Oscar Brito Augusto

Multi-objective genetic algorithms: A way to improve the convergence rate

Abstract Multi-objective optimization is generally a time consuming step of the design process. In this paper, a Pareto based multi-objective genetic algorithm is proposed, which enables a faster convergence without degrading the estimated set of solutions. Indeed, the population diversity is correctly conserved during the optimization process; moreover, the solutions belonging to the frontier are equally distributed along the frontier. This improvement is due to an extension function based on a natural phenomenon, which is similar to a cyclical epidemic which happens every N generations (eN-MOGA). The use of this function enables a faster convergence of the algorithm by reducing the necessary number of generations.

A mixed continuous and discrete nonlinear constrained algorithm for optimizing ship hull structural design

A nonlinear search algorithm for optimizing constrained design of ship structures is presented. The decision variables can be continuous or discrete and the constraints can be homogeneous or inequality nonlinear functions of those variables. The algorithm does not use gradients; therefore, it can work with non-systematized functions such as tables or another class of design routine. It was tested in the structural design of a Patrol Boat and has proved to be a powerful tool decreasing the time expended in preliminary design when it is done by the conventional spiral approach.

A new method for decision making in multi-objective optimization problems

Many engineering sectors are challenged by multi-objective optimization problems. Even if the idea behind these problems is simple and well established, the implementation of any procedure to solve them is not a trivial task. The use of evolutionary algorithms to find candidate solutions is widespread. Usually they supply a discrete picture of the non-dominated solutions, a Pareto set. Although it is very interesting to know the non-dominated solutions, an additional criterion is needed to select one solution to be deployed. To better support the design process, this paper presents a new method of solving non-linear multi-objective optimization problems by adding a control function that will guide the optimization process over the Pareto set that does not need to be found explicitly. The proposed methodology differs from the classical methods that combine the objective functions in a single scale, and is based on a unique run of non-linear single-objective optimizers.

Anchor deployment for deep water floating offshore equipments

In this work a planning methodology for deep water anchor deployment in offshore platforms and floating production systems aiming at operational resources optimization is explored, by minimizing a multi criteria objective function. As an additional advantage provided by the proposed methodology, planning automation is achieved. Planning automation overcomes the traditional way, using a trial error basis. With it, an engineer, using an anchoring software, decides how much work wire and anchoring line must be paid out from both the floating system and the tug boat. Additionally, he decides which horizontal force must be applied to the line, trying to settle the anchor on a previously defined target on the ocean floor.

MULTIOBJECTIVE ENGINEERING DESIGN OPTIMIZATION PROBLEMS: A SENSITIVITY ANALYSIS APPROACH

This paper proposes two new approaches for the sensitivity analysis of multiobjective design optimization problems whose performance functions are highly susceptible to small variations in the design variables and/or design environment parameters. In both methods, the less sensitive design alternatives are preferred over others during the multiobjective optimization process. While taking the first approach, the designer chooses the design variable and/or parameter that causes uncertainties. The designer then associates a robustness index with each design alternative and adds each index as an objective function in the optimization problem. For the second approach, the designer must know, a priori, the interval of variation in the design variables or in the design environment parameters, because the designer will be accepting the interval of variation in the objective functions. The second method does not require any law of probability distribution of uncontrollable variations. Finally, the authors give two illustrative examples to highlight the contributions of the paper.

Toward the Use of Pareto Performance Solutions and Pareto Robustness Solutions for Multi-Objective Robust Optimization Problems

For Multi-Objective Robust Optimization Problem (MOROP), it is important to obtain design solutions that are both optimal and robust. To find these solutions, usually, the designer need to set a threshold of the variation of Performance Functions (PFs) before optimization, or add the effects of uncertainties on the original PFs to generate a new Pareto robust front. In this paper, we divide a MOROP into two Multi-Objective Optimization Problems (MOOPs). One is the original MOOP, another one is that we take the Robustness Functions (RFs), robust counterparts of the original PFs, as optimization objectives. After solving these two MOOPs separately, two sets of solutions come out, namely the Pareto Performance Solutions (PP) and the Pareto Robustness Solutions (PR). Make a further development on these two sets, we can get two types of solutions, namely the Pareto Robustness Solutions among the Pareto Performance Solutions (PR(PP)), and the Pareto Performance Solutions among the Pareto Robustness Solutions (PP(PR)). Further more, the intersection of PR(PP) and PP(PR) can represent the intersection of PR and PP well. Then the designer can choose good solutions by comparing the results of PR(PP) and PP(PR). Thanks to this method, we can find out the optimal and robust solutions without setting the threshold of the variation of PFs nor losing the initial Pareto front. Finally, an illustrative example highlights the contributions of the paper.Copyright

Otimização de recursos para a operação de instalação de âncoras de equipamentos offshore

In this work is explored a planning methodology for deep water anchor lines deployment in offshore platforms and floating production systems aiming operational resources optimization, by minimizing a multi criteria objective function. As an additional advantage, inherited from the proposed methodology, the planning automation is achieved. The planning automation overcomes the traditional way to do in a trial error basis, where an engineer, using a anchoring software, decides how much of work wire and anchoring line must be paid out from both the floating system and the supply vessel and additionally which horizontal force must be applied to the line trying settle the anchor on a previously defined target in the ocean floor.

Multiobjective Optimization Involving Quadratic Functions

Multiobjective optimization is nowadays a word of order in engineering projects. Although the idea involved is simple, the implementation of any procedure to solve a general problem is not an easy task. Evolutionary algorithms are widespread as a satisfactory technique to find a candidate set for the solution. Usually they supply a discrete picture of the Pareto front even if this front is continuous. In this paper we propose three methods for solving unconstrained multiobjective optimization problems involving quadratic functions. In the first, for biobjective optimization defined in the bidimensional space, a continuous Pareto set is found analytically. In the second, applicable to multiobjective optimization, a condition test is proposed to check if a point in the decision space is Pareto optimum or not and, in the third, with functions defined in n-dimensional space, a direct noniterative algorithm is proposed to find the Pareto set. Simple problems highlight the suitability of the proposed methods.

Anchor Deployment Planning Using Simulated Annealing

In this work a planning methodology for deep-water anchor deployment of anchor lines for offshore platforms and floating production systems aiming at operational resources optimization is explored, by minimizing a multi criteria objective function. A Simulated Annealing Algorithm was used to optimize the objective function. As an additional advantage, inherited from the proposed methodology, the planning automation is achieved. Planning automation overcomes the traditional way based on trial error exercise, where an engineer using an anchoring application, decides how much of work wire and anchoring line must be paid out from both the floating system and the supply boat and additionally which horizontal force must be applied to the line trying settle the anchor on a previously defined target in the ocean floor. Some cases, from anchor deployment of some MODUs operating in deep-water oil fields in Brazil, are shown demonstrating some potentialities of the proposed model.Copyright