The personal best is an interesting topic, but little work has focused on whether it is still efficient
for multiobjective particle swarm optimization. In dealing with single objective optimization problems, a single
global best exists, so the personal best provides optimal diversity to prevent premature convergence. But in multiobjective
optimization problems, the diversity provided by the personal best is less optimal, whereas the global
archive contains a series of global bests, thus provides optimal diversity. If the algorithm excluding the personal
best provides sufficient randomness, the personal best becomes worthless. Therefore we propose no personal best
strategy that no longer uses the personal best when the global archive exceeds the population size. Experimental
results validate the efficiency of our strategy.
WANG Ying-lin1,2 (王英林), XU He-ming2* (徐鹤鸣)
. Multiobjective Particle Swarm Optimization Without the Personal Best[J]. Journal of Shanghai Jiaotong University(Science), 2014
, 19(2)
: 155
-159
.
DOI: 10.1007/s12204-014-1484-8
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