A new genetic-based tabu search algorithm for unit commitment problem

This paper presents a new algorithm based on integrating the use of genetic algorithms and tabu search methods to solve the unit commitment problem. T he proposed algorithm, which is mainly based on genetic algorithms incorpor ates tabu search method to generate new population members in the reproduct ion phase of the genetic algorithm. In the proposed algorithm, genetic algo rithm solution is coded as a mix between binary and decimal representation. A fitness function is constructed from the total operating cost of the gen erating units without penalty terms. In the tabu search part of the algorit hm, a simple short term memory procedure is used to counter the danger of e ntrapment at a local optimum by preventing cycling of solutions, and the pr emature convergence of the genetic algorithm. A significant improvement of the proposed algorithm results, over those obtained by either genetic algor ithm or tabu search, has been achieved. Numerical examples also showed the superiority of the proposed algorithm compared with two classical methods i n the literature. (C) 1999 Elsevier Science S.A. All rights reserved.