Intelligent Hybrid Taguchi-Genetic Algorithm for Multi-Criteria Optimization of Shaft Alignment in Marine Vessels

Abstract

An intelligent hybrid Taguchi-genetic algorithm (IHTGA) is used to optimize bearing offsets and shaft alignment in a marine vessel propulsion system. The objectives are to minimize normal shaft stress and shear force. The constraints are permissible reaction force, bearing stress, shear force, and bending moment in the shaft thrust flange under cold and hot operating conditions. Accurate alignment of the shaft for a main propulsion system is important for ensuring the safe operation of a vessel. To obtain a set of acceptable forces and stresses for the bearings and shaft under operating conditions, the optimal bearing offsets must be determined. Instead of the time-consuming classical local search methods with some trial-and-error procedures used in most shipyards to optimize bearing offsets, this paper used IHTGA. The proposed IHTGA performs Taguchi method between the crossover operation of the conventional GA. Incorporating the systematic reasoning ability of Taguchi method in the crossover operation enables intelligent selection of genes used to achieve crossover, which enhances the performance of the IHTGA in terms of robustness, statistical performance, and convergence speed. A penalty function method is performed using the fitness function as a pseudo-objective function comprising a linear combination of design objectives and constraints. A finite-element method is also used to determine the reaction forces and stresses in the bearings and to determine normal stresses, bending moments, and shear forces in the shaft. Computational experiments in a 2200 TEU container vessel show that the results obtained by the proposed IHTGA are significantly better than those obtained by the conventional local search methods with some trial-and-error procedures.INDEX TERMS Marine vessel propulsion system, bearing offsets, shaft alignment, optimal design, genetic algorithm.

Optimal design of reduced-order observers with specified eigenvalues and performance measurement of minimizing estimation errors using evolutionary optimization

“…For the parameter optimization problems of the complex nonlinear functions with continuous variables, it has been shown that the HTGA method can obtain improved results regarding robustness than existing approaches. 27,34 Thus, the HTGA method is used here to design the optimal NOFBreduced-order observers. The details regarding the HTGA can be found in Ho et al 27,34 The following evolutionary environments of the HTGA are used in this section: the population size is 30, the crossover rate is 0.95, the mutation rate is 0.01, and the maximum generation is 100.…”

“…27,34 Thus, the HTGA method is used here to design the optimal NOFBreduced-order observers. The details regarding the HTGA can be found in Ho et al 27,34 The following evolutionary environments of the HTGA are used in this section: the population size is 30, the crossover rate is 0.95, the mutation rate is 0.01, and the maximum generation is 100. The type of OF considered in this section is the shifted Chebyshev series.…”

Optimal Observer Design of State Delay Systems

“…The GA is adopted in this paper to deal with the complex problem of searching for the optimal observer gain matrix in the algebraic form of (24) subject to the constraint of there being a matrix such that the LMI in (13) holds, where 24is a complex nonlinear function having continuous variables. In addition, for the parameter optimization problems of complex nonlinear functions having continuous variables, it has been shown that the Hybrid Taguchi-Genetic Algorithm (HTGA) method can obtain better results than existing approaches [47]. The HTGA method is therefore employed in this paper to design the optimal observer for a time-delay system.…”

“…Step (HTGA method execution [47]). By integrating (19), (21), and (22), the HTGA method is utilized to search for the optimal observer gain matrix , where a penalty on the fitness value is given for the chromosome violating the LMI-based constraint in (13).…”

A fuzzy-genetic based design of permanent magnet synchronous motor

“…This algorithm has been applied to so many different electric motor design problems up to date [3][4][5]. In addition, the more strong algorithms constructed by combining the different artificial intelligence algorithms or other methods with genetic algorithm have been used [6][7][8][9]. The designs of permanent magnet synchronous motors are more complex and non-linear engineering problems and also contain some fuzzy facts such as other electric motor designs [10,11].…”