A comparison of memetic algorithms, tabu search, and ant colonies for the quadratic assignment problem

A memetic algorithm (MA), i.e. an evolutionary algorithm making use of local search, for the quadratic assignment problem is presented. A new recombination operator for realizing the approach is described, and the behavior of the MA is investigated on a set of problem instances containing between 25 and 100 facilities/locations. The results indicate that the proposed MA is able to produce high quality solutions quickly. A comparison of the MA with some of the currently best alternative approaches-reactive tabu search, robust tabu search and the fast ant colony system-demonstrates that the MA outperforms its competitors on all studied problem instances of practical interest.

[1]  T. Koopmans,et al.  Assignment Problems and the Location of Economic Activities , 1957 .

[2]  Leon Steinberg,et al.  The Backboard Wiring Problem: A Placement Algorithm , 1961 .

[3]  Elwood S. Buffa,et al.  A Heuristic Algorithm and Simulation Approach to Relative Location of Facilities , 1963 .

[4]  Elwood S. Buffa,et al.  The Facilities Layout Problem in Perspective , 1966 .

[5]  R. Lewontin.  ‘The Selfish Gene’ , 1977, Nature.

[6]  A. N. Elshafei,et al.  Hospital Layout as a Quadratic Assignment Problem , 1977 .

[7]  H. P. Schwefel,et al.  Numerische Optimierung von Computermodellen mittels der Evo-lutionsstrategie , 1977 .

[8]  David S. Johnson,et al.  Computers and Intractability: A Guide to the Theory of NP-Completeness , 1978 .

[9]  M. Garey.  Johnson: computers and intractability: a guide to the theory of np- completeness (freeman , 1979 .

[10]  R. Burkard.  Quadratic Assignment Problems , 1984 .

[11]  F. Rendl,et al.  A thermodynamically motivated simulation procedure for combinatorial optimization problems , 1984 .

[12]  Heinz Mühlenbein,et al.  Parallel Genetic Algorithms, Population Genetics, and Combinatorial Optimization , 1989, Parallelism, Learning, Evolution.

[13]  Pablo Moscato,et al.  On Evolution, Search, Optimization, Genetic Algorithms and Martial Arts : Towards Memetic Algorithms , 1989 .

[14]  Donald E. Brown,et al.  A Parallel Genetic Heuristic for the Quadratic Assignment Problem , 1989, ICGA.

[15]  Jon Louis Bentley,et al.  Experiments on traveling salesman heuristics , 1990, SODA '90.

[16]  Larry J. Eshelman,et al.  The CHC Adaptive Search Algorithm: How to Have Safe Search When Engaging in Nontraditional Genetic Recombination , 1990, FOGA.

[17]  Emile H. L. Aarts,et al.  Genetic Local Search Algorithms for the Travelling Salesman Problem , 1990, PPSN.

[18]  Jadranka Skorin-Kapov,et al.  Tabu Search Applied to the Quadratic Assignment Problem , 1990, INFORMS J. Comput..

[19]  M. Dorigo,et al.  1 Positive Feedback as a Search Strategy , 1991 .

[20]  Éric D. Taillard,et al.  Robust taboo search for the quadratic assignment problem , 1991, Parallel Comput..

[21]  Ron Shonkwiler,et al.  Parallel Genetic Algorithms , 1993, ICGA.

[22]  Charles Fleurent,et al.  Genetic Hybrids for the Quadratic Assignment Problem , 1993, Quadratic Assignment and Related Problems.

[23]  Panos M. Pardalos,et al.  A Greedy Randomized Adaptive Search Procedure for the Quadratic Assignment Problem , 1993, Quadratic Assignment and Related Problems.

[24]  Jadranka Skorin-Kapov,et al.  Extensions of a tabu search adaptation to the quadratic assignment problem , 1994, Comput. Oper. Res..

[25]  Roberto Battiti,et al.  The Reactive Tabu Search , 1994, INFORMS J. Comput..

[26]  Volker Nissen,et al.  Solving the quadratic assignment problem with clues from nature , 1994, IEEE Trans. Neural Networks.

