A new multi-swarm multi-objective particle swarm optimization based on pareto front set

Abstract: In this paper, a new multi-swarm method is proposed for multiobjective particle swarm optimization. To enhance the Pareto front searching ability of PSO, the particles are divided into many swarms. Several swarms are dynamically searching the objective space around some points of the Pareto front set. The rest of particles are searching the space keeping away from the Pareto front to improve the global search ability. Simulation results and comparisons with existing Multi-objective Particle Swarm Optimization methods demonstrate that the proposed method effectively enhances the search efficiency and improves the search quality.Originally presented at 2011 International Conference on Intelligent Computing, Zhengzhou, China 11-14 August, 2011

Adaptive sharing scheme based sub-swarm multi-objective PSO

Abstract: To improve the optimization performance of multi-objective particle swarm optimization, a new sub-swarm method, where the particles are divided into several sub-swarms, is proposed. To enhance the quality of the Pareto front set, a new adaptive sharing scheme, which depends on the distances from nearest neighbouring individuals, is proposed and applied. In this method, the first sub-swarms particles dynamically search their corresponding areas which are around some points of the Pareto front set in the objective space, and the chosen points of the Pareto front set are determined based on the adaptive sharing scheme. The second sub-swarm particles search the rest objective space, and they are away from the Pareto front set, which can promote the global search ability of the method. Moreover, the core points of the first sub-swarms are dynamically determined by this new adaptive sharing scheme. Some Simulations are used to test the proposed method, and the results show that the proposed method can achieve better optimization performance comparing with some existing methods

Analysis and design of multifunctional agricultural landscapes : a graph theoretic approach

This thesis deals with the development of quantitative methodologies for the evaluation of landscape functions and their interactions in multifunctional agricultural landscapes. It focuses on the spatial coherence of hedgerow networks for ecological functions and landscape character for perception of landscape identity, and on their integration in a multifunctional and multiscale trade-off analysis. Graph theory provided the basis for new methodologies that are applied in this research

A study on distribution preservation mechanism in evolutionary multi-objective optimization

10.1007/s10462-004-2902-3Artificial Intelligence Review23131-56AIRV