MOMH Multiple-Objective MetaHeuristics

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MOMHLib++ (Multiple Objective MetaHeuristics Library in C++) is a library of C++ classes that implements a number of multiple objective metaheuristics. It has been developed on the basis of former implementations of Pareto simulated annealing (PSA) and multiple objective genetic local search (MOGLS). The goals of development of the library may be summarized as follows:

• to implement the main multiple objective metaheuristics in a consistent way,
• to allow easy adaptation of the methods to a given problem,
• to support evaluation of multiple objective metaheuristics on a given problem,
• to allow easy incorporation of new multiple objective metaheuristics and to support the development of new methods.

At present the library includes the following methods:

• Pareto simulated annealing (PSA) PSA's home page,
• Serafini's multiple objective simulated annealing (SMOSA)[4][5],
• Ulungu's et al. multiple objective simulated annealing (MOSA) [7],
• Pareto memetic algorithm [8],
• multiple objective genetic local search (MOGLS) MOGLS's home page,
• Ishibuchi's and Murata's multiple objective genetic local search (IMMOGLS) [3],
• multiple objective multiple start local search (MOMSLS),
• non-dominated sorting genetic algorithm (NSGA) [6] and controlled NSGA II [1],
• Strength Pareto Evolutionary Algorithm [9].

Each method is implemented in a separate template class that utilizes a number of common library classes. The template classes are instantiated with classes corresponding to solutions of a given problem. In order to adapt one or more of the multiple objective metaheuristics to a given problem one has to implement a new class corresponding to the problem's solution by deriving from the library class TMOMHSolution. A detailed design pattern described in the documentation of MOMHLib++ illustrates the suggested way of adapting the library to a given problem. The library is implemented in standard C++. It uses standard template library - STL. Thus, it may be compiled under any standard C++ compiler.

Reference:

1. Deb K. , Goel T. (2001), Controlled elitist non-dominated sorting genetic algorithms for better convergence, in: E. Zitzler, K. Deb, L. Thiele, C.A. Coello Coello, D. Corne (eds.) Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization, Lecture Notes in Computer Science 1993, Springer, Berlin, 67-81.
2. Fonseca C.M., Fleming P.J. (1993), Genetic algorithms for multiobjective optimization: Formulation, discussion and generalization. In Forrest S. (ed.), Genetic Algorithms: Proceedings of 5th International Conference, San Mateo, CA, Morgan Kaufmann, 416-423.
3. Ishibuchi H. Murata T. (1998), Multi-Objective Genetic Local Search Algorithm and Its Application to Flowshop Scheduling. IEEE Transactions on Systems, Man and Cybernetics, 28, 3, 392-403.
4. Serafini P. (1992), Simulated annealing for multiple objective optimization problems. Proceedings of the Tenth International Conference on Multiple Criteria Decision Making, Taipei 19-24.07.92, vol. 1, 87-96.
5. Serafini P. (1994), Simulated annealing for multiple objective optimization problems. In: Tzeng G.H., Wang H.F., Wen V.P., Yu P.L. (eds), Multiple Criteria Decision Making. Expand and Enrich the Domains of Thinking and Application, Springer Verlag, 283-292.
6. Srinivas N., Deb K. (1994), Multiple objective optimization using nondominated sorting in genetic algorithms, Evolutionary Computation, 2, 2, 221-248.
7. Ulungu E.L., Teghem J., Fortemps Ph., Tuyttens D. (1999), MOSA method: a tool for solving multiobjective combinatorial optimization problems. Journal of Multi-Criteria Decision Analysis, 8, 221-236.
8. A. Jaszkiewicz (2001), A comparative study of multiple-objective metaheuristics on the bi-objective set covering problem and the Pareto memetic algorithm. Research report, Institute of Computing Science, Poznan University of Technology, RA-003/01, pp. 23. dowload
9. Zitzler E., Thiele L. (1999), Multiobjective Evolutionary Algorithms: A Comparative Case Study and the Strength Pareto Evolutionary Algorithm, IEEE Transactions on Evolutionary Computation, 3, 4, 257-271