Multiplicity and local search in evolutionary algorithms to build the Pareto front

In multicriteria optimization determination of the Pareto-optimal front is of utmost importance for decision making. Simultaneous parallel search for multiple members of an evolutionary algorithm can lead to effective optimization. In a previous approach (Esquivel et al., 1999) extending the ideas of a former work of (Lis and Eiben, 1997), we proposed the multi-sexual-parents-crossovers genetic algorithm (MSPC-GA), a method which by allowing multiple parents per sex and multiple crossovers per mating action attempted to balance the explorative and exploitative efforts which are present in any evolutionary algorithm. The performance of the method produced an evenly distributed and larger set of efficient points. Following this concept the present proposal incorporates a hybridisation of global and local search to the multiplicity approach. Now the evolutionary approach combined with simulated annealing and neighbourhood search produced better results.

[1]  Gary B. Lamont,et al.  Multiobjective evolutionary algorithm test suites , 1999, SAC '99.

[2]  David A. Van Veldhuizen,et al.  Evolutionary Computation and Convergence to a Pareto Front , 1998 .

[3]  Kalyanmoy Deb,et al.  Multi-objective Genetic Algorithms: Problem Difficulties and Construction of Test Problems , 1999, Evolutionary Computation.

[4]  Michael P. Fourman,et al.  Compaction of Symbolic Layout Using Genetic Algorithms , 1985, ICGA.

[5]  Zbigniew Michalewicz,et al.  Handbook of Evolutionary Computation , 1997 .

[6]  Godfrey A. Walters,et al.  Genetic Operators and Constraint Handling for Pipe Network Optimization , 1995, Evolutionary Computing, AISB Workshop.

[7]  Michael Pilegaard Hansen,et al.  Tabu Search for Multiobjective Optimization: MOTS , 1997 .

[8]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[9]  Graeme C. Dandy,et al.  Genetic algorithms compared to other techniques for pipe optimization , 1994 .

[10]  Susana Cecilia Esquivel,et al.  Multiplicity in genetic algorithms to face multicriteria optimization , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[11]  Angus R. Simpson,et al.  Genetic algorithms compared to other techniques for pipe optimization , 1994 .

[12]  V. Vemuri,et al.  A new genetic algorithm for multi-objective optimization in water resource management , 1995, Proceedings of 1995 IEEE International Conference on Evolutionary Computation.

[13]  Kalyanmoy Deb,et al.  Muiltiobjective Optimization Using Nondominated Sorting in Genetic Algorithms , 1994, Evolutionary Computation.

[14]  David Corne,et al.  The Pareto archived evolution strategy: a new baseline algorithm for Pareto multiobjective optimisation , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[15]  A. Eiben,et al.  A multi-sexual genetic algorithm for multiobjective optimization , 1997, Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97).