In solving engineering constrained optimization problems, the conventional black widow algorithm (BWOA) has some shortcomings such as insufficient robustness and slow convergence speed. Therefore, an improved (IBWOA) is proposed by combining methods of double chaotic map, Cauchy center gravity inverse difference mutation golden sine guidance strategy. Firstly, quality initial population BWOA ba...

Real world engineering design problems are usually characterized by the presence of many conflicting objectives. Therefore, it is natural to look at the engineering design problem as a multiobjective optimization problem. This report summarizes a survey of techniques to conduct multiobjective optimization in an engineering design context. The report starts with discussing some of the difficulti...

Virtual engineering utilizes various computer–based simulation techniques for the analysis of complex systems such as mechanical structures, environmental systems, production processes, and even whole production plants. One main goal of virtual engineering is to find the optimum for mechanical structures, facilities, or production processes in terms of cost, time, energy consumption etc. Hence,...

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...

THIS THESIS FOCUSES on how to improve design and development of complex engineering systems by employing simulation and optimization techniques. Within the thesis, methods are developed and applied to systems that combine mechanical, hydraulical and electrical subsystems, so-called multi-domain systems. Studied systems include a landing gear system for a civil aircraft, electro-hydrostatic actu...

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...