원문정보
초록
영어
Multi-objective optimization (MOO) is the procedure of all the while streamlining two or all the more clashing objectives subject to specific requirements. Genuine building outlines regularly contain more than one clashing objective function, which requires a MOO approach. In a single objective optimization (SOO) issue, the ideal solution is clearly characterized, while a group of exchange offs that offers ascend to various groups exists in MOO issues. Every solution indiactes to a specific execution exchange off between the goals and can be viewed as ideal. In this paper introduces an overview on MOO and MOEA produces a solution of non-dominated (ND) solutions toward the end of run, which is called a Pareto set. An examination of Pareto strategies alongside their focal points and weaknesses and exploration take a shot at MOP utilizing distinctive systems.
목차
1. Introduction
2. Evolutionary Algorithm
2.1. Deterministic Meta-Heuristics
2.2. Probabilistic Meta-Heuristics
3. Single Objective Versus Multi-Objective
3.1. Single Objective Optimization (SOO)
3.2. Multi-Objective Optimization (MOO)
4. Techniques to Solve Optimization Problems
5. Multi-Objective Evolutionary Algorithms (MOEA)
5.1. Non-Elitist Multi-Objective Evolutionary Algorithms
5.2. Elitist Multi-Objective Evolutionary Algorithms
6. Applications of MOEA
7. Literature Survey
8. Problem Statment
9. Performance Measures
9.1. Hypervolume (HV)
9.2. Generational Distance (GD)
9.3. Maximum Pareto Front Error (MPFE)
9.4. Test Function
10. Conclusion
References