• 제목/요약/키워드: Pareto front

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Evolutionary Multi-Objective Optimization Algorithms for Uniform Distributed Pareto Optimal Solutions (균일분포의 파레토 최적해 생성을 위한 다목적 최적화 진화 알고리즘)

  • Jang Su-Hyun;Yoon Byungjoo
    • The KIPS Transactions:PartB
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    • v.11B no.7 s.96
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    • pp.841-848
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    • 2004
  • Evolutionary a1gorithms are well-suited for multi-objective optimization problems involving several, often conflicting objectives. Pareto-based evolutionary algorithms, in particular, have shown better performance than other multi-objective evolutionary algorithms in comparison. However, generalized evolutionary multi-objective optimization algorithms have a weak point, in which the distribution of solutions are not uni-formly distributed onto Pareto optimal front. In this paper, we propose an evolutionary a1gorithm for multi-objective optimization which uses seed individuals in order to overcome weakness of algorithms Published. Seed individual means a solution which is not located in the crowded region on Pareto front. And the idea of our algorithm uses seed individuals for reproducing individuals for next generation. Thus, proposed a1go-rithm takes advantage of local searching effect because new individuals are produced near the seed individual with high probability, and is able to produce comparatively uniform distributed pareto optimal solutions. Simulation results on five testbed problems show that the proposed algo-rithm could produce uniform distributed solutions onto pareto optimal front, and is able to show better convergence compared to NSGA-II on all testbed problems except multi-modal problem.

Optimal Controller Design for Single-Phase PFC Rectifiers Using SPEA Multi-Objective Optimization

  • Amirahmadi, Ahmadreza;Dastfan, Ali;Rafiei, Mohammadreza
    • Journal of Power Electronics
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    • v.12 no.1
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    • pp.104-112
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    • 2012
  • In this paper a new method for the design of a simple PI controller is presented and it has been applied in the control of a Boost based PFC rectifier. The Strength Pareto evolutionary algorithm, which is based on the Pareto Optimality concept, used in Game theory literature is implemented as a multi-objective optimization approach to gain a good transient response and a high quality input current. In the proposed method, the input current harmonics and the dynamic response have been assumed as objective functions, while the PI controller's gains of the PFC rectifier (Kpi, Tpi) are design variables. The proposed algorithm generates a set of optimal gains called a Pareto Set corresponding to a Pareto Front, which is a set of optimal results for the objective functions. All of the Pareto Front points are optimum, but according to the design priority objective function, each one can be selected. Simulation and experimental results are presented to prove the superiority of the proposed design methodology over other methods.

Pareto Optimal Design of the Vehicle Body (차체의 팔렛토 최적 설계)

  • Kim, Byoung-Gon;Chung, Tae-Jin;Lee, Jeong-Ick
    • Transactions of the Korean Society of Machine Tool Engineers
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    • v.17 no.4
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    • pp.67-74
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    • 2008
  • The important dynamic specifications in the aluminum automobile body design are the vibrations and crashworthiness in the views of ride comforts and safety. Thus, considerable effort has been invested into improving the performance of mechanical structures comprised of the interactive multiple sub-structures. Most mechanical structures are complex and are essentially multi-criteria optimization problems with objective functions retained as constraints. Each weight factor can be defined according to the effects and priorities among objective functions, and a feasible Pareto-optimal solution exists for the criteria-defined constraints. In this paper, a multi-criteria design based on the Pareto-optimal sensitivity is applied to the vibration qualities and crushing characteristics of front structure in the automobile body design. The vibration qualities include the idle, wheel unbalance and road shake. The crushing characteristic of front structure is the axial maximum peak load.

