A study of evolutionary algorithms based on multi-objective pareto optimality

In real life, there are a lot of Multi-objective Optimization Problem, which is shorted for MOP in the process of people working in production and economic and engineering activities. These problems are often very complex and nonlinear, and even conflicted with each other. When solving these problems, Multi-objective Optimization, shorted for MOO should be done on these issues. For example, for a project, people always want to spend minimum and get maximum efficiency. And here the cost and efficiency are two objectives of this project. In 1896, the French economist Vilfredo Pareto explained the MOP from the perspective of economics, which is now commonly referred as Pareto Optimization. In order to optimize the overall goal, it is necessary to consider the subgoals comprehensively which are conflicted with each other, that is to say that compromise on multiple objectives is needed, so it has multiple solutions. Multi-objective optimization algorithm based on Pareto just uses the algorithm to find the optimal solution to the multiple objectives.