Literature Review: Evolution Strategies Application in Solving Multi-Objective Optimization

Evolution Strategies (ES) is a heuristic-nature-inspired algorithm that uses real-coded gene representation. It is in the same family of evolutionary algorithms as the Genetic Algorithm (GA). ES is less popular and rarely used in solving problems than GA, but some research has proved that ES can result in a feasible solution, sometimes better than GA. This literature review tries to review ES's performance in computational time and its ability. Multi-objective optimization (MO) cases. While the basic forms of ES such as (µ,λ)-ES, (µ/p, λ)-ES, (µ+ λ)-ES, and (µ/p+ λ)-ES is usually capable of solving complex problems, sometimes ES needs to be modified. Another modification has been made by making a hybrid algorithm between ES and Fuzzy Inference System (FIS). The 12 literature that has been reviewed proves that ES is indeed capable of solving multi-objective problems ranging from the ordinary to the very complex.