• Ocean Systems Engineering
• /
• v.5 no.2
• /
• pp.91-107
• /
• 2015

Ship hull optimization is categorized as a bound, multi variable, multi objective problem with nonlinear constraints. In such analysis, where the objective function representing the performance of the ship generally requires computationally involved hydrodynamic interaction evaluation methods, the objective functions are not smooth. Hence, the evolutionary techniques to attain the optimum hull forms is considered as the most practical strategy. In this study, a parametric ship hull form represented by B-Spline curves is optimized for multiple performance criteria using Genetic Algorithm. The methodology applied to automate the hull form generation, selection of optimization solvers and hydrodynamic parameter calculation for objective function and constraint definition are discussed here.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.17 no.4
• /
• pp.1100-1122
• /
• 2023

Scheduling user-submitted cloud tasks to the appropriate virtual machine (VM) in cloud computing is critical for cloud providers. However, as the demand for cloud resources from user tasks continues to grow, current evolutionary algorithms (EAs) cannot satisfy the optimal solution of large-scale cloud task scheduling problems. In this paper, we first construct a large- scale multi-objective cloud task problem considering the time and cost functions. Second, a multi-objective optimization algorithm based on multi-factor optimization (MFO) is proposed to solve the established problem. This algorithm solves by decomposing the large-scale optimization problem into multiple optimization subproblems. This reduces the computational burden of the algorithm. Later, the introduction of the MFO strategy provides the algorithm with a parallel evolutionary paradigm for multiple subpopulations of implicit knowledge transfer. Finally, simulation experiments and comparisons are performed on a large-scale task scheduling test set on the CloudSim platform. Experimental results show that our algorithm can obtain the best scheduling solution while maintaining good results of the objective function compared with other optimization algorithms.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.44 no.2
• /
• pp.36-42
• /
• 2021

This paper deals with a vehicle routing problem with resource repositioning (VRPRR) which is a variation of well-known vehicle routing problem with pickup and delivery (VRPPD). VRPRR in which static repositioning of public bikes is a representative case, can be defined as a multi-objective optimization problem aiming at minimizing both transportation cost and the amount of unmet demand. To obtain Pareto sets for the problem, famous multi-objective optimization algorithms such as Strength Pareto Evolutionary Algorithm 2 (SPEA2) can be applied. In addition, a linear combination of two objective functions with weights can be exploited to generate Pareto sets. By varying weight values in the combined single objective function, a set of solutions is created. Experiments accomplished with a standard benchmark problem sets show that Variable Neighborhood Search (VNS) applied to solve a number of single objective function outperforms SPEA2. All generated solutions from SPEA2 are completely dominated by a set of VNS solutions. It seems that local optimization technique inherent in VNS makes it possible to generate near optimal solutions for the single objective function. Also, it shows that trade-off between the number of solutions in Pareto set and the computation time should be considered to obtain good solutions effectively in case of linearly combined single objective function.

• 한국가스학회:학술대회논문집
• /
• 1997.09a
• /
• pp.115-120
• /
• 1997

• Journal of Computational Design and Engineering
• /
• v.3 no.3
• /
• pp.179-190
• /
• 2016

A kind of modified epoxy resin sheet molding compounds of the impeller has been designed. Through the test, the non-metal impeller has a better environmental aging performance, but must do the waterproof processing design. In order to improve the stability of the impeller vibration design, the influence of uncertainty factors is considered, and a multi-objective robust optimization method is proposed to reduce the weight of the impeller. Firstly, based on the fluid-structure interaction, the analysis model of the impeller vibration is constructed. Secondly, the optimal approximate model of the impeller is constructed by using the Latin hypercube and radial basis function, and the fitting and optimization accuracy of the approximate model is improved by increasing the sample points. Finally, the micro multi-objective genetic algorithm is applied to the robust optimization of approximate model, and the Monte Carlo simulation and Sobol sampling techniques are used for reliability analysis. By comparing the results of the deterministic, different sigma levels and different materials, the multi-objective optimization of the SMC molding impeller can meet the requirements of engineering stability and lightweight. And the effectiveness of the proposed multi-objective robust optimization method is verified by the error analysis. After the SMC molding and the robust optimization of the impeller, the optimized rate reached 42.5%, which greatly improved the economic benefit, and greatly reduce the vibration of the ventilation system.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.45 no.4
• /
• pp.109-117
• /
• 2022

Most of real-world decision-making processes are used to optimize problems with many objectives of conflicting. Since the betterment of some objectives requires the sacrifice of other objectives, different objectives may not be optimized simultaneously. Consequently, Pareto solution can be considered as candidates of a solution with respect to a multi-objective optimization (MOP). Such problem involves two main procedures: finding Pareto solutions and choosing one solution among them. So-called multi-objective genetic algorithms have been proved to be effective for finding many Pareto solutions. In this study, we suggest a fitness evaluation method based on the achievement level up to the target value to improve the solution search performance by the multi-objective genetic algorithm. Using numerical examples and benchmark problems, we compare the proposed method, which considers the achievement level, with conventional Pareto ranking methods. Based on the comparison, it is verified that the proposed method can generate a highly convergent and diverse solution set. Most of the existing multi-objective genetic algorithms mainly focus on finding solutions, however the ultimate aim of MOP is not to find the entire set of Pareto solutions, but to choose one solution among many obtained solutions. We further propose an interactive decision-making process based on a visualized trade-off analysis that incorporates the satisfaction of the decision maker. The findings of the study will serve as a reference to build a multi-objective decision-making support system.

