Evolutionary Multiobjective Optimization: Principles, Procedures, and Practices

Multi‐objective optimization problems deal with multiple conflicting objectives. In principle, they give rise to a set of trade‐off Pareto‐optimal solutions. Over the past one‐and‐half decade, evolutionary multi‐objective optimization (EMO) has established itself as a mature field of research and application with an extensive literature, commercial softwares, numerous freely downloadable codes, a dedicated biannual conference running successfully five times so far since 2001, special sessions and workshops held at all major evolutionary computing conferences, and full‐time researchers from universities and industries from all around the globe. This is because evolutionary algorithms (EAs) work with a population of solutions and in solving multi‐objective optimization problems, EAs can be modified to find and capture multiple solutions in a single simulation run. In this article, we make a brief outline of EMO principles, discuss one specific EMO algorithm, and present some current research issues of EMO.