Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/6541
Title: PARALLEL IMPLEMENTATION OF MULTI-OBJECTIVE PARTICLE SWARM OPTIMIZATION
Authors: Arun, Jambhlekar Pushkar
Keywords: ELECTRONICS AND COMPUTER ENGINEERING
PARALLEL IMPLEMENTATION
MULTI-OBJECTIVE PARTICLE SWARM OPTIMIZATION
PARTICLE SWARM OPTIMIZATION
Issue Date: 2011
Abstract: Nature has helped mankind in every fields and computer science is not an exception. - To solve compute intensive NP hard problems, it is required to obtain the solution in polynomial time. For such problems, algorithm as well as implementation need to be revised. For the implementation of such compute-intensive problems, supercomputing is a key solution, but it has limitations. Due to rapid advancement in computer science field, Moore law is now saturated, hence, it is essential to explore more and more options. Particle swarm optimization (PSO) is nature inspired algorithm, in which particles search solution in a given space by using personal and global experiences. PSO is iterative in nature, thus we can obtain the solution in polynomial time. PSO is proven to be efficient in multi-objective domain and can be used to find the solutions of Multi-Objective Problem (MOP) by storing non-dominated solutions. Complexity of the MOPSO algorithm depends on the fitness function and archiving method. Clusters are "loosely coupled systems", in which nodes are interconnected by high-speed LAN and communicate through message passing. "Tightly coupled systems" such as GPUs are set of streaming processors, who share memory and communication between them is carried through shared global memory. For parallel implementation of MOPSO, it follows parallel models defined for MOEA. However, parallel implementation of MOPSO is highly platform dependent. It is essential to know the performance of MOPSO on different platforms. In this report, implementations of MOPSO on GPU and cluster using multi-objective benchmark functions are explained. Novel archiving methods are proposed for parallel as well as serial implementation of MOPSO. Performances of parallel MOPSO on GPU and clusters are compared. Finally, hybrid parallel model of MOPSO for GPU cluster is proposed.
URI: http://hdl.handle.net/123456789/6541
Other Identifiers: M.Tech
Appears in Collections:MASTERS' DISSERTATIONS (E & C)

Files in This Item:
File Description SizeFormat 
ECED G20673.pdf4.34 MBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.