Quantum behaved Particle Swarm Optimization (QPSO) for multi-objective design optimization of composite structures

We present a new, generic method/model for multi-objective design optimization of laminated composite components using a novel multi-objective optimization algorithm developed on the basis of the Quantum behaved Particle Swarm Optimization (QPSO) paradigm. QPSO is a co-variant of the popular Particle Swarm Optimization (PSO) and has been developed and implemented successfully for the multi-objective design optimization of composites. The problem is formulated with multiple objectives of minimizing weight and the total cost of the composite component to achieve a specified strength. The primary optimization variables are - the number of layers, its stacking sequence (the orientation of the layers) and thickness of each layer. The classical lamination theory is utilized to determine the stresses in the component and the design is evaluated based on three failure criteria; Failure Mechanism based Failure criteria, Maximum stress failure criteria and the Tsai-Wu Failure criteria. The optimization method is validated for a number of different loading configurations - uniaxial, biaxial and bending loads. The design optimization has been carried for both variable stacking sequences as well as fixed standard stacking schemes and a comparative study of the different design configurations evolved has been presented. Also, the performance of QPSO is compared with the conventional PSO.

Pole placement in multi-input systems via elementary transformations

This paper is concerned with the development of an algorithm for pole placement in multi-input dynamic systems. The algorithm which uses a series of elementary transformations is believed to be simpler, computationally more efficient and numerically stable when compared with earlier methods. In this paper two methods have been presented.

A parallel-serial binary arbitration scheme for collision-free multi-access techniques

Multi-access techniques are widely used in computer networking and distributed multiprocessor systems. On-the-fly arbitration schemes permit one of the many contenders to access the medium without collisions. Serial arbitration is cost effective but is slow and hence unsuitable for high-speed multiprocessor environments supporting very high data transfer rates. A fully parallel arbitration scheme takes less time but is not practically realisable for large numbers of contenders. In this paper, a generalised parallel-serial scheme is proposed which significantly reduces the arbitration time and is practically realisable.

Distributed Scheduling for Multi-Hop Wireless Networks

We study the performance of greedy scheduling in multihop wireless networks where the objective is aggregate utility maximization. Following standard approaches, we consider the dual of the original optimization problem. Optimal scheduling requires selecting independent sets of maximum aggregate price, but this problem is known to be NP-hard. We propose and evaluate a simple greedy heuristic. We suggest how the greedy heuristic can be implemented in a distributed manner. We evaluate an analytical bound in detail, for the special case of a line graph and also provide a loose bound on the greedy heuristic for the case of an arbitrary graph.

Multi-time scale analysis applied to long time simulation of power systems

The results are presented of applying multi-time scale analysis using the singular perturbation technique for long time simulation of power system problems. A linear system represented in state-space form can be decoupled into slow and fast subsystems. These subsystems can be simulated with different time steps and then recombined to obtain the system response. Simulation results with a two-time scale analysis of a power system show a large saving in computational costs.

Robustness of the partitions in grouping multi-item inventories

A recent work obtained closed-form solutions to the.problem of optimally grouping a multi-item inventory into subgroups with a common order cycle per group, when the distribution by value of the inventory could be described by a Pareto function. This paper studies the sensitivity of the optimal subgroup boundaries so obtained. Closed-form expressions have been developed to find intervals for the subgroup boundaries for any given level of suboptimality. Graphs have been provided to aid the user in selecting a cost-effective level of aggregation and choosing appropriate subgroup boundaries for a whole range of inventory distributions. The results of sensitivity analyses demonstrate the availability of flexibility in the partition boundaries and the cost-effectiveness of any stock control system through three groups, and thus also provide a theoretical support to the intuitive ABC system of classifying the items.