This paper addresses the problem of application mapping onto Butterfly-Fat-Tree (BFT) based Network-on-Chip design. It proposes a new mapping technique based on discrete Particle Swarm Optimization (PSO) to map the cores of the core graph to the routers. The basic PSO has been augmented by running multiple PSO and deterministically generating a part of the initial population for PSO. The mappin...

The Orthogonal arrays are helpful in guiding the heuristic algorithms to obtain a good solution when applied to NP-hard problems. This chapter deals with a new variant of PSO named Orthogonal PSO (OPSO) for solving the multiprocessor scheduling problem. The objective of applying the orthogonal concept in the basic PSO algorithm is to enhance the performance when applied to the scheduling proble...

A branch and bound-PSO hybrid algorithm for solving integer separable concave programming problems is proposed, in which the lower bound of the optimal value was determined by solving linear programming relax and the upper bound of the optimal value and the best feasible solution at present were found and renewed with particle swarm optimization (PSO). It is shown by the numerical results that ...

The authors applied the particle swarm optimization (PSO) algorithm to solve the conditional nonlinear optimal perturbation (CNOP) and the lower bound of maximum predictable time (LBMPT). The results obtained by the PSO algorithm were compared to those by the traditional optimization algorithm (such as, a gradient descent algorithm based on the adjoint model, ADJ). The authors found that the PS...

Particle swarm optimization (PSO) algorithm can be viewed as a series of iterative matrix computation and its population diversity can be considered as an observation of the distribution of matrix elements. In this paper, PSO algorithm is first represented in the matrix format, then the PSO normalized population diversities are defined and discussed based on matrix analysis. Based on the analys...

Data clustering is a powerful technique for discerning the structure of and simplifying the complexity of large scale data. An improved technique combining chaotic map particle swarm optimization (CPSO) with an acceleration strategy, is proposed in this paper. Accelerated chaotic particle swarm optimization (ACPSO) searches for cluster centers of an arbitrary data set and can effectively find t...

The aim of this research is to design a PID Controller using particle swarm optimization (PSO) algorithm for multiple-input multiple output (MIMO) Takagi-Sugeno fuzzy model. The conventional gain tuning of PID controller (such as Ziegler-Nichols (ZN) method) usually produces a big overshoot, and therefore modern heuristics approach such as PSO are employed to enhance the capability of tradition...

Particle Swarm Optimization (PSO) is a population based stochastic optimization technique developed by Eberhart (1995) and Kennedy, inspired by the social behavior of bird flocking or fish schooling. PSO shares many similarities with evolutionary computation techniques such as GA. The system is initialized with a population of random solutions and searches for optima by updating generations. Ho...

The authors applied the particle swarm optimization (PSO) algorithm to solve the conditional nonlinear optimal perturbation (CNOP) and the lower bound of maximum predictable time (LBMPT). The results obtained by the PSO algorithm were compared to those by the traditional optimization algorithm (such as, a gradient descent algorithm based on the adjoint model, ADJ). The authors found that the PS...

In this paper, we empirically study the performance of the particle swarm optimizer (PSO). Four different benchmark functions with asymmetric initial range settings are selected as testing functions. The experimental results illustrate the advantages and disadvantages of the PSO. Under all the testing cases, the PSO always converges very quickly towards the optimal positions but may slow its co...