A New Multiobjective Evolutionary Algorithm Based on Decomposition of the Objective Space for Multiobjective Optimization
Cai Dai,Yuping Wang +1 more
TL;DR: Simulation results on six multiobjective benchmark functions show that the proposed algorithm is able to obtain better diversity and more evenly distributed Pareto front than the other two algorithms.
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Abstract: In order to well maintain the diversity of obtained solutions, a new multiobjective evolutionary algorithm based on decomposition of the objective space for multiobjective optimization problems (MOPs) is designed. In order to achieve the goal, the objective space of a MOP is decomposed into a set of subobjective spaces by a set of direction vectors. In the evolutionary process, each subobjective space has a solution, even if it is not a Pareto optimal solution. In such a way, the diversity of obtained solutions can be maintained, which is critical for solving some MOPs. In addition, if a solution is dominated by other solutions, the solution can generate more new solutions than those solutions, which makes the solution of each subobjective space converge to the optimal solutions as far as possible. Experimental studies have been conducted to compare this proposed algorithm with classic MOEA/D and NSGAII. Simulation results on six multiobjective benchmark functions show that the proposed algorithm is able to obtain better diversity and more evenly distributed Pareto front than the other two algorithms.
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Citations
Moea/d with adaptive weight adjustment
TL;DR: Experimental results indicate that MOEA/D-AWA outperforms the benchmark algorithms in terms of the IGD metric, particularly when the PF of the MOP is complex.
A Strength Pareto Evolutionary Algorithm Based on Reference Direction for Multiobjective and Many-Objective Optimization
Shouyong Jiang,Shengxiang Yang +1 more
TL;DR: An early developed and computationally expensive strength Pareto-based evolutionary algorithm is revived by introducing an efficient reference direction-based density estimator, a new fitness assignment scheme, and a new environmental selection strategy, for handling both multiobjective and many-objective problems.
An Improved Multiobjective Optimization Evolutionary Algorithm Based on Decomposition for Complex Pareto Fronts
Shouyong Jiang,Shengxiang Yang +1 more
TL;DR: In the proposed algorithm, a two-phase strategy (TP) is employed to divide the whole optimization procedure into two phases and a new niche scheme is introduced into the improved MOEA/D to guide the selection of mating parents to avoid producing duplicate solutions.
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Asynchronous master-slave parallelization of differential evolution for multi-objective optimization
TL;DR: AMS-DEMO is presented, an asynchronous master-slave implementation of DEMO, an evolutionary algorithm for multi-objective optimization, and Selection lag is identified as the key property of the parallelization method, which explains how its behavior depends on the type of computer architecture and the number of processors.
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A Multi-Objective Genetic Algorithm Based on Fitting and Interpolation
TL;DR: MOGA/F and MogA/I are compared with the traditional methods, non-dominated sorting genetic algorithm-II and multi-objective evolutionary algorithm based on decomposition, by optimizing the mathematical problems and show that MOG a/F has a much higher performance in terms of diversity and convergence of the final solutions.
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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.