METAMORPHIC BIO-INSPIRED MODEL FOR OPTIMIZING SOFT CONSTRAINED COMBINATORIAL PROBLEMS

This paper establishes a new metamorphic bio-inspired model to optimize soft constrained combinatorial problems. It is based on improved genetic algorithm (GA) and local search of steepest ascent hill climbing algorithm(SAHC). The performance of GA can be improved by proposing new selection, crossover and mutation operators and thus forming twelve improved GA models with various combinations of GA operators. Empirical study has been done on instances of college course timetabling problems (CCTP) and benchmark problems of multi job shop scheduling(MJSSP) problems and formed 12 models with various combinations of GA operators (proposed and existing) for solving soft constrained combinatorial problems and identified the best to solve the soft constrained combinatorial problems.

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