• International Journal of Naval Architecture and Ocean Engineering
• /
• v.8 no.6
• /
• pp.602-614
• /
• 2016

It is necessary to develop an efficient optimization technique to perform optimum designs which have given design spaces, discrete design values and several design goals. As optimization techniques, direct search method and stochastic search method are widely used in designing of ship structures. The merit of the direct search method is to search the optimum points rapidly by considering the search direction, step size and convergence limit. And the merit of the stochastic search method is to obtain the global optimum points well by spreading points randomly entire the design spaces. In this paper, Pareto Strategy (PS) multi-objective function method is developed by considering the search direction based on Pareto optimal points, the step size, the convergence limit and the random number generation. The success points between just before and current Pareto optimal points are considered. PS method can also apply to the single objective function problems, and can consider the discrete design variables such as plate thickness, longitudinal space, web height and web space. The optimum design results are compared with existing Random Search (RS) multi-objective function method and Evolutionary Strategy (ES) multi-objective function method by performing the optimum designs of double bottom structure and double hull tanker which have discrete design values. Its superiority and effectiveness are shown by comparing the optimum results with those of RS method and ES method.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.13 no.9
• /
• pp.4329-4348
• /
• 2019

Mobile cloud computing (MCC) can offload heavy computation from mobile devices onto nearby cloudlets or remote cloud to improve the performance as well as to save energy for these devices. Therefore, it is essential to consider how to achieve efficient computation offloading with constraints for multiple users. However, there are few works that aim at multi-objective problem for multiple users. Most existing works concentrate on only single objective optimization or aim to obtain a tradeoff solution for multiple objectives by simply setting weight values. In this paper, a multi-objective optimization model is built to minimize the average energy consumption, time and cost while satisfying the constraint of bandwidth. Furthermore, an improved multi-objective optimization algorithm called D-NSGA-II-ELS is presented to get Pareto solutions with better convergence and diversity. Compared to other existing works, the simulation results show that the proposed algorithm can achieve better performance in terms of energy consumption, time and cost while satisfying the constraint of the bandwidth.

• Journal of Electrical Engineering and Technology
• /
• v.4 no.4
• /
• pp.467-475
• /
• 2009

Congestion management is one of the technical challenges in power system deregulation. This paper presents single objective and multi-objective optimization approaches for optimal choice, location and size of Static Var Compensators (SVC) and Thyristor Controlled Series Capacitors (TCSC) in deregulated power system to improve branch loading (minimize congestion), improve voltage stability and reduce line losses. Though FACTS controllers offer many advantages, their installation cost is very high. Hence Independent System Operator (ISO) has to locate them optimally to satisfy a desired objective. This paper presents optimal location of FACTS controllers considering branch loading (BL), voltage stability (VS) and loss minimization (LM) as objectives at once using GA. It is observed that the locations that are most favorable with respect to one objective are not suitable locations with respect to other two objectives. Later these competing objectives are optimized simultaneously considering two and three objectives at a time using multi-objective Strength Pareto Evolutionary Algorithms (SPEA). The developed algorithms are tested on IEEE 30 bus system. Various cases like i) uniform line loading ii) line outage iii) bilateral and multilateral transactions between source and sink nodes have been considered to create congestion in the system. The developed algorithms show effective locations for all the cases considered for both single and multiobjective optimization studies.

• Steel and Composite Structures
• /
• v.32 no.4
• /
• pp.455-466
• /
• 2019

Despite the rapid advancement in computing resources, many real-life design and optimization problems in structural engineering involve huge computation costs. To counter such challenges, approximate models are often used as surrogates for the highly accurate but time intensive finite element models. In this paper, surrogates for first-order shear deformation based finite element models are built using a polynomial regression approach. Using statistical techniques like Box-Cox transformation and ANOVA, the effectiveness of the surrogates is enhanced. The accuracy of the surrogate models is evaluated using statistical metrics like $R^2$, $R^2{_{adj}}$, $R^2{_{pred}}$ and $Q^2{_{F3}}$. By combining these surrogates with nature-inspired multi-criteria decision-making algorithms, namely multi-objective genetic algorithm (MOGA) and multi-objective particle swarm optimization (MOPSO), the optimal combination of various design variables to simultaneously maximize fundamental frequency and frequency separation is predicted. It is seen that the proposed approach is simple, effective and good at inexpensively producing a host of optimal solutions.