Proceedings Article10.1109/CIS.2016.0017
A New Evolutionary Algorithm Based on Decomposition for Multi-Objective Optimization Problems
Cai Dai,Xiujuan Lei +1 more
- 01 Dec 2016
- pp 33-38
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TL;DR: A new MOEA based on decomposition of a MOP into a number of constrained single-objective sub-problems (MOEA/D-MCS) is proposed, which is able to obtain a set of solutions with better diversity and convergence.
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Abstract: For multi-objective optimization problems (MOPs), the diversity of obtained solutions is of great importance for multi-objective evolutionary algorithms (MOEAs). To this end, in this paper, a new MOEA based on decomposition of a MOP into a number of constrained single-objective sub-problems (MOEA/D-MCS) is proposed. A MOP is firstly decomposed into a set of simple single-objective optimization sub-problems, and the objective space of the MOP is also decomposed into some number of sub-objective spaces. Then, each sub-objective space is considered as the feasible region of the corresponding single-objective problem. For each sub-problem, solutions of the feasible region are better than solutions of the infeasible region, which can maintain quite well the diversity of obtained solutions. Moreover, MOEA/D-MCS compares with MOEA/D UMOEA/D, GDRE3 d NSGAIII on six multi-objective benchmark functions. Simulation results show that the proposed algorithm is able to obtain a set of solutions with better diversity and convergence.
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Citations
A novel multi-objective evolutionary algorithm with dynamic decomposition strategy
Songbai Liu,Songbai Liu,Qiuzhen Lin,Ka-Chun Wong,Lijia Ma,Carlos A. Coello Coello,Dunwei Gong +6 more
TL;DR: A novel multi-objective evolutionary algorithm (MOEA) is proposed with dynamic decomposition strategy, called MOEA/D-DDS that combines parents and offspring populations both with the size N as a union population in environmental selection and is validated over six recently proposed MOEAs.
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A Novel Multi-objective Evolutionary Algorithm Based on a Further Decomposition Strategy
Songbai Liu,Qiuzhen Lin,Jianyong Chen +2 more
- 01 Dec 2017
TL;DR: This paper suggests a novel MOEA based on a further decomposition strategy (MOEA/FD) that presents some advantages on tackling seventeen well-known test problems and chooses a well converged solution for next evolution.
1
A novel two-archive strategy for evolutionary many-objective optimization algorithm based on reference points
TL;DR: A novel two-archive strategy for evolutionary many-objective optimization algorithm that is applied to improving the Non-dominated Sorting Genetic Algorithm (NSGA-III) and has a better performance than other state-of-art algorithms.
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Genetic algorithms in search, optimization, and machine learning
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TL;DR: This paper suggests a non-dominated sorting-based MOEA, called NSGA-II (Non-dominated Sorting Genetic Algorithm II), which alleviates all of the above three difficulties, and modify the definition of dominance in order to solve constrained multi-objective problems efficiently.
Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces
Rainer Storn,Kenneth Price +1 more
TL;DR: In this article, a new heuristic approach for minimizing possibly nonlinear and non-differentiable continuous space functions is presented, which requires few control variables, is robust, easy to use, and lends itself very well to parallel computation.
MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition
Qingfu Zhang,Hui Li +1 more
TL;DR: Experimental results have demonstrated that MOEA/D with simple decomposition methods outperforms or performs similarly to MOGLS and NSGA-II on multiobjective 0-1 knapsack problems and continuous multiobjectives optimization problems.