Ruey-Maw Chen

Using novel particle swarm optimization scheme to solve resource-constrained scheduling problem in PSPLIB

This investigation proposes an improved particle swam optimization (PSO) approach to solve the resource-constrained scheduling problem. Two proposed rules named delay local search rule and bidirectional scheduling rule for PSO to solve scheduling problem are proposed and evaluated. These two suggested rules applied in proposed PSO facilitate finding global minimum (minimum makespan). The delay local search enables some activities delayed and altering the decided start processing time, and being capable of escaping from local minimum. The bidirectional scheduling rule which combines forward and backward scheduling to expand the searching area in the solution space for obtaining potential optimal solution. Moreover, to speed up the production of feasible solution, a critical path is adopted in this study. The critical path method is used to generate heuristic value in scheduling process. The simulation results reveal that the proposed approach in this investigation is novel and efficient for resource-constrained class scheduling problem.

Multiprocessor system scheduling with precedence and resource constraints using an enhanced ant colony system

This study presents and evaluates a modified ant colony optimization (ACO) approach for the precedence and resource-constrained multiprocessor scheduling problems. A modified ant colony system is proposed to solve the scheduling problems. A two-dimensional matrix is proposed in this study for assigning jobs on processors, and it has a time-dependency relation structure. The dynamic rule is designed to modify the latest starting time of jobs and hence the heuristic function. In exploration of the search solution space, this investigation proposes a delay solution generation rule to escape the local optimal solution. Simulation results demonstrate that the proposed modified ant colony system algorithm provides an effective and efficient approach for solving multiprocessor system scheduling problems with resource constraints.

Scheduling multiprocessor job with resource and timing constraints using neural networks

The Hopfield neural network is extensively applied to obtaining an optimal/feasible solution in many different applications such as the traveling salesman problem (TSP), a typical discrete combinatorial problem. Although providing rapid convergence to the solution, TSP frequently converges to a local minimum. Stochastic simulated annealing is a highly effective means of obtaining an optimal solution capable of preventing the local minimum. This important feature is embedded into a Hopfield neural network to derive a new technique, i.e., mean field annealing. This work applies the Hopfield neural network and the normalized mean field annealing technique, respectively, to resolve a multiprocessor problem (known to be a NP-hard problem) with no process migration, constrained times (execution time and deadline) and limited resources. Simulation results demonstrate that the derived energy function works effectively for this class of problems.

Multiprocessor Task Assignment with Fuzzy Hopfield Neural Network Clustering Technique

Most scheduling applications have been demonstrated as NP-complete problems. A variety of schemes are introduced in solving those scheduling applications, such as linear programming, neural networks, and fuzzy logic. In this paper, a new approach of first analogising a scheduling problem to a clustering problem and then using a fuzzy Hopfield neural network clustering technique to solve the scheduling problem is proposed. This fuzzy Hopfield neural network algorithm integrates fuzzy c-means clustering strategies into a Hopfield neural network. This investigation utilises this new approach to demonstrate the feasibility of resolving a multiprocessor scheduling problem with no process migration and constrained times (execution time and deadline). Each process is regarded as a data sample, and every processor is taken as a cluster. Simulation results illustrate that imposing the fuzzy Hopfield neural network onto the proposed energy function provides an appropriate approach to solving this class of scheduling problem.

Solving University Course Timetabling Problems Using Constriction Particle Swarm Optimization with Local Search

Course timetabling is a combinatorial optimization problem and has been confirmed to be an NP-complete problem. Course timetabling problems are different for different universities. The studied university course timetabling problem involves hard constraints such as classroom, class curriculum, and other variables. Concurrently, some soft constraints need also to be considered, including teacher’s preferred time, favorite class time etc. These preferences correspond to satisfaction values obtained via questionnaires. Particle swarm optimization (PSO) is a promising scheme for solving NP-complete problems due to its fast convergence, fewer parameter settings and ability to fit dynamic environmental characteristics. Therefore, PSO was applied towards solving course timetabling problems in this work. To reduce the computational complexity, a timeslot was designated in a particle’s encoding as the scheduling unit. Two types of PSO, the inertia weight version and constriction version, were evaluated. Moreover, an interchange heuristic was utilized to explore the neighboring solution space to improve solution quality. Additionally, schedule conflicts are handled after a solution has been generated. Experimental results demonstrate that the proposed scheme of constriction PSO with interchange heuristic is able to generate satisfactory course timetables that meet the requirements of teachers and classes according to the various applied constraints.

