Evolutionary algorithms in multiobjective problems

Abstract

Many real-world problems involve two types of difficulties: 1) multiple, conflicting objectives and 2) a highly complex search space. Efficient evolutionary strategies have been developed to deal with both types of difficulties. Evolutionary algorithms possess several characteristics such as parallelism and robustness that make them preferable to classical optimization methods. In this work 1 conducted comparative studies among the well-known evolutionary algorithms based on NP-hard 0-1 multiobjective knapsack problem.