• International Journal of Naval Architecture and Ocean Engineering
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• 제8권6호
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• pp.602-614
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• 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.

• Structural Engineering and Mechanics
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• 제43권6호
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• pp.771-794
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• 2012

This paper presents a multi-objective evolutionary algorithm for combinatorial optimisation and applied for design optimisation of fiber reinforced composite structures. The proposed algorithm closely follows the implementation of Pareto Archive Evolutionary strategy (PAES) proposed in the literature. The modifications suggested include a customized neighbourhood search algorithm in place of mutation operator to improve intensification mechanism and a cross over operator to improve diversification mechanism. Further, an external archive is maintained to collect the historical Pareto optimal solutions. The design constraints are handled in this paper by treating them as additional objectives. Numerical studies have been carried out by solving a hybrid fiber reinforced laminate composite cylindrical shell, stiffened composite cylindrical shell and pressure vessel with varied number of design objectives. The studies presented in this paper clearly indicate that well spread Pareto optimal solutions can be obtained employing the proposed algorithm.

• Journal of Electrical Engineering and Technology
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• 제3권2호
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• pp.218-223
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• 2008

A new Pareto-optimal design algorithm, requiring least computational work, is proposed for a single phase AC solenoid actuator with multi-design-objectives: maximizing holding force and minimizing eddy current loss simultaneously. In the algorithm, the design space is successively reduced by a suitable factor, as iteration repeats, with the center of pseudo-optimal point. At each iteration, the objective functions are approximated to a simple second-order response surface with the CCD sampling points generated within the reduced design space, and Pareto-optimal solutions are obtained by applying($1+{\lambda}$) evolution strategy with the fitness values of Pareto strength.

• Journal of Information Technology Applications and Management
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• 제24권2호
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• pp.71-80
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• 2017

Software reliability is one of the most elementary and important problems in software development In order to find the software failure occurrence, the instantaneous failure rate function in the Poisson process can have a constant, incremental or decreasing tendency independently of the failure time. In this study, we compared the reliability performance of the software reliability model using the parameters of Pareto life distribution with the intensity decreasing pattern and the shape parameter of Erlang life distribution with the intensity increasing and decreasing pattern in the software product testing. In order to identify the software failure environment, the parametric estimation was applied to the maximum likelihood estimation method. Therefore, in this paper, we compare and evaluate software reliability by applying software failure time data. The reliability of the Erlang and Pareto life models is shown to be higher than that of the Pareto lifetime distribution model when the shape parameter is higher and the Erlang model is more reliable when the shape parameter is higher. Through this study, the software design department will be able to help the software design by applying various life distribution and shape parameters, and providing basic knowledge using software failure analysis.

• 한국지능시스템학회논문지
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• 제12권6호
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• pp.491-496
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• 2002

다목적 함수 최적화 문제(Multi-objective Optimization Problems : MOPs)는 공학적인 문제를 풀고자 할 때 자주 접하게 되는 대표적인 문제 중 하나이다. 공학자들이 다루는 실세계 최적화 문제들은 몇 개의 경합하는 목적 함수(objective function) 들로 이루어진 문제일 경우가 많다. 본 논문에서는 다목적 함수 최적화 문제의 정의를 소개하고 이 문제를 풀기 위한 몇 가지 접근법을 소개한다. 먼저 서론에서는 파레토 최적해(Pareto optimal solution) 의 개념을 이용한 기존의 최적화 알고리즘과 이와는 달리 게임 이론(Game Theory) 으로부터 도출된 최적화 알고리즘인 내쉬 유전자 알고리즘(Nash Genetic Algorithm Nash GA) 그리고 본 논문에서 제안하는 공진화 알고리즘의 기반이 되는 진화적 안정 전략 (Evolutionary Stable Strategy : ESS) 의 이론적 배경을 소개한다. 또 본론에서는 다목적 함수 최적화 문제와 파레토 최적 해의 정의를 소개하고 다목적 함수 최적화 문제를 풀기 위하여 유전자 알고리즘을 진화적 게임 이론(Evolutionary Game Theory : EGT) 에 적용시킨 내쉬 유전자 알고리즘과 본 논문에서 새로이 제안하는 공진화 알고리즘의 구조를 설명하고 이 두 가지 알고리즘을 대표적인 다목적 함수 최적화 문제에 적용하고 결과를 비교 검토함으로써 진화적 게임 이론의 두 가지 아이디어 내쉬의 균형(Equilibrium) 과 진화적 안정전략 에 기반한 최적화 알고리즘들이 다목적 함수 문제의 최적해 를 탐색할 수 있음을 확인한다.

