Swift — a new constrained optimization technique

“…Let us apply the penalty method to deal with the leader's constraints. In order to use the technique efficiently, we design a dynamic parameter inspired by the sequential weight increasing factor technique (SWIFT) proposed by B. V. Sheela and P. Ramamoorthy [19]. The fitness function is denoted by F(x, y) + M g .…”

Section: Fitness Functionmentioning

confidence: 99%

An Evolutionary Algorithm Based on a New Decomposition Scheme for Nonlinear Bilevel Programming Problems

Li¹,

Wang²

2010

IJCNS

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In this paper, we focus on a class of nonlinear bilevel programming problems where the follower’s objective is a function of the linear expression of all variables, and the follower’s constraint functions are convex with respect to the follower’s variables. First, based on the features of the follower’s problem, we give a new decomposition scheme by which the follower’s optimal solution can be obtained easily. Then, to solve efficiently this class of problems by using evolutionary algorithm, novel evolutionary operators are designed by considering the best individuals and the diversity of individuals in the populations. Finally, based on these techniques, a new evolutionary algorithm is proposed. The numerical results on 20 test problems illustrate that the proposed algorithm is efficient and stable

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An algorithm for engineering design optimization

Belsare

Arora

1983

Numerical Meth Engineering

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SUMMARYIn this paper a new concept for development of algorithms for optimal design of engineering systems is presented. The basic idea is to use upper and lower bounds on optimum cost to develop iterative search strategies. The main feature of the concept is that it does not rely on one-dimensional search to compute a step size at any design iteration. Implication of the feature is that the algorithms based on this concept require evaluation of constraint functions only once at any design iteration. This is highly desirable for optimal design of engineering systems because evaluation of functions for such systems is very expensive due to their implicit dependence on design variables. An algorithm based on the new concept is derived in the paper. Several new step sizes are introduced and their relation to proper reduced optimal design problems are presented. A new step size based on the constant cost requirement at some design iterations is introduced. Numerical aspects for the algorithm are also presented. Based on the new algorithm, a general-purpose computer code GRP2 is developed. The code is used to solve several problems to gain experience and insight for the algorithm. Numerical experience with examples is discussed. It is concluded that algorithms based on bounding optimum cost have substantial potential for applications in optimal design of engineering systems.

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Swift — a new constrained optimization technique

Cited by 17 publications

References 2 publications

The Cross-Entropy Method for Continuous Multi-Extremal Optimization

The Cross-Entropy Method for Continuous Multi-Extremal Optimization

An Evolutionary Algorithm Based on a New Decomposition Scheme for Nonlinear Bilevel Programming Problems

An algorithm for engineering design optimization

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