Gaofeng Huang

A hybrid genetic algorithm for the Three-Index Assignment Problem

AbstractThe Three-Index Assignment Problem (AP3) is well-known problem which has been shown to be NP-hard. Thisproblem has been studied extensively, and many exact and heuristic methods have been proposed to solve it. Inspiredby the classical assignment problem, we propose a new local search heuristic which solves the problem by simplifying itto the classical assignment problem. We further hybridize our heuristic with the genetic algorithm (GA). Extensiveexperimental results indicate that our hybrid method is superior to all previous heuristic methods including those pro-posed by Balas and Saltzman [Operations Research 39 (1991) 150–161], Crama and Spieksma [European Journal ofOperational Research 60 (1992) 273–279], Burkard et al. [Discrete Applied Mathematics 65 (1996) 123–169], and Aiexet al. [GRASP with path relinking for the three-index assignment problem, Technical report, INFORMS Journal onComputing, in press. Available from: ]. 2004 Elsevier B.V. All rights reserved.

Designing a hybrid genetic algorithm for the linear ordering problem

The Linear Ordering Problem(LOP), which is a well-known NP-hard problem, has numerous applications in various fields. Using this problem as an example, we illustrate a general procedure of designing a hybrid genetic algorithm, which includes the selection of crossover/mutation operators, accelerating the local search module and tuning the parameters. Experimental results show that our hybrid genetic algorithm outperforms all other existing exact and heuristic algorithms for this problem.

A hybrid genetic algorithm for three-index assignment problem

Three-index assignment problem (AP3) is well-known problem which has been shown to be NP-hard. This problem has been studied extensively, and many exact and heuristic methods have been proposed to solve it. Inspired by the classical assignment problem, we propose a new iterative heuristic, called fragmental optimization (FO), which solves the problem by simplifying it to the assignment problem. We further hybridize our heuristic with the genetic algorithm (GA). Extensive experimental results indicate that our hybrid method to be superior to all previous heuristic methods including those proposed by Balas and Saltzman(1991), Crama and Spieksma(1992), Burkard et al(1996), and Aiex et al(2003).

Fragmental optimization on the 2-machine bicriteria flowshop scheduling problem

The 2-machine bicriteria flowshop scheduling problem F2/spl par/(/spl Sigma/C/sub i//C/sub max/) is studied in this paper, which minimizes the total flow time subject to the makespan of the schedule being minimum. This problem is known to be strongly NP-hard, and several heuristic algorithms have been proposed to solve it. In this paper, we present a new approach, which we named fragmental optimization (FO), that combines the dynamic programming and local search strategies. Extensive experimentation shows that our FO algorithm outperforms existing heuristics and provides solutions that are very close to the optimal.

A local search using solution fragments for the 2-machine bicriteria scheduling problem

Abstract In this work, we introduce a local search strategy for combinatorial optimization problems which explores neighborhoods obtained using fragments of current solutions. We apply the approach to the well-known

An effective Branch-and-Bound algorithm to solve the k-longest common subsequence problem

\mathcal{NP}

A Simple Yet Effective Framework for Optimization Problems.

-hard 2-machine bicriteria flowshop scheduling problem. Computational experiments using benchmark data show the approach to be effective when compared to other algorithms available for the problem.