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6 CONCLUSIONS
We have introduced a new hybrid multi-objective
evolutionary algorithm based on Tribes and TS. This
hybrid aims to combine the high convergence rate of
Tribes with the good neighbourhood exploration
performed by the TS algorithm. Therefore, we have
studied the impact of the place where we apply TS
technique on the performance of the algorithm. The
proposed version TS-TribesV1 gave the best results
almost for all the test functions except for S-ZDT4
and R-ZDT4 for which the TS-TribesV3 gave the
best results.
The results showed that the hybridization is a
very promising approach to multi-objective
optimization. As part of our ongoing work we are
going to compare the proposed algorithms with other
techniques that are representative of the state of art
of the multi-objective optimization. Moreover, we
are going to study other hybridization between
Tribes and other local search techniques.
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NEW PROPOSAL FOR A MULTI-OBJECTIVE TECHNIQUE USING TRIBES AND TABU SEARCH
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