A Comprehensive Review on Multi-Objective Optimization Using Genetic Algorithms

In real world applications, most of the optimization problems involve more than one objective to be optimized. The objectives in most of engineering problems are often conflicting, i.e., maximize performance, minimize cost, maximize reliability, etc. In the case, one extreme solution would not satisfy both objective functions and the optimal solution of one objective will not necessary be the best solution for other objective(s). Therefore different solutions will produce trade-offs between different objectives and a set of solutions is required to represent the optimal solutions of all objectives. Multi-objective formulations are realistic models for many complex engineering optimization problems. Customized genetic algorithms have been demonstrated to be particularly effective to determine excellent solutions to these problems. A reasonable solution to a multi-objective problem is to investigate a set of solutions, each of which satisfies the objectives at an acceptable level without being dominated by any other solution. In this paper, an overview is presented describing various multi objective genetic algorithms developed to handle different problems with multiple objectives.