A Probabilistic-Driven Search Algorithm for solving a Class of Optimization Problems
Nguyen Huu Thong (thong_nh2002yahoo.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.
Entry Submitted: 08/16/2012
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