Cellular Genetic Algorithm for Multi-Objective Optimization
TL;DRAbstract
In this paper, we show how cellular structures can be combined with a multi-objective genetic algorithm (MOGA) for improving its search ability to find Pareto-optimal solutions of multi-objective optimization problems. We propose an assignment method of a different search direction to each cell for implementing a cellular MOGA. In our cellular MOGA, every individual in each population exists in a cell of a spatially structured space (e.g., two-dimensional grid-world) where each cell has a different search direction. Such a search direction corresponds to weights in a scalar fitness function defined by the weighted sum of multiple objectives. The selection of parents for generating a new individual in a cell is performed within the neighborhood of that cell based on its search direction. The effectiveness of the proposed cellular MOGA is shown by computer simulations on two-objective flowshop scheduling problems. 1
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In this paper, we show how cellular structures can be combined with a multi-objective genetic algorithm (MOGA) for improving its search ability to find Pareto-optimal solutions of multi-objective optimization problems. We propose an assignment method of a different search direction to each cell for implementing a cellular MOGA. In our cellular MOGA, every individual in each population exists in a cell of a spatially structured space (e.g., two-dimensional grid-world) where each cell has a different search direction. Such a search direction corresponds to weights in a scalar fitness function defined by the weighted sum of multiple objectives. The selection of parents for generating a new individual in a cell is performed within the neighborhood of that cell based on its search direction. The effectiveness of the proposed cellular MOGA is shown by computer simulations on two-objective flowshop scheduling problems. 1
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