Soft Constraints Handling for Multi-objective Optimization

Most real-world search and optimization problems naturally involve multiple objectives and several constraints. In this work, an idea for a generalized new approach for handling both hard and soft constraints in Multi-Objective Optimization Problems (MOOP) is demonstrated. The main purpose is to fully satisfy all the hard constraints and satisfy soft constraints as much as possible. A modification to the binary tournament parent selection approach is proposed. The proposed approach is integrated with the two most widely used multi-objective evolutionary algorithms, i.e., NSGA-II and SPEA2. A test is conducted on four benchmark problems and satisfactory results are achieved.

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