OPTIMIZATION OF THE TEST INTERVALS OF A NUCLEAR SAFETY SYSTEM BY GENETIC ALGORITHMS, SOLUTION CLUSTERING AND FUZZY PREFERENCE ASSIGNMENT

  • Zio, E.;Bazzo, R.
    • Nuclear Engineering and Technology
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    • v.42 no.4
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    • pp.414-425
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    • 2010
  • In this paper, a procedure is developed for identifying a number of representative solutions manageable for decision-making in a multiobjective optimization problem concerning the test intervals of the components of a safety system of a nuclear power plant. Pareto Front solutions are identified by a genetic algorithm and then clustered by subtractive clustering into "families". On the basis of the decision maker's preferences, each family is then synthetically represented by a "head of the family" solution. This is done by introducing a scoring system that ranks the solutions with respect to the different objectives: a fuzzy preference assignment is employed to this purpose. Level Diagrams are then used to represent, analyze and interpret the Pareto Fronts reduced to the head-of-the-family solutions.

NSGA-II Technique for Multi-objective Generation Dispatch of Thermal Generators with Nonsmooth Fuel Cost Functions

  • Rajkumar, M.;Mahadevan, K.;Kannan, S.;Baskar, S.
    • Journal of Electrical Engineering and Technology
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    • v.9 no.2
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    • pp.423-432
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    • 2014
  • Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is applied for solving Combined Economic Emission Dispatch (CEED) problem with valve-point loading of thermal generators. This CEED problem with valve-point loading is a nonlinear, constrained multi-objective optimization problem, with power balance and generator capacity constraints. The valve-point loading introduce ripples in the input-output characteristics of generating units and make the CEED problem as a nonsmooth optimization problem. To validate its effectiveness of NSGA-II, two benchmark test systems, IEEE 30-bus and IEEE 118-bus systems are considered. To compare the Pareto-front obtained using NSGA-II, reference Pareto-front is generated using multiple runs of Real Coded Genetic Algorithm (RCGA) with weighted sum of objectives. Comparison with other optimization techniques showed the superiority of the NSGA-II approach and confirmed its potential for solving the CEED problem. Numerical results show that NSGA-II algorithm can provide Pareto-front in a single run with good diversity and convergence. An approach based on Technique for Ordering Preferences by Similarity to Ideal Solution (TOPSIS) is applied on non-dominated solutions obtained to determine Best Compromise Solution (BCS).

Development of Pareto Artificial Life Optimization Algorithm (파레토 인공생명 최적화 알고리듬의 제안)

  • Song, Jin-Dae;Yang, Bo-Suk
    • Transactions of the Korean Society of Mechanical Engineers A
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    • v.30 no.11 s.254
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    • pp.1358-1368
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    • 2006
  • This paper proposes a Pareto artificial life algorithm for solving multi-objective optimization problems. The artificial life algorithm for optimization problem with a single objective function is improved to handle Pareto optimization problem through incorporating the new method to estimate the fitness value for a solution and the Pareto list to memorize and to improve the Pareto optimal set. The proposed algorithm was applied to the optimum design of a journal bearing which has two objective functions. The Pareto front and the optimal solution set for the application were presented to give the possible solutions to a decision maker or a designer. Furthermore, the relation between linearly combined single-objective optimization problem and Pareto optimization problem has been studied.

Optimum Design of Integer and Fractional-Order PID Controllers for Boost Converter Using SPEA Look-up Tables

  • Amirahmadi, Ahmadreza;Rafiei, Mohammadreza;Tehrani, Kambiz;Griva, Giovanni;Batarseh, Issa
    • Journal of Power Electronics
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    • v.15 no.1
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    • pp.160-176
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    • 2015
  • This paper presents a method of designing optimal integer- and fractional-order proportional-integral-derivative (FOPID) controllers for a boost converter to gain a set of favorable characteristics at various operating points. A Pareto-based multi-objective optimization approach called strength Pareto evolutionary algorithm (SPEA) is used to obtain fast and low overshoot start-up and dynamic responses and switching stability. The optimization approach generates a set of optimal gains called Pareto set, which corresponds to a Pareto front. The Pareto front is a set of optimal results for objective functions. These results provide designers with a trade-off look-up table, in which they can easily choose any of the optimal gains based on design requirements. The SPEA also overcomes the difficulties of tuning the FOPID controller, which is an extension to the classic integer-order PID controllers and potentially promises better results. The proposed optimized FOPID controller provides an excellent start-up response and the desired dynamic response. This paper presents a detailed comparison of the optimum integer- and the fractional-order PID controllers. Extensive simulation and experimental results prove the superiority of the proposed design methodology to achieve a wide set of desired technical goals.

