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A Probabilistic-Driven Search Algorithm for solving a Class of Optimization Problems

Nguyen Huu Thong (thong_nh2002***at***yahoo.com)

Abstract: In this paper we introduce a new numerical optimization technique, a Probabilistic-Driven Search Algorithm. This algorithm has the following characteristics: 1) In each iteration of loop, the algorithm just changes the value of k variables to find a new solution better than the current one; 2) In each variable of the solution of the problem, the algorithm finds the value of each digit from left digit to right digit; 3) The algorithm uses probability to control the implementation of the algorithm to perform two tasks mentioned above in a very special connection. We build two applications of Probabilistic-Driven Search algorithm, PDS algorithm 1 for solving single-objective optimization problems and PDS algorithm 2 for solving multi-objective optimization problems. We test this approach by implementing the algorithms on some benchmark optimization problems and we find very good and stable results.

Keywords: Optimization, Probability, Algorithm.

Category 1: Nonlinear Optimization

Category 2: Global Optimization

Category 3: Robust Optimization

Citation: 25 pages. Department of mathematics-information, HCMC University of Pedagogy 280, An Duong Vuong, Ho Chi Minh city, Viet Nam. 8/2012.

Download: [PDF]

Entry Submitted: 08/16/2012
Entry Accepted: 08/17/2012
Entry Last Modified: 08/30/2012

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