Abstract

Multi-Objective Optimization is a prominent research area, in which approaches for the

simultaneous solution of multiple objectives are proposed. The possibility to discover a set of

parameters optimizing all the goals can be achieved only if the considered problems are rather

trivial, while compromise solutions are generally discovered. Things become even more

complex when the set of parameters is used in opposite, and potentially conflicting, ways.

In this work, we compared some state-of-the-art Evolutionary Algorithms with regard to the

optimization of different conflicting objectives, by highlighting strengths and weaknesses

of the different approaches. In particular, we considered four benchmark problems — 4-bit

parity, double-pole balancing, grid navigation and test function optimization — to be solved

simultaneously. Our investigation identifies the algorithms leading to a better optimization.

In particular, three algorithms emerge as the most suitable methods for dealing with the

considered scenario. Notably, a relatively simple strategy is not significantly inferior to a

more sophisticated one. Moreover, we illustrate the solutions discovered by the different

methods to address the benchmark problems.

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