An Approach to Optimize Multi-objective Problems Using Hybrid Genetic Algorithms Supported by Initial Centroid Selection Optimization Enhanced K-Means Based Selection Operator

The process of optimization is approached as a searching problem, where an optimization algorithm attempts to find the best possible solution to a given objective function within a permissible search domain. Such problems are complicated since we attempt to find the best possible solution to a given objective function. The problem becomes harder when there is more than one objective function that can be defined as multi-objective optimization problems. In such problems, the algorithm attempts to optimize more than one objective function. Furthermore, the problem becomes worse when these objectives are contradicting. Evolutionary algorithms are used to solve such problems including genetic algorithms (GAs). Hybridizing genetic algorithms is also utilized to overcome the sub optimal solution tendency of basic genetic algorithms. In this paper, an enhanced hybrid genetic algorithm is introduced with an advanced selection operator mechanism based on the K-means clustering algorithm that is also supported by the initial centroid selection optimization to ensure the best possible selection process. The proposed algorithm was tested against 4 benchmark multi-objective optimization algorithms where it succeeded to maximize the balance between search space exploration performed by the GA and search space exploitation performed by the PSO, that was reflected in the optimization ability of the algorithm. The enhanced ICSO/K-means selection operator also succeeded to enhance the optimization ability of the proposed algorithm by assuring fair distribution of the selected individuals from each generation.