Optimized Load Balancing based Task Scheduling in Cloud Environment

The fundamental issue of Task scheduling is one important factor to load balance between the virtual machines in a Cloud Computing network. However, the optimal broadcast methods which have been proposed so far focus only on cluster or grid environment. In this paper, task scheduling strategy based on load balancing Quantum Particles Swarm algorithm (BLQPSO) was proposed. The fitness function based minimizing the makespan and data transmission cost. In addition, the salient feature of this algorithm is to optimize node available throughput dynamically using MatLab10A software. Furthermore, the performance of proposed algorithm had been compared with existing PSO and shows their effectiveness in balancing the load

Memetic chicken swarm algorithm for job shop scheduling problem

This paper presents a Memetic Chicken swarm optimization (MeCSO) to solve job shop scheduling problem (JSSP). The aim is to find a better solution which minimizes the maximum of the completion time also called Makespan. In this paper, we adapt the chicken swarm algorithm which take into consideration the hierarchical order of chicken swarm while seeking for food. Moreover, we integrate 2-opt method to improve the movement of the rooster. The new algorithm is applied on some instances of ORLibrary. The empirical results show the forcefulness of MeCSO comparing to other metaheuristics from literature in term of run time and quality of solution

Swarm intelligence for scheduling: a review

Swarm Intelligence generally refers to a problem-solving ability that emerges from the interaction of simple information-processing units. The concept of Swarm suggests multiplicity, distribution, stochasticity, randomness, and messiness. The concept of Intelligence suggests that problem-solving approach is successful considering learning, creativity, cognition capabilities. This paper introduces some of the theoretical foundations, the biological motivation and fundamental aspects of swarm intelligence based optimization techniques such Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO) and Artificial Bees Colony (ABC) algorithms for scheduling optimization

Development of a Neural Network Algorithm for Estimating the Makespan in Jobshop Production Scheduling

Since production scheduling is considered a short-term plan for future production planning, the advantages of effective scheduling and control and their contribution to the production process are numerous. Efficient use of resources improves productivity and ensures that customer orders are met on time. Even the simplest scheduling system has a complex solution structure. Long lead times also make it difficult to estimate the demand accurately. Therefore, it is important to solve scheduling problems effectively for such difficult-to-manage production processes. Job shop scheduling (JSS) problems are among the combinatorial problems in the NP-hard problems class. As constraints increase in such problems, the solution space starts to go to infinity, making it increasingly difficult to find the exact optimum solution. For this reason, metaheuristic algorithms have been used to solve such problems in recent years. This study aims to develop an artificial neural network (ANN)-based application to produce an optimal or near-optimal solution for JSS. Using the job shop type production data of Taillard comparison problems, the total processing time (i.e., makespan) has been calculated with the proposed ANN application. The results have been compared with the results of related studies in the literature, and the algorithm\u27s efficiency has been evaluated in detail

Artificial intelligence effectiveness in job shop environments

The aim of this paper is to define a new methodology that allows the comparison of the effectiveness among some of the major artificial intelligence techniques (random technique, taboo search, data mining, evolutionary algorithms). This methodology is applied in the sequencing production process in job shop environments, in a problem with N orders, and M machines, where each of the orders must pass through every machine regardless of its turn. These techniques are measured by the variables of total makespan time, total idle time, and machine utilization percentage. Initially, a theoretical review was conducted and showed the usefulness and effectiveness of artificial intelligence in the sequencing production processes. Subsequently and based on the experiments presented, the obtained results showed that these techniques have an effectiveness higher than 95%, with a confidence interval of 99.5% measured by the variables under study

A Particle Swarm Optimisation for Vehicle Routing Problem with Time Windows

A heuristic based on Particle Swarm Optimisation (PSO) algorithm for solving VRPTW, which is an extension of PSO application for the Capacitated Vehicle Routing Problem (CVRP) (Ai and Kachitvichyanukul,2007), is presented in this paper. A computational experiment is carried out by running the proposed algorithm with the VRPTW benchmark data set of Solomon (1987). The results show that the proposed algorithm is able to provide VRPTW solutions that are very close to its optimal solutions for problems with 25 and 50 customers within reasonably short of computational tim

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