• 제목/요약/키워드: Constrained Optimization Problems

검색결과 120건 처리시간 0.024초

Symbiotic Organisms Search for Constrained Optimization Problems

  • Wang, Yanjiao;Tao, Huanhuan;Ma, Zhuang
    • Journal of Information Processing Systems
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    • 제16권1호
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    • pp.210-223
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    • 2020
  • Since constrained optimization algorithms are easy to fall into local optimum and their ability of searching are weak, an improved symbiotic organisms search algorithm with mixed strategy based on adaptive ε constrained (ε_SOSMS) is proposed in this paper. Firstly, an adaptive ε constrained method is presented to balance the relationship between the constrained violation degrees and fitness. Secondly, the evolutionary strategies of symbiotic organisms search algorithm are improved as follows. Selecting different best individuals according to the proportion of feasible individuals and infeasible individuals to make evolutionary strategy more suitable for solving constrained optimization problems, and the individual comparison criteria is replaced with population selection strategy, which can better enhance the diversity of population. Finally, numerical experiments on 13 benchmark functions show that not only is ε_SOSMS able to converge to the global optimal solution, but also it has better robustness.

SMOOTHING APPROXIMATION TO l1 EXACT PENALTY FUNCTION FOR CONSTRAINED OPTIMIZATION PROBLEMS

  • BINH, NGUYEN THANH
    • Journal of applied mathematics & informatics
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    • 제33권3_4호
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    • pp.387-399
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    • 2015
  • In this paper, a new smoothing approximation to the l1 exact penalty function for constrained optimization problems (COP) is presented. It is shown that an optimal solution to the smoothing penalty optimization problem is an approximate optimal solution to the original optimization problem. Based on the smoothing penalty function, an algorithm is presented to solve COP, with its convergence under some conditions proved. Numerical examples illustrate that this algorithm is efficient in solving COP.

유전해법을 이용한 비선형최적화 문제의 효율적인 해법 (An Efficient Method for Nonlinear Optimization Problems using Genetic Algorithms)

  • 임승환;이동춘
    • 산업경영시스템학회지
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    • 제20권44호
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    • pp.93-101
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    • 1997
  • This paper describes the application of Genetic Algorithms(GAs) to nonlinear constrained mixed optimization problems. Genetic Algorithms are combinatorial in nature, and therefore are computationally suitable for treating discrete and integer design variables. But, several problems that conventional GAs are ill defined are application of penalty function that can be adapted to transform a constrained optimization problem into an unconstrained one and premature convergence of solution. Thus, we developed an improved GAs to solve this problems, and two examples are given to demonstrate the effectiveness of the methodology developed in this paper.

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DUALITY FOR LINEAR CHANCE-CONSTRAINED OPTIMIZATION PROBLEMS

  • Bot, Radu Ioan;Lorenz, Nicole;Wanka, Gert
    • 대한수학회지
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    • 제47권1호
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    • pp.17-28
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    • 2010
  • In this paper we deal with linear chance-constrained optimization problems, a class of problems which naturally arise in practical applications in finance, engineering, transportation and scheduling, where decisions are made in presence of uncertainty. After giving the deterministic equivalent formulation of a linear chance-constrained optimization problem we construct a conjugate dual problem to it. Then we provide for this primal-dual pair weak sufficient conditions which ensure strong duality. In this way we generalize some results recently given in the literature. We also apply the general duality scheme to a portfolio optimization problem, a fact that allows us to derive necessary and sufficient optimality conditions for it.

ITERATION METHOD FOR CONSTRAINED OPTIMIZATION PROBLEMS GOVERNED BY PDE

  • Lee, Hyung-Chun
    • 대한수학회논문집
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    • 제13권1호
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    • pp.195-209
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    • 1998
  • In this paper we present a new iteration method for solving optimization problems governed by partial differential equations. We generalize the existing methods such as simple gradient methods and pseudo-time methods to get an efficient iteration method. Numerical tests show that the convergence of the new iteration method is much faster than those of the pseudo-time methods especially when the parameter $\sigma$ in the cost functional is small.

