Characteristics of many-objective test problems and penalty parameter specification in MOEA/D

2016 ◽  
Author(s):  
Hisao Ishibuchi ◽  
Ken Doi ◽  
Yusuke Nojima
Keyword(s):  
Objective Test ◽  
Test Problems ◽  
Penalty Parameter

Hindrances for robust multi-objective test problems

2015 ◽  
Vol 35 ◽  
pp. 333-348 ◽  
Author(s):  
Seyedali Mirjalili ◽  
Andrew Lewis
Keyword(s):  
Objective Test ◽  
Test Problems ◽  
Multi Objective

An Efficient Framework Using Normalized Dominance Operator for Multi-Objective Evolutionary Algorithms

2019 ◽  
Vol 10 (1) ◽  
pp. 15-37 ◽  
Author(s):  
Muneendra Ojha ◽  
Krishna Pratap Singh ◽  
Pavan Chakraborty ◽  
Shekhar Verma
Keyword(s):  
Evolutionary Algorithms ◽  
Pareto Front ◽  
Optimization Problem ◽  
Computational Cost ◽  
Optimization Methods ◽  
Objective Test ◽  
Test Problems ◽  
Multi Objective Optimization ◽  
Pareto Dominance ◽  
Multi Objective

Multi-objective optimization algorithms using evolutionary optimization methods have shown strength in solving various problems using several techniques for producing uniformly distributed set of solutions. In this article, a framework is presented to solve the multi-objective optimization problem which implements a novel normalized dominance operator (ND) with the Pareto dominance concept. The proposed method has a lesser computational cost as compared to crowding-distance-based algorithms and better convergence. A parallel external elitist archive is used which enhances spread of solutions across the Pareto front. The proposed algorithm is applied to a number of benchmark multi-objective test problems with up to 10 objectives and compared with widely accepted aggregation-based techniques. Experiments produce a consistently good performance when applied to different recombination operators. Results have further been compared with other established methods to prove effective convergence and scalability.

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