A Multi-resolution GA-PSO Layered Encoding Cascade Optimization Model

Many real-world problems involve optimization of multi-resolution parameters. In optimization problems, the higher the resolution, the larger the search space, and resolution affects the accuracy and performance of an optimization model. This article presents a genetic algorithm and particle swarm based cascade multi-resolution optimization model, and it is known as GA-PSO LECO. GA and PSO are combined in this research to integrate random as well as directional search to promote global exploration and local exploitation of solutions. The model is developed using the layered encoding representation structure, and is evaluated using two parameter optimization problems, i.e., the Tennessee Eastman chemical process optimization and the MMIC amplifier design interactive optimization.

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