Proven Runtime Guarantees for How the MOEA/D: Computes the Pareto Front from the Subproblem Solutions

The decomposition-based multi-objective evolutionary algorithm (MOEA/D) does not directly optimize a given multi-objective function f, but instead optimizes N+1 single-objective subproblems of f in a co-evolutionary manner. It maintains an archive of all non-dominated solutions found and outputs it as approximation to the Pareto front. Once the MOEA/D found all optima of the subproblems (the g-optima), it may still miss Pareto optima of f. The algorithm is then tasked to find the remaining Pareto optima directly by mutating the g-optima. In this work, we analyze for the first time how the MOEA/D with only standard mutation operators computes the whole Pareto front of the OneMinMax benchmark when the g-optima are a strict subset of the Pareto front. For standard bit mutation, we prove an expected runtime of O(nNlogn+n^n/(2N)Nlogn) function evaluations. Especially for the second, more interesting phase when the algorithm start with all g-optima, we prove an Ω(n^(1/2)(n/N+1)N2^-n/N) expected runtime. This runtime is super-polynomial if N=o(n), since this leaves large gaps between the g-optima, which require costly mutations to cover. For power-law mutation with exponent β∈(1,2), we prove an expected runtime of OnNlogn+n^βlogn function evaluations. The On^βlogn term stems from the second phase of starting with all g-optima, and it is independent of the number of subproblems N. This leads to a huge speedup compared to the lower bound for standard bit mutation. In general, our overall bound for power-law suggests that the MOEA/D performs best for N=O(n^β-1), resulting in an O(n^βlogn) bound. In contrast to standard bit mutation, smaller values of N are better for power-law mutation, as it is capable of easily creating missing solutions.