• Journal of Magnetics
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• 제21권4호
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• pp.660-665
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• 2016

The electromagnetic apparatus plays an important role in high power electrical systems. It is of great importance to provide an effective approach for the optimization of the high power electromagnetic apparatus. However, premature convergence and few Pareto solution set of the optimization for electromagnetic apparatus always happen. This paper proposed a modified multi-objective particle swarm optimization algorithm based on the niche sorting strategy. Applying to the modified algorithm, this paper guarantee the better Pareto optimal front with an enhanced distribution. Aiming at shortcomings in the closing bounce and slow breaking velocity of electromagnetic apparatus, the multi-objective optimization model was established on the basis of the traditional optimization. Besides, by means of the improved multi-objective particle swarm optimization algorithm, this paper processed the model and obtained a series of optimized parameters (decision variables). Compared with other different classical algorithms, the modified algorithm has a satisfactory performance in the multi-objective optimization problems in the electromagnetic apparatus.

• International Journal of Reliability and Applications
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• 제3권2호
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• pp.61-80
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• 2002

Tree-harvesting worker data of 508 separate worker accidents are analyzed and an exploratory approach taken. The worker accident data cover a sample of five years. The scope of the study was the southeastern United States of America. As might be hypothesized, the chainsaw was the most hazardous type of tree-harvesting equipment. It accounted for 55％ of the tree-harvesting accidents. Most chainsaw accidents resulted in injuries to the lower extremities and were more frequent among younger employees. The probability of one or more chainsaw accidents occurring in any 30-day period was approximately 0.856. Chainsaw accidents were more likely to occur in late morning and early afternoon. We used statistical tools such as Pareto charts, c-charts and Ishikawa diagrams. Such tools are useful in diagnosing the root-cause of tree-harvesting worker accidents and help in developing preventive safety programs. Recommendations to help improve the quality of information of accident data collected by insurance companies and others are briefly given. The strategy and culture of continuous process improvements are stressed.

• Journal of Electrical Engineering and Technology
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• 제3권2호
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• pp.207-212
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• 2008

This paper presents a robust and computationally efficient optimal design algorithm for electromagnetic devices by combining an adaptive response surface approximation of the objective function and($1+{\lambda}$) evolution strategy. In the adaptive response surface approximation, the design space is successively reduced with the iteration, and Pareto-optimal sampling points are generated by using Latin hypercube design with the Max Distance and Min Distance criteria. The proposed algorithm is applied to an analytic example and TEAM problem 22, and its robustness and computational efficiency are investigated.

• International Journal of Control, Automation, and Systems
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• 제2권2호
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• pp.247-255
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• 2004

The majority of real-world problems encountered by engineers involve simultaneous optimization of competing objectives. In this case instead of single optima, there is a set of alternative trade-offs, generally known as Pareto-optimal solutions. The use of evolutionary algorithms Pareto GA, which was first introduced by Goldberg in 1989, has now become a sort of standard in solving Multiobjective Optimization Problems (MOPs). Though this approach was further developed leading to numerous applications, these applications are based on Pareto ranking and employ the use of the fitness sharing function to maintain diversity. Another scheme for solving MOPs has been presented by J. Nash to solve MOPs originated from Game Theory and Economics. Sefrioui introduced the Nash Genetic Algorithm in 1998. This approach combines genetic algorithms with Nash's idea. Another central achievement of Game Theory is the introduction of an Evolutionary Stable Strategy, introduced by Maynard Smith in 1982. In this paper, we will try to find ESS as a solution of MOPs using our game model based co-evolutionary algorithm. First, we will investigate the validity of our co-evolutionary approach to solve MOPs. That is, we will demonstrate how the evolutionary game can be embodied using co-evolutionary algorithms and also confirm whether it can reach the optimal equilibrium point of a MOP. Second, we will evaluate the effectiveness of our approach, comparing it with other methods through rigorous experiments on several MOPs.

• Structural Engineering and Mechanics
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• 제3권4호
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• pp.373-382
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• 1995

The weighting strategy has received a great attention and has been widely applied to multicriterion optimization. This gaper examines a global criterion method (GCM) with the weighting objectives strategy in fuzzy structural engineering problems. Fuzziness of those problems are in their design goals, constraints and variables. Most of the constraints are originated from analysis of engineering mechanics. The GCM is verified to be equivalent to fuzzy goal programming via a truss design. Continued and mixed discrete variable spaces are presented and examined using a fuzzy global criterion method (FGCM). In the design process a weighting parameter with fuzzy information is introduced into the design and decision making. We use a uniform machine-tool spindle as an illustrative example in continuous design space. Fuzzy multicriterion optimization in mixed design space is illustrated by the design of mechanical spring stacks. Results show that weighting strategy in FGCM can generate both the best compromise solution and a set of Pareto solutions in fuzzy environment. Weighting technique with fuzziness provides a more relaxed design domain, which increases the satisfying degree of a compromise solution or improves the final design.