numerous problems encountered in real life cannot be actually formulated as a single objective problem; hence the requirement of multi-objective optimization (moo) had arisen several years ago. due to the complexities in such type of problems powerful heuristic techniques were needed, which has been strongly satisfied by swarm intelligence (si) techniques. particle swarm optimization (pso) has ...

The paper develops innovative methodology for robust and stable design of digital infinite impulse response (IIR) filters using a heuristic search method. The proposed heuristic search method enhances the capability to explore and exploit the search space locally as well globally to obtain optimal filter design parameters. A multicriterion optimization is employed as design criterion to obtain ...

In this paper, meta-heuristic optimization techniques are introduced and their applications to water resources engineering, particularly in hydrological science are introduced. In recent years, meta-heuristic optimization techniques have been introduced that can overcome the problems inherent in iterative simulations. These methods are able to find good solutions and require limited computation...

the present study is concerned with the simultaneous optimization of the size of components and the arrangement of connections for performance-based seismic design of low-rise spsws. design variables include the size of beams and columns, the thickness of the infill panels, the type of each beam-to-column connection and the type of each infill-to-boundary frame connection. the objective functio...

CS265 Semester Project Report Dan Bonachea, [email protected] Abstract Titanium is a parallel dialect of Java designed for writing high-performance scientific applications. In this paper, we discuss the design and implementation of a method inlining optimization added to the Titanium compiler. We address issues relevant to the design of any inlining optimization, and specifics relevant t...

The performance of heuristic algorithms for combinatorial optimization is often sensitive to problem instances. In extreme cases, a specialized heuristic algorithm may perform exceptionally well on a particular set of instances while fail to produce acceptable solutions on others. Such a problem-sensitive nature is most evident in algorithms for combinatorial optimization problems such as job s...