Ship Hull Structural Multiobjective Optimization by Evolutionary Algorithm

Abstract

An evolutionary algorithm for multiobjective optimization of the structural elements of the large spatial sections of ships is presented. The evolutionary algorithm where selection takes place based on the aggregated objective function combined with domination attributes as well as distance to the asymptotic solution is proposed and applied to solve the problem of optimizing structural elements with respect to their weight and surface area on a high-speed vehicle-passenger catamaran structure with several design variables such as plate thickness, scantlings of longitudinal stiffeners and transverse frames, and spacing between longitudinal and transversal structural members. Details of the computational models were at the level typical for conceptual design. Scantlings were analyzed using the selected rules of a classification society. The results of numerical experiments with the use of the developed algorithm are presented. They show that the proposed genetic algorithm can be a foundation of the effective multiobjective optimization tool for ship structure optimization. Further development of the tool should include more advanced methods for ship structural analysis.

Integration of Ship Hull Assembly Sequence Planning, Scheduling and Budgeting

“…Figure 7 presents the peak levels of the resource consumption achievable for every sequence. The peak resource consumption fits into the range [10,32]. The highest peak values (32) have 4 sequences shown in Figure 8.…”

“…peak Peak rc G rc ≤ t r (10) For the studied case 5 connections determined the number of the acceptable sequences depending on the values of the limiting parameters t F and rc Peak (Figure 10).…”

“…As a result, a complex product called the assembly is created. The specificity of the ship's structure is connected with a large number of elements [10]. The production of the ship's hull consists of a combination with welding methods in an inseparable way [13,14].…”

Metodología para el diseño a fatiga del reforzado transversal de un portacontenedores tipo Malacca Max

“…83 El método estocástico se basa en obtener los puntos globales óptimos mediante la dispersión de puntos al azar por todos los espacios de diseño. Existen dos enfoques dentro de este método, por un lado para el caso de una única función objetivo, el Algoritmo Genético [349] y la Estrategia Evolutiva [350] y, por otro lado, para el caso de funciones multiobjetivo, también se han desarrollado investigaciones acerca del Algoritmo Genético [351] y la Estrategia Evolutiva ( [352], [353] y [354]).…”