset 2, the best cost produced by IPSO is , IPSO-SA produces the best cost as and the proposed hybrid approach IPSO-AIS produces the best cost as The proposed hybrid intelligent approach IPSO-AIS is compared with the other hybrid approaches which were earlier proposed, namely, PSO-HC and PSO-SA. The results infer that the proposed hybrid intelligent approach IPSO-AIS improves the performance of the scheduling. The proposed hybrid intelligent approach reduces the makespan for both static and dynamic task scheduling problems, but there is slight increase in the convergence time (app 1.15 times) when compared with IPSO-SA for dynamic task scheduling. Hence, other hybrid technologies need to be tried so that the convergence time is better than the methodologies tried out. Hence, new hybrid algorithms are proposed in the subsequent chapters to further refine the cost and the convergence time achieved, which is the main objective of task scheduling. The next chapter deals with the hybrid algorithm, Improved Particle Swarm Optimization with Ant Colony Optimization.