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Multi-objective evolutionary algorithm based on decision space partition and its application in hybrid power system optimisation
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DOI：10.1007/s10489-016-0864-1.png)
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Abstract
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The distribution of individuals in a population significantly influences convergence to global optimal solutions. However, determining how to maximise decision space information, which benefits convergence, is disregarded. This paper proposes a type of multi-objective evolutionary algorithm based on decision space partition (DSPEA), and designs the sphere initialisation strategies, initialisation method of individuals in each sphere, updating approach for the centroid, radius, and individuals of a hypersphere, and information sharing mechanism among spheres. The decision space in the DSPEA framework is explicitly divided into several hyperspheres. The non-dominated sorting genetic algorithm II is employed to implement each evolution of each hypersphere. An improvement approach related to the information sharing of the spheres is used to produce the future motions of the spheres by adopting particle swarm optimisation. Twelve problems were used to test the performance of DSPEA, and extensive experimental results show that DSPEA performs better than six state-of-the-art multi-objective evolutionary algorithms. Finally, DSPEA is used to optimise a hybrid power system. The results of the simulation optimisation tests on the parameters of the control strategy and the drive system for hybrid electric vehicles demonstrate that the proposed approach can obtain a set of improved solutions with low fuel consumption and pollutant emission.
Keywords:
Decision space partition model
Hypersphere
Evolutionary algorithm
Hybrid electric vehicles
Multi-objective optimisation
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