[27]  Erwin Pesch,et al.  Genetic Local Search in Combinatorial Optimization , 1994, Discret. Appl. Math..

[28]  É. Taillard.  COMPARISON OF ITERATIVE SEARCHES FOR THE QUADRATIC ASSIGNMENT PROBLEM. , 1995 .

[29]  Alice E. Smith,et al.  A genetic approach to the quadratic assignment problem , 1995, Comput. Oper. Res..

[30]  Volker Nissen,et al.  A modification of threshold accepting and its application to the quadratic assignment problem , 1995 .

[31]  M. Dorigo,et al.  Algodesk : an experimental comparison of eight evolutionary heuristics applied to the Quadratic Assignment Problem , 1995 .

[32]  P.,et al.  Parallel Hybrid MetaHeuristics : Application to the Quadratic Assignment , 1996 .

[33]  Bernd Freisleben,et al.  A genetic local search algorithm for solving symmetric and asymmetric traveling salesman problems , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[34]  Marco Dorigo,et al.  Ant system: optimization by a colony of cooperating agents , 1996, IEEE Trans. Syst. Man Cybern. Part B.

[35]  Bernd Freisleben,et al.  New Genetic Local Search Operators for the Traveling Salesman Problem , 1996, PPSN.

[36]  Takeshi Yamada,et al.  Scheduling by Genetic Local Search with Multi-Step Crossover , 1996, PPSN.

[37]  T. Schnier,et al.  Genetic Engineering and Design Problems , 1997 .

[38]  F. Aida,et al.  Max{min Ant System for Quadratic Assignemnt Problems Max{min Ant System for Quadratic Assignment Problems , 1997 .

[39]  Luca Maria Gambardella,et al.  An ant approach for structured quadratic assignment problems , 1997 .

[40]  Bernd Freisleben,et al.  A Genetic Local Search Approach to the Quadratic Assignment Problem , 1997, ICGA.

[41]  Thomas Stützle,et al.  The MAX–MIN Ant System and Local Search for Combinatorial Optimization Problems: Towards Adaptive Tools for Global Optimization , 1997 .

[42]  Ric,et al.  Adaptive Memories for the Quadratic Assignment Problem , 1997 .

[43]  B. Freisleben,et al.  Genetic local search for the TSP: new results , 1997, Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97).

[44]  Corso Elvezia.  Ant Colonies for the QAP , 1997 .

[45]  É. Taillard,et al.  Adaptive memories for the Quadratic Assignment Problems , 1997 .

[46]  Franz Rendl,et al.  QAPLIB – A Quadratic Assignment Problem Library , 1997, J. Glob. Optim..

[47]  Bernd Freisleben,et al.  On the effectiveness of evolutionary search in high-dimensional NK-landscapes , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[48]  Shin Ishii,et al.  Constrained neural approaches to quadratic assignment problems , 1998, Neural Networks.

[49]  Éric D. Taillard,et al.  FANT: Fast ant system , 1998 .

[50]  Bernd Freisleben,et al.  Memetic Algorithms and the Fitness Landscape of the Graph Bi-Partitioning Problem , 1998, PPSN.

[51]  Bernd Freisleben,et al.  Fitness landscapes and memetic algorithm design , 1999 .

[52]  Vittorio Maniezzo,et al.  Exact and Approximate Nondeterministic Tree-Search Procedures for the Quadratic Assignment Problem , 1999, INFORMS J. Comput..

[53]  Vittorio Maniezzo,et al.  The Ant System Applied to the Quadratic Assignment Problem , 1999, IEEE Trans. Knowl. Data Eng..

[54]  B. Freisleben,et al.  Genetic algorithms for binary quadratic programming , 1999 .

[55]  Bernd Freisleben,et al.  Fitness Landscapes, Memetic Algorithms, and Greedy Operators for Graph Bipartitioning , 2000, Evolutionary Computation.

[56]  Bernd Freisleben,et al.  Fitness landscape analysis and memetic algorithms for the quadratic assignment problem , 2000, IEEE Trans. Evol. Comput..