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Hopfield neuron based nonlinear constrained programming to fuzzy structural engineering optimization

  • Shih, C.J.;Chang, C.C.
    • Structural Engineering and Mechanics
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    • 제7권5호
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    • pp.485-502
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    • 1999
  • Using the continuous Hopfield network model as the basis to solve the general crisp and fuzzy constrained optimization problem is presented and examined. The model lies in its transformation to a parallel algorithm which distributes the work of numerical optimization to several simultaneously computing processors. The method is applied to different structural engineering design problems that demonstrate this usefulness, satisfaction or potential. The computing algorithm has been given and discussed for a designer who can program it without difficulty.

실수코딩 유전알고리즘에 관한 연구 (A Study on a Real-Coded Genetic Algorithm)

  • 진강규;주상래
    • 제어로봇시스템학회논문지
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    • 제6권4호
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    • pp.268-275
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    • 2000
  • The increasing technological demands of today call for complex systems, which in turn involve a series of optimization problems with some equality or inequality constraints. In this paper, we presents a real-coded genetic algorithm(RCGA) as an optimization tool which is implemented by three genetic operators based on real coding representation. Through a lot of simulation works, the optimum settings of its control parameters are obtained on the basis of global off-line robustness for use in off-line applications. Two optimization problems are Presented to illustrate the usefulness of the RCGA. In case of a constrained problem, a penalty strategy is incorporated to transform the constrained problem into an unconstrained problem by penalizing infeasible solutions.

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수정된 유전 알고리즘을 이용한 비선형최적화 문제의 효율적인 해법 (An efficient method for nonlinear optimization problems using modified genetic algorithms)

  • 윤영수;이상용
    • 한국경영과학회:학술대회논문집
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    • 대한산업공학회/한국경영과학회 1996년도 춘계공동학술대회논문집; 공군사관학교, 청주; 26-27 Apr. 1996
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    • pp.519-524
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    • 1996
  • This paper describes the application of Genetic Algorithms(GAs) to nonlinear constrained mixed optimization problems. Genetic Algorithms are combinatorial in nature, and therefore are computationally suitable for treating discrete and integer design variables. But, several problems that conventional GAs are ill defined are applicaiton of penalty function that can be adapted to transform a constrained optimization problem into an unconstrained optimization problem into an unconstrained one and premature convergence of solution. Thus, we developed an modified GAs to solve this problems, and two examples are given to demonstrate the effectiveness of the methodology developed in this paper.

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Hybrid PSO를 이용한 안전도를 고려한 경제급전 (The Security Constrained Economic Dispatch with Line Flow Constraints using the Hybrid PSO Algorithm)

  • 장세환;김진호;박종배;박준호
    • 전기학회논문지
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    • 제57권8호
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    • pp.1334-1341
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    • 2008
  • This paper introduces an approach of Hybrid Particle Swarm Optimization(HPSO) for a security-constrained economic dispatch(SCED) with line flow constraints. To reduce a early convergence effect of PSO algorithm, we proposed HPSO algorithm considering a mutation characteristic of Genetic Algorithm(GA). In power system, for considering N-1 line contingency, we have chosen critical line contingency through a process of Screening and Selection based on PI(performance Index). To prove the ability of the proposed HPSO in solving nonlinear optimization problems, SCED problems with nonconvex solution spaces are considered and solved with three different approach(Conventional GA, PSO, HPSO). We have applied to IEEE 118 bus system for verifying a usefulness of the proposed algorithm.

Approximate Dynamic Programming-Based Dynamic Portfolio Optimization for Constrained Index Tracking

  • Park, Jooyoung;Yang, Dongsu;Park, Kyungwook
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • 제13권1호
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    • pp.19-30
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    • 2013
  • Recently, the constrained index tracking problem, in which the task of trading a set of stocks is performed so as to closely follow an index value under some constraints, has often been considered as an important application domain for control theory. Because this problem can be conveniently viewed and formulated as an optimal decision-making problem in a highly uncertain and stochastic environment, approaches based on stochastic optimal control methods are particularly pertinent. Since stochastic optimal control problems cannot be solved exactly except in very simple cases, approximations are required in most practical problems to obtain good suboptimal policies. In this paper, we present a procedure for finding a suboptimal solution to the constrained index tracking problem based on approximate dynamic programming. Illustrative simulation results show that this procedure works well when applied to a set of real financial market data.