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achieve improved convergence while maintaining good diversity. (The well-
known functions ZDT1 ZDT4 were used for benchmark testing).
Further work on the multi-objective optimization method, AEPSO, will
seek to improve the ability of the algorithm to distribute more uniformly
along the Pareto front. There is a very strong continuing interest in the
literature on the use of PSO for multi-objective optimization. Important
recent papers include research into distributed co-evolutionary PSO,
50
improvements using crowding, mutation and epsilon-dominance
51
and an
approach which draws heavily from the experience of evolutionary multi-
objective optimization research.
52
Developing new optimization algorithms
with high performance on convergence, diversity and user preference
53
is
our goal for future work.
Acknowledgments
The authors would like to thank the financial support from Education
Ministry of China via the “111 Project”.
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