Pareto optimum design of journal bearings by artificial life algorithm (인공생명최적화알고리듬에 의한 저널베어링의 파레토 최적화)

  • Song, Jin-Dae;Yang, Bo-Suk
    • Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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    • 2005.11a
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    • pp.869-874
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    • 2005
  • This paper proposes the Pareto artificial life algorithm for a multi-objective function optimization problem. The artificial life algorithm for a single objective function optimization problem is improved through incorporating the new method to estimate the fitness value fur a solution and the Pareto list to memorize and to improve the Pareto optimal set. The proposed algorithm is applied to the optimum design of a Journal bearing which has two objective functions. The Pareto front and the optimal solution set for the application are reported to present the possible solutions to a decision maker or a designer.

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Multi-objective Optimization of a Laidback Fan Shaped Film-Cooling Hole Using Evolutionary Algorithm

  • Lee, Ki-Don;Husain, Afzal;Kim, Kwang-Yong
    • International Journal of Fluid Machinery and Systems
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    • v.3 no.2
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    • pp.150-159
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    • 2010
  • Laidback fan shaped film-cooling hole is formulated numerically and optimized with the help of three-dimensional numerical analysis, surrogate methods, and the multi-objective evolutionary algorithm. As Pareto optimal front produces a set of optimal solutions, the trends of objective functions with design variables are predicted by hybrid multi-objective evolutionary algorithm. The problem is defined by four geometric design variables, the injection angle of the hole, the lateral expansion angle of the diffuser, the forward expansion angle of the hole, and the ratio of the length to the diameter of the hole, to maximize the film-cooling effectiveness compromising with the aerodynamic loss. The objective function values are numerically evaluated through Reynolds- averaged Navier-Stokes analysis at the designs that are selected through the Latin hypercube sampling method. Using these numerical simulation results, the Response Surface Approximation model are constructed for each objective function and a hybrid multi-objective evolutionary algorithm is applied to obtain the Pareto optimal front. The clustered points from Pareto optimal front were evaluated by flow analysis. These designs give enhanced objective function values in comparison with the experimental designs.

Combined Economic and Emission Dispatch with Valve-point loading of Thermal Generators using Modified NSGA-II

  • Rajkumar, M.;Mahadevan, K.;Kannan, S.;Baskar, S.
    • Journal of Electrical Engineering and Technology
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    • v.8 no.3
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    • pp.490-498
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    • 2013
  • This paper discusses the application of evolutionary multi-objective optimization algorithms namely Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and Modified NSGA-II (MNSGA-II) for solving the Combined Economic Emission Dispatch (CEED) problem with valve-point loading. The valve-point loading introduce ripples in the input-output characteristics of generating units and make the CEED problem as a non-smooth optimization problem. IEEE 57-bus and IEEE 118-bus systems are taken to validate its effectiveness of NSGA-II and MNSGA-II. To compare the Pareto-front obtained using NSGA-II and MNSGA-II, reference Pareto-front is generated using multiple runs of Real Coded Genetic Algorithm (RCGA) with weighted sum of objectives. Furthermore, three different performance metrics such as convergence, diversity and Inverted Generational Distance (IGD) are calculated for evaluating the closeness of obtained Pareto-fronts. Numerical results reveal that MNSGA-II algorithm performs better than NSGA-II algorithm to solve the CEED problem effectively.