A hybrid optimization technique coupling an evolutionary and a local search algorithm

Evolutionary Algorithms are robust and powerful global optimization techniques for solving
large scale problems that have many local optima. However, they require high CPU
times, and they are very poor in terms of convergence performance. On the other hand,
local search algorithms can converge in a few iterations but lack a global perspective. The
combination of global and local search procedures should offer the advantages of both optimization
methods while offsetting their disadvantages. This paper proposes a new hybrid
optimization technique that merges a Genetic Algorithm with a local search strategy based
on the Interior Point method. The efficiency of this hybrid approach is demonstrated by
solving a constrained multi-objective mathematical test-case.