An effective ant colony optimization-based algorithm for flow shop scheduling

This article presents a modified scheme named local search ant colony optimization algorithm on the basis of alternative ant colony optimization algorithm for solving flow shop scheduling problems. The flow shop problem (FSP) is confirmed to be an NP-hard sequencing scheduling problem, which has been studied by many researchers and applied to plenty of applications. Restated, the flow shop problem is hard to be solved in a reasonable time, therefore many meta-heuristics schemes proposed to obtain the optima or near optima solution efficiently. The ant colony optimization (ACO) is one of the well-applied meta-heuristics algorithms, nature inspired by the foraging behavior of real ants. Different implementations of state transition rules applied in ACO are studied in this work. Meanwhile, a local search mechanism was introduced to increase the probability of escaping from local optimal. Hence, this work integrates the local search mechanism into ant colony optimization algorithm for solving flow shop scheduling problem to improve the quality of solutions. Simulation results demonstrate that the applied ldquorandom orderrdquo state transition rule used in ACO with local search integrated is an effective scheme for the flow shop scheduling problems.

Medical image segmentation using mean field annealing network

This paper presents an unsupervised segmentation approach applying the mean field annealing (MFA) heuristic with the modified cost function. The idea is to cast a clustering problem as a minimization problem where the criteria for the optimum segmentation is chosen as the minimization of the Euclidean distance between samples to cluster centers. To resolve the optimal problem using a Hopfield or simulated annealing neural network, the penalty terms are combined into a weighted sum using several coefficients determined by user. Using the MFA network to medical image segmentation, the need for finding weighting factors in the energy function can be eliminated and the rate of convergence is much faster than that of simulated annealing. The experimental results show that good and valid solutions can be obtained using the MFA neural network.

Effective allied network security system based on designed scheme with conditional legitimate probability against distributed network attacks and intrusions

Dependence on the Internet is increasing dramatically. Therefore, many researchers have given great attention to the issue of how to tighten Internet security. This study proposes a new scheme for the distributed intrusion prevention system (DIPS), in which the concept of ‘union’ is presented for satisfying the increasing requirements of Internet security issues. In this proposed design, the network intrusion detection system (NIDS) applies a misuse detection technique to detect well-known intrusion behavior on the Internet. Meanwhile, for anomaly detection technique, a tool named ‘Scent’ (a network traffic sniffer) is combined with conditional legitimate probability to reveal previously undiscovered intrusion packets that do not match the intrusion signatures in NIDS. Moreover, blocking distributed denial-of-service (DDoS) attacks inside the protected allied network is also covered. To increase the detection accuracy, reduction of false positives and false negatives is also accomplished. Experimental results reveal that the suggested network security system scheme is effective and efficient in resolving the intrusion activity problem of real network environments. Copyright

Combined discrete particle swarm optimization and simulated annealing for grid computing scheduling problem

The grid scheduling problem is concerted with some tasks assigning to a grid distributed system that the relative tasks have to exchange information on different grids. In the original particle swarm optimization (PSO) algorithm, particles search solutions in a continuous solution space. Since the solution space of the grid scheduling problem is discrete. This paper presents a discrete particle swarm optimization (PSO) that combines the simulated annealing (SA) method to solve the grid scheduling problems. The proposed discrete PSO uses a population of particles through a discrete space on the basis of information about each particles local best solution and global best solution of all particles. For generating the next solution of each particle, the SA is adopted into the discrete PSO. The objective is to minimize the maximum cost of the grid, which includes computing cost and communication cost. Simulation results show that the grid scheduling problem can be solved efficiently by the proposed method.

Using particle swarm optimization to solve resource-constrained scheduling problems

This investigation introduced a particle swarm optimization (PSO) approach to solve the multi-processor resource-constrained scheduling problems. There are two new rules are proposed and evaluated, named anti-inertia solution generation rule and bidirectional searching rule of PSO. The anti-inertia solution generation rule enables some jobs with anti-inertia velocity used to decide the start processing time, and escaping from local minimum. The bidirectional searching rule combines forward and backward scheduling to extend the search solution space. These two suggested rules applied in PSO scheme are capable of finding global minimum. The simulation results reveal that the proposed approach in this investigation can successfully solve scheduling problems.