Response surface methodology is a common tool in optimizing processes. It mainly concerns situations when there is only one response of interest. However, many designed experiments often involve simultaneous optimization of several quality characteristics. This is called a Multiresponse Surface Optimization problem. A common approach in dealing with these problems is to apply desirability funct...

The paper describes an optimization method to support the conceptual stages of structural design. The method, called Blended Close Harmony Search, is a population-based metaheuristic approach that incorporates the following key features: 1) decision variable types that include real numbers defining geometry, discrete structural sections, and topology variables of enumeration (e.g. how many bays...

The synthesis of heat integrated multi-component distillation systems is complex due to its huge search space for structural combination and optimization computation. To provide a systematic approach and tools for the synthesis design of distillation systems, a new method for modeling heat integrated columns is presented, and the operating cost objective function is minimized by improved harmon...

This work addresses applications of the classical harmony search (HS), improved harmony search (IHS) and the harmony search with differential mutation based pith adjustment (HSDM) to PWR core reloading pattern optimization problems. Proper loading pattern of fuel assemblies (FAs) depends on both neutronic and thermal–hydraulic aspects; obtaining optimal arrangement of fuel assemblies, FA, in a ...

The Harmony Search is a relatively new meta-heuristic algorithm for continuous optimization, in which its concept imitates the process of music improvisation. This paper proposes an improved harmony search algorithm called Harmony Search with Adaptive Pitch Adjustment (HSAPA). The adaptive pitch adjustment scheme during a search period is inspired from the velocity clamping in particle swarm op...

Most engineering optimization algorithms are based on numerical linear and nonlinear programming methods that require substantial gradient information and usually seek to improve the solution in the neighborhood of a starting point. These algorithms, however, reveal a limited approach to complicated real-world optimization problems. If there is more than one local optimum in the problem, the re...

Mass optimization on shape and sizing with multiple natural frequency constraints are highly nonlinear dynamic optimization problems. Multiple natural frequency constraints normally cause difficult dynamic sensitivity analysis and, in addition, two different types of design variables, nodal coordinates and crosssectional areas, often lead to divergence. Thus, the choice of the